WO2020155075A1 - Navigation apparatus and method, and related device - Google Patents
Navigation apparatus and method, and related device Download PDFInfo
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- WO2020155075A1 WO2020155075A1 PCT/CN2019/074310 CN2019074310W WO2020155075A1 WO 2020155075 A1 WO2020155075 A1 WO 2020155075A1 CN 2019074310 W CN2019074310 W CN 2019074310W WO 2020155075 A1 WO2020155075 A1 WO 2020155075A1
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- navigation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
Definitions
- This application relates to the field of navigation technology, and in particular to a navigation device, method and related equipment.
- the user can usually obtain the navigation route between the navigation start address and the navigation target address through online map or offline map navigation, and conduct real-time navigation based on the navigation route and current location information To reach the navigation destination address.
- the server may not be able to provide a navigation map, or it can provide but cannot provide an accurate navigation map.
- users are often unable to find the target address quickly due to reasons such as large places, inconspicuous markers, and inaccurate positioning on maps. Unable to quickly return to a certain target address to find storage items or deal with related affairs, which makes the user experience poor.
- the embodiment of the present invention provides a navigation device, a method and related equipment to guide a user to find a navigation target location.
- an embodiment of the present invention provides a navigation device, which may include: a first processor, and a neural network processor coupled to the first processor, wherein the first processor is configured to Acquiring multiple images on a road segment collected by the camera module, and instructing the neural network processor to recognize the multiple images, wherein the navigation device moves along the road segment, and the road segment includes a navigation target position;
- the neural network processor is configured to recognize the multiple images to obtain multiple signs, and each sign is used to mark a position on the road section;
- the first processor is also configured to , Determining a plurality of route reference points in the road section, and generating a navigation route of the road section based on the plurality of route reference points, and the navigation route is used to guide the user to the navigation target location.
- the first processor in the navigation device acquires multiple images on the road segment moved by the navigation device collected by the camera module, and instructs the neural network processor to recognize the multiple images on the road segment, Finally, the first processor determines the route reference points in the road section according to the signs recognized by the neural network processor, and generates a navigation route based on the route reference points, so as to help the user to easily find the navigation target location according to the navigation route when returning.
- the embodiment of the present invention realizes the navigation function of finding the target address only by using the navigation device, without a lot of manpower and material resources, no network, and no need to lay out a lot of hardware equipment, and the cost is low; and the whole process does not require user's cumbersome operation and does not rely on
- the user's active memory, map data of the site environment, network signals, etc. are convenient and feasible, which greatly improves the user's navigation experience and effects.
- the device further includes the camera module, configured to collect the multiple images on the road section.
- the device further includes a memory coupled to the first processor and the camera module; the memory is configured to store the multiple images collected by the camera module.
- multiple images on the road section moved by the navigation device are collected through the camera module in the navigation device, and stored in the memory.
- the first processor instructs the neural network processor to perform the processing on the multiple images on the road section.
- the first processor determines the route reference point in the road segment according to the signs recognized by the neural network processor, and generates a navigation route based on the route reference point to help the user to easily find the navigation route according to the navigation route when returning Navigate to the target location.
- the embodiment of the present invention realizes the navigation function of finding the target address only by using the navigation device, without a lot of manpower and material resources, no network, and no need to lay out a lot of hardware equipment, and the cost is low; and the whole process does not require user's cumbersome operation and does not rely on
- the user's active memory, map data of the site environment, network signals, etc. are convenient and feasible, which greatly improves the user's navigation experience and effects.
- the device further includes a measurement module coupled to the first processor: the measurement module is configured to measure the navigation device at any two of the multiple route reference points The movement direction and the number of steps between adjacent route reference points; the first processor is further configured to generate navigation assistance information of the navigation route according to the direction and the number of steps.
- the measurement module in the navigation device measures the direction and the number of steps between any two adjacent route reference lines on the moving section of the navigation device, so that the first processor can use the direction and the number of steps Determine the navigation assistance information of the navigation route to reduce the cumulative error of the route and improve the accuracy.
- the navigation route pushed by the navigation device to the user includes multiple route reference points, as well as the direction and number of steps between the route reference points calculated based on the pedestrian dead reckoning PDR technology.
- the multiple signs are M signs
- the multiple route reference points are N signs in the M signs
- N is less than or equal to M
- N and M are greater than An integer of 1
- the navigation route includes the N signs.
- the navigation route further includes a time stamp corresponding to at least one route reference point.
- the neural network processor recognizes M signs from multiple images on the road section, where M is an integer greater than 0; the first processor is specifically configured to: determine from the M signs N signs, the N signs are determined as N route reference points on the road section; N is an integer greater than 0 and less than or equal to M; generated according to the N route reference points and the corresponding time stamp Navigation route.
- the first processor in the navigation device selects N from the M signs recognized by the neural network processor as the route reference points on the road section, and according to the timestamps of the N route reference points Generate navigation routes. That is to say, the embodiment of the present invention can eliminate some useless or insignificant signs, and use signs with better and more significant indications as the route reference points on the final navigation route, thereby ensuring more accurate navigation and better results. .
- the multiple signs are M signs
- the multiple route reference points are L reference areas
- L is less than or equal to M
- N and M are integers greater than 1
- the The navigation route includes the L reference areas
- each reference area includes at least one sign.
- the navigation route further includes a time stamp corresponding to at least one route reference point.
- the neural network processor recognizes M signs from multiple images on the road section, where M is an integer greater than 0; the first processor is specifically configured to: determine according to the M signs L reference areas; where L is an integer greater than 0 and less than M, any one of the reference areas includes at least one of the signs; a route reference point is determined in each of the L reference areas , Obtain L route reference points; generate a navigation route according to the L route reference points and the corresponding time stamp.
- the first processor in the navigation device determines L reference areas from the M signs recognized by the neural network processor, and finally determines a route reference point in each reference area. That is, the user's line of sight is taken as a reference area. In each reference area, a sign is selected as the route reference point, and some of the signs with repeated indications are removed, and redundant information is removed, which not only ensures that the user has the sight range Route reference points can also ensure better navigation effects.
- the device further includes a second processor coupled to the first processor; the first processor is further configured to send the navigation route to the second processor The second processor is also used to push the navigation route to the user.
- the first processor can be used as a coprocessor with lower power consumption
- the second processor can be used as a main processor with normal power consumption.
- the coprocessor After the coprocessor generates the navigation route required by the user, and When it is judged that the user reaches the return navigation starting address, the main processor is awakened and the judgment result is sent to the main processor; the main processor can push the navigation route to the user through multimedia methods such as text, image, and voice to ensure The user can easily find the navigation target address, and can ensure the low power consumption of the navigation device.
- the road section further includes a navigation start position, and the navigation start position is located at the end position of the road section.
- the neural network processor can be instructed by the first processor in the navigation device to recognize the end position of the road section according to the image collected by the camera module. For example, if the navigation target position is the starting position on the road section, it is the parking lot. For parking spaces, the navigation start position is the end position used to collect and recognize images on the road section, and is the position where the user exits the parking lot.
- the neural network processor can identify the user’s parking point and the exit point of the parking lot, and according to According to the recognition result, it is judged whether the user returns to the parking lot to find a parking space, so that the first processor can wake up the second processor at the right time to push the navigation route to the user, help the user find his car easily, and greatly improve the user search The experience and effects of car navigation.
- the second processor is further configured to: receive instruction information sent by an in-vehicle system, where the instruction information is used to indicate that the in-vehicle system has reached the navigation target location;
- the target position is located at the starting position for collecting and recognizing images on the road section; the first processor is awakened according to the instruction information to acquire the multiple images on the road section collected by the camera module.
- the navigation target location is a parking space of the vehicle in a parking lot.
- the embodiment of the present invention can be applied to the scene of finding a car in a parking lot.
- the on-board system can use the vehicle's own camera system to capture the parking location before the vehicle enters the parking lot to find a parking space.
- the parking space image, and the parking space is identified, and then sent to the navigation device in this application through communication methods such as Bluetooth or Wi-Fi, and then the second processor in the navigation device wakes up the first processor to control the camera module to start collecting road sections Multiple images on the.
- the navigation device is a smart phone, smart bracelet or smart glasses that the user carries, before the user drives the vehicle into the parking lot and arrives at the parking space, it is inconvenient to use the navigation device to image the parking lot environment and parking space. Acquisition and recognition. At this time, the image can be collected through the front or rear camera in the vehicle driven by the user, and recognized through the on-board system; and between the vehicle reaching the parking space of the parking lot and the user exiting the parking lot Because the user will carry a navigation device, it is more convenient to identify the signs on the road section. This ensures that the navigation device can start collecting images at the right time, thereby ensuring the completeness and accuracy of the sign recognition on the road section, helping users find their car easily, and greatly improving the user experience and effect of car-finding navigation .
- an embodiment of the present invention provides a navigation method applied to a navigation device, which may include: the method includes: acquiring a plurality of images on a road section, wherein the navigation device moves along the road section, and The road segment includes a navigation target location; the multiple images are identified to obtain multiple signs, each of which is used to mark a position on the road segment; and multiple route reference points in the road segment are determined according to the multiple signs , And generate a navigation route of the road segment based on the multiple route reference points, and the navigation route is used to guide the user to the navigation target location.
- the method further includes: measuring the direction and number of steps the navigation device moves between any two adjacent route reference points among the multiple route reference points; And the number of steps to generate navigation assistance information of the navigation route.
- the multiple signs are M signs
- the multiple route reference points are N signs in the M signs
- N is less than or equal to M
- N and M are greater than An integer of 1
- the navigation route includes the N signs.
- the multiple signs are M signs
- the multiple route reference points are L reference areas
- L is less than or equal to M
- N and M are integers greater than 1
- the The navigation route includes the L reference areas
- each reference area includes at least one sign.
- the navigation route further includes a time stamp corresponding to at least one route reference point.
- the method further includes: pushing the navigation route to the user.
- the method further includes: receiving instruction information sent by an in-vehicle system, where the instruction information is used to indicate that the in-vehicle system has reached the navigation target position; the navigation target position is located in the The starting position of the road section; triggering the execution of the acquiring of multiple images on the road section according to the indication information.
- the road section further includes a navigation start position, and the navigation start position is located at the end position of the road section.
- the present application provides a navigation device, which has the function of implementing any of the above-mentioned navigation methods.
- This function can be realized by hardware, or by hardware executing corresponding software.
- the hardware or software includes one or more modules corresponding to the above-mentioned functions.
- the present application provides a terminal, the terminal includes a processor, and the processor is configured to support the terminal to perform a corresponding function in a navigation method provided in the second aspect.
- the terminal may also include a memory, which is used for coupling with the processor and stores necessary program instructions and data for the terminal.
- the terminal may also include a communication interface for the terminal to communicate with other devices or communication networks.
- the present application provides a computer storage medium that stores a computer program that, when executed by a processor, implements the navigation method process described in any one of the above-mentioned second aspects.
- an embodiment of the present invention provides a computer program, the computer program includes instructions, when the computer program is executed by a computer, the computer can execute the navigation method flow described in any one of the second aspect.
- the present application provides a chip system, which includes a processor, configured to implement the functions involved in the process of the navigation method described in any one of the second aspects.
- the chip system further includes a memory for storing program instructions and data necessary for the navigation method.
- the chip system can be composed of chips, or include chips and other discrete devices.
- Figure 1 is a schematic structural diagram of a navigation device provided by an embodiment of the present invention.
- FIG. 2 is a schematic diagram of identifying multiple images on a road section according to an embodiment of the present invention
- Fig. 3 is a schematic diagram of parking lot navigation provided by an embodiment of the present invention.
- Figure 4 is a schematic diagram of another parking lot navigation provided by an embodiment of the present invention.
- Figure 5 is a schematic structural diagram of another navigation device provided by an embodiment of the present invention.
- FIG. 6A is a schematic structural diagram of another navigation device provided by an embodiment of the present invention.
- FIG. 6B is a schematic structural diagram of yet another navigation device provided by an embodiment of the present invention.
- FIG. 7 is a distribution diagram of parking spaces in a parking lot provided by an embodiment of the present invention.
- FIG. 8 is a schematic diagram of identifying multiple images on another road section according to an embodiment of the present invention.
- FIG. 9 is a schematic diagram of identifying directions and steps between reference points of adjacent routes according to an embodiment of the present invention.
- FIG. 10 is another schematic diagram of multiple image recognition on a road section according to an embodiment of the present invention:
- FIG. 11 is a schematic diagram of reference area division according to an embodiment of the present invention.
- FIG. 12 is a schematic diagram of path push provided by an embodiment of the present invention.
- FIG. 13 is a hardware structure diagram of a neural network processor provided by an embodiment of the present invention.
- FIG. 14 is a schematic flowchart of a navigation method provided by an embodiment of the present invention.
- FIG. 15 is a schematic structural diagram of another navigation device provided by an embodiment of the present invention.
- a component may be, but is not limited to, a process, a processor, an object, an executable file, an execution thread, a program, and/or a computer running on a processor.
- a component may be, but is not limited to, a process, a processor, an object, an executable file, an execution thread, a program, and/or a computer running on a processor.
- the application running on the computing device and the computing device can be components.
- One or more components may reside in processes and/or threads of execution, and components may be located on one computer and/or distributed among two or more computers.
- these components can be executed from various computer readable media having various data structures stored thereon.
- a component can be based on a signal having one or more data packets (for example, data from two components interacting with another component between a local system, a distributed system, and/or a network, such as the Internet that interacts with other systems through signals) Communicate through local and/or remote processes.
- data packets for example, data from two components interacting with another component between a local system, a distributed system, and/or a network, such as the Internet that interacts with other systems through signals
- the parking lot is a place for vehicles to park. There are simple parking lots that only draw parking grids without management and charge, and there are also paid parking lots equipped with entry and exit barriers, parking guards and hourly cashiers. Modern parking lots often have automated time-toll collection systems, closed-circuit television and video recorder systems.
- the flat parking lot also known as the square type
- the flat parking lot has a certain area of land, divided into passages and parking spaces by traffic markings, and equipped with traffic facilities such as pointing arrows and signs. Its parking methods include vertical (at right angles to the passage), parallel (parallel to the passage), diagonal and staggered arrangements.
- the parking lot in this application may include an underground parking lot, a road parking lot, a three-dimensional parking lot, etc. The application does not limit the specific form of the parking lot.
- AI Artificial Intelligence
- digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results. operating system.
- artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that can react in a similar way to human intelligence.
- Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that the machines have the functions of perception, reasoning and decision-making.
- Research in the field of artificial intelligence includes robotics, natural language processing, computer vision, decision-making and reasoning, human-computer interaction, recommendation and search, and basic AI theories.
- Convolutional Neural Network is a multi-layer neural network. Each layer is composed of multiple two-dimensional planes, and each plane is composed of multiple independent neurons. Each neuron shares weights, and the number of parameters in the neural network can be reduced through weight sharing.
- the convolution operation performed by the processor usually converts the convolution of the input signal characteristics and the weight into a matrix multiplication operation between the signal matrix and the weight matrix.
- the signal matrix and the weight matrix are divided into blocks to obtain multiple fractal signal matrices and fractal weight matrices, and then the multiple fractal signal matrices and fractal weight matrices are subjected to matrix multiplication and accumulation operations.
- IMU Inertial measurement unit
- IMU is a device that measures the three-axis attitude angle (or angular rate) and acceleration of an object. It generally includes three single-axis accelerometers and three single-axis gyroscopes. Among them, The accelerometer is used to detect the acceleration signal of the object in the independent three-axis of the carrier coordinate system, and the gyroscope is used to detect the angular velocity signal of the carrier relative to the navigation coordinate system, and measure the angular velocity and acceleration of the object in the three-dimensional space, and then calculate the object Gesture. IMUs are mostly used in equipment that requires motion control, such as automobiles and robots, and are generally installed on the center of gravity of the object to be measured.
- Pedestrian Dead Reckoning mainly uses Inertial Measurement Unit (IMU) in a beacon-free environment to sense the acceleration, angular velocity, magnetic force, and pressure of a person in the process of traveling. And use these data to calculate the step length and direction of the marching personnel, so as to achieve the purpose of positioning and tracking the personnel.
- IMU Inertial Measurement Unit
- the main processes involved are gait detection, step length and direction calculation.
- Optical character recognition refers to the process of analyzing and recognizing image files of text data to obtain text and layout information. Generally divided into two steps: text positioning, that is, finding the position of the text in the image; text recognition, that is, identifying the found text. Text positioning may also include some binarization and correction steps.
- Fast object detection can be used to solve complex computer vision problems and achieve good results.
- the idea is that using conv+pooling in the neural network does not change the relative position of the features in the image.
- RCNN Regions with CNN
- target detection can apply the strategy of region proposal to the convolutional neural network, and training from bottom to top can be used to locate the target and image division.
- R-CNN uses the Selective Search algorithm to extract (propose) possible regions of interest (regions of interest, RoIs), and then uses standard CNN to classify each extracted region.
- Navigation is an operation used to determine the location of the target object and guide the user to the location.
- the navigation technology of this embodiment can be used to guide the user to the location of his vehicle, that is, to guide the user to find a car.
- the vehicles or vehicles involved in this implementation generally refer to vehicles, including but not limited to motor vehicles or non-motor vehicles.
- Motor vehicles may include automobiles, electric vehicles, motorcycles, smart cars, battery cars or tractors, etc.; non-motor vehicles include but are not limited to bicycles, bicycles, or scooters.
- Scenario 1 Global Positioning System (GPS) navigation and positioning to find a car: GPS technology is widely used for navigation and positioning, but the signal strength of GPS signals in the underground or indoors is weak, and the positioning effect is not ideal. Therefore, indoor positioning usually uses Wi-Fi positioning technology, Bluetooth positioning technology, and geomagnetic positioning technology. Among them, Wi-Fi positioning is the most widely used in indoor positioning. Wi-Fi positioning technology can usually be used to realize the function of finding a car in a parking lot: after the user stops parking, mark the location of the parking, when the user picks up the car and leaves, the current location is determined through Wi-Fi positioning, and then according to the internal map of the parking lot, Determine the navigation route for the pickup.
- GPS Global Positioning System
- Wi-Fi positioning technology requires the deployment of a large number of routers in the environment. If the Wi-Fi system deployed in the parking lot is only used for car search, it will bring about low efficiency and high cost. Similarly, when geomagnetic positioning and Bluetooth positioning are used to find a car in a parking lot, it will also cause high costs.
- Scenario 2 IoV navigation and positioning to find a car: When the user parks, use the car's on-board system to obtain the sign image corresponding to the current parking space; identify the location information of the parking space according to the above-mentioned sign image, and send it to the mobile phone through the Internet of Vehicles; the user returns When looking for a car, the mobile phone scans the QR code of the pedestrian exit to obtain the current location (each QR code information corresponds to a preset location on the map of the parking lot); according to the location of the parking space and the current location of the user, and parking The map inside the field, planning the navigation path.
- Scenario 3 Finding a car through a parking lot camera: Each parking space is equipped with one or more cameras. When the user parks, the camera near the parking space records the license plate and sends the corresponding information of the license plate and the parking space to the server; when the user picks up the car, use it The mobile phone queries the license plate information to get the corresponding parking location; finally use the parking lot map to search for the parking location, and get the car navigation route.
- the car-finding problem that the embodiment of the present invention mainly solves specifically includes the following: in view of the high cost of the prior art, the cumbersome operation, and the poor user experience, a low cost is provided. , Navigation device with easy operation and good user experience. That is, this application needs to provide a "low-cost, zero-operation, and micro-sensing" fast car-finding solution that does not rely on environmental deployment and does not require user operations.
- the technical problems solved by this application include, but are not limited to, the navigation problem in the above-mentioned parking lot car-finding scene.
- the above-mentioned application scene is only an exemplary application scene in this application.
- the navigation device, method and related The application scenarios of the equipment can also include, for example, in large-scale shopping malls, office buildings, stations, parking lots, amusement parks, schools and other places, the navigation in the process of turning back and looking for a certain landmark, commodity, storage place, etc., The following scenes and example descriptions will not be listed and repeated.
- FIG. 1 is a schematic structural diagram of a navigation device provided by an embodiment of the present invention.
- the navigation device 10 may include: a first processor 101 and a neural network processor coupled to the first processor 104;
- the navigation device 10 may also include a camera module 102 and a memory 103; wherein the camera module 102 is used to collect multiple images on the road section and store them in the memory, and the navigation device along the The road segment is moving, and the road segment includes the navigation target position.
- the road segment includes the road segment between the parking space where the vehicle reaches the parking lot and the user exiting the parking lot
- the navigation target position is the parking space where the vehicle reaches the parking lot.
- the camera module 101 may be a rear camera, a front camera, a side camera, a rotating camera or a reversible camera of the terminal device.
- the main function of the camera module 101 is to track and capture the environment image of the navigation device on the moving road section in real time during the navigation process, for example, the environment image from the parking space to the elevator.
- the camera module 102 is a camera module with low power consumption and low-resolution frames, or the camera module 102 operates in a low-power, low-resolution frame mode when collecting multiple images on a road section. That is, as long as the multiple images on the road section collected by the camera module 102 satisfy the requirement that the neural network processor 104 can recognize the signs, the power consumption can be reduced.
- the memory 103 is used to store multiple images on the road section collected by the camera module 102.
- the memory 103 can be used as a shared memory in the navigation device 10. That is, the first processor 101, the camera module 102, and the neural network processor 104 can all be connected to the memory 103, and the memory 103 can be used for the first processor 101 and the camera module. 102 and the neural network processor 104 provide memory space required for related image collection, recognition, and generation of navigation routes.
- the first processor 101 is used to instruct the neural network processor 104 to recognize multiple images on the road section.
- the first processor 101 can be used as a co-processor of the navigation device 10.
- the navigation device 10 When the navigation device 10 is in a standby or dormant state, it still supports the normal operation of the camera module 102 and the neural network processor 104, so as to ensure that there is more information on the road. Collection and recognition of images.
- the first processor 101 in this application may be a smart sensor hub (Sensor hub), and the Sensor hub is based on a low-power microcontroller unit (MCU) and a lightweight real-time multitasking operating system (Real Time Operating System (RTOS) is a solution that combines software and hardware, which can connect and process data from various sensor devices. Due to its low power consumption and lightweight features, it can ensure that the navigation device 10 The power consumption is for the user to search and navigate the car.
- MCU low-power microcontroller unit
- RTOS Real Time Operating System
- a neural network processor (Neutral Processing Unit, NPU) 104 is used to identify the multiple images to obtain multiple signs, and each sign is used to mark a position on the road section.
- the signs are various types in a parking lot.
- Logo images such as numbers, letters, straight arrows, diagonal arrows, turning arrows, designated patterns, exits (EXIT), boxes, entrances (ENTRANCE), signs, etc.
- Figure 2 is a schematic diagram of the recognition of multiple images on a road section provided by an embodiment of the present invention.
- the marks are H050, H084, K108, K120, L005, L100, M008, M070, elevator 3, etc.
- the neural network processor 104 needs to collect a large number of parking lot signs that need to be learned in advance, and then perform image preprocessing on the parking lot signs that need to be learned, and then use the deep learning engine to train the processed images to obtain Deep learning model, and train the classifier, and finally use the trained model and classifier to recognize multiple images on the received road section.
- the first processor 102 is further configured to determine a route reference point in the road section according to the sign recognized by the neural network processor 104, and generate a navigation route based on the route reference point. That is, the first processor 102 determines the route reference point of the user between the parking space and the exit parking place according to the sign recognized by the neural network processor 104, and the route reference point can be understood as an indication node on the navigation route.
- the recognized signs include: "A027, A029, A030, A032, B005, B006, B007, B008, arrows, ENTRANCE entrance”
- you can follow the preset rules e.g., exclude signs that are closer, Eliminate signs with unclear indications, etc.
- the navigation route is: "ENTRANCE ⁇ Arrow ⁇ Exit ⁇ B008 ⁇ B005 ⁇ A032 ⁇ A030 ⁇ A027”.
- the embodiment of the present invention does not specifically limit how to determine the route reference point from the sign, nor does it specifically limit how to generate the navigation route according to the route reference point.
- the aforementioned neural network processor 104 can also be integrated in the first processor 101 as a part of the first processor 101; it can also be coupled to the aforementioned first processor 101 and can implement multiple Other functional chips or modules in the chip recognized by the logo in the image; similarly, the functions executed by the first processor 101 can also be located on one chip or distributed on multiple different functional chips, which are not specifically described in the embodiment of the present invention. limited.
- the first processor instructs the neural network to process
- the device recognizes multiple images on the road section.
- the first processor determines the route reference point in the road section according to the signs recognized by the neural network processor, and generates a navigation route based on the route reference point to help the user in When returning to find a car, you can easily find the navigation target location according to the navigation route.
- the embodiment of the present invention realizes the navigation function of finding a car only by using the navigation device, without a lot of manpower and material resources, and no need to lay out a lot of hardware equipment, and the cost is low; and the whole process does not require the user's cumbersome operation and does not rely on the user's active memory
- the map data and network signal of the parking lot are convenient and feasible, which greatly improves the user experience and effect of car navigation.
- the multiple signs are M signs
- the multiple route reference points are N signs in the M signs
- N is less than or equal to M
- N and M are greater than An integer of 1
- the navigation route includes the N signs.
- the neural network processor 104 recognizes M signs from multiple images on the road section; the first processor 101 determines N signs from the M signs, and determines the N signs as the N route reference points on the road segment, and a navigation route is generated according to the N route reference points and the corresponding time stamp. As shown in FIG. 3, FIG.
- FIG. 3 is a schematic diagram of parking lot navigation provided by an embodiment of the present invention, in which the neural network processor 104 recognizes M signs from multiple images on multiple road sections, for example, A014, B004, A015, B005, A016, B006, A017, B007, A018, B008, A019, B009, A020, B010, F008, E001, E003, E004, D001, D002, D003, D004, D005, D006; then M is equal to 24 .
- the first processor 101 determines N signs from the above M signs, for example, B004, B005, B006, B007, B008, B009, B010, E001, E003, D001, D002, D003, D004, D005, D006, then N is equal to 15, and its determination principle is to determine one of the signs with the same indicating function.
- N is equal to 15
- the navigation route is generated as: "D006 ⁇ D005 ⁇ D004 ⁇ D003 ⁇ D002 ⁇ D001 ⁇ E003 ⁇ E001 ⁇ B010 ⁇ B009 ⁇ B008 ⁇ B007 ⁇ B006 ⁇ B005 ⁇ B004".
- the multiple signs are M signs
- the multiple route reference points are L reference areas
- L is less than or equal to M
- N and M are integers greater than 1
- the The navigation route includes the L reference areas
- each reference area includes at least one sign.
- the neural network processor 104 recognizes M signs from a plurality of images on the road section; the first processor 103 determines L reference regions according to the M signs, and among the L reference regions A route reference point is determined in each reference area; a navigation route is generated according to the determined L route reference points and the corresponding time stamp. As shown in FIG. 4, FIG.
- the first processor 101 determines L reference areas according to the above M signs, which are reference area 1, reference area 2, reference area 3, reference area 4, reference area 5, reference area 6, reference area 7, and reference area 8.
- the route reference point in reference area 1 is B005
- the route reference point in reference area 2 is B007
- the route reference point in reference area 3 is B009
- the route reference point in reference area 4 is B010
- the route reference point in reference area 5 is E011
- the route reference point in reference area 6 is D001
- the route reference point in the reference area 8 is D005, and the determination principle is to determine a route reference point from a reference area within the visible range.
- the first processor 101 generates the navigation route according to the determined L route reference points and the corresponding timestamps, combined with the reverse pick-up principle: "D005 ⁇ D003 ⁇ D002 ⁇ D001 ⁇ E011 ⁇ B010 ⁇ B009 ⁇ B007 ⁇ B005".
- the road section further includes a navigation start position
- the navigation start position is located at the end position of the road section.
- the navigation starting position is the elevator where the user exits the parking lot
- the recognized signs are "EXIT", "Elevator B", etc.
- the first processor in the navigation device may be used to instruct the neural network processor to recognize the end position of the road section according to the image collected by the camera module. For example, if the navigation target position is the start of the collection and the start of the other image on the road section
- the location is the parking space of the parking lot, and the navigation start location is the end location of the end collection and different images on the road segment. It is the location where the user exits the parking lot.
- the neural network processor can identify the user’s parking spot And the point of exiting the parking lot, and based on the recognition results, determine whether the user returns to the parking lot to find a parking space, so that the first processor can wake up the second processor at the right time to push the navigation route to the user, helping the user find their own
- the car greatly improves the user experience and effect of car-finding navigation.
- the navigation device 10 may further include a measurement module 105 coupled with the first processor 101.
- the measurement module 105 is used to measure the direction and the number of steps between any two adjacent route reference points of the navigation device in the road section, and store the direction and the number of steps in the memory 103.
- the measurement module can be an inertial measurement unit (IMU), which is a combination of sensors (accelerometer, gyroscope, and magnetometer) to measure and report at least one of speed, direction, and gravity Electronic equipment. It can achieve 3 degrees of freedom (DoF, degrees of freedom) or 6DoF data measurement.
- IMU inertial measurement unit
- IMU can be used to calculate the user's direction and number of steps on the first road based on pedestrian dead reckoning PDR technology. It can be understood that the aforementioned IMU may be used as a part of the first processor 101, or may be coupled to the functional chip or an on-chip module of the first processor 101, which is not specifically limited in the embodiment of the present invention.
- the memory 103 is also used to store the direction and the number of steps measured by the measurement module 105.
- the memory 103 can be used as a shared memory in the navigation device 10, for example, for storing images collected by the camera module 102 and storing the directions and steps measured by the measurement module.
- the first processor 101 is further configured to generate navigation assistance information of the navigation route according to the direction and the number of steps. For example, while providing navigation routes for car owners, they also provide navigation assistance information for them to indicate the direction and number of steps between any two adjacent route reference points, enhancing the indication effect of navigation routes, and helping car owners find navigation easily target location.
- the measurement module in the navigation device measures the direction and the number of steps of the navigation device between any two adjacent route reference lines on the road section between the parking space of the parking lot and the user exiting the parking lot.
- the first processor determines the navigation assistance information of the navigation route according to the direction and the number of steps, so as to reduce the accumulated error of the route and improve the accuracy.
- the navigation route pushed by the navigation device to the user includes multiple route reference points, as well as the direction and number of steps between the route reference points calculated based on the pedestrian dead reckoning PDR technology.
- FIG. 6A is a schematic structural diagram of another navigation device provided by an embodiment of the present invention.
- the navigation device 10 may also include a second processor 106 coupled to the first processor 101, and the power consumption of the first processor 101 is lower than that of the second processor 106. Power consumption; optionally, as shown in FIG. 6B, FIG. 6B is a schematic structural diagram of another navigation device provided by an embodiment of the present invention.
- the navigation device 10 may also include For the measurement module 105, refer to FIG. 5 for details. It is assumed that the location where the user exits the parking lot is the starting position of the navigation.
- the first processor 101 is further configured to send the navigation route to the second processor.
- the first processor 101 since the first processor has lower power consumption than the second processor, the first processor 101 can be used as a co-processor to perform operations related to navigation route generation with lower power consumption.
- the second processor 106 can be used as the main processor to perform other related operations that require higher computing capabilities, and therefore can process other processes than generating the navigation route.
- the second processor 106 is configured to push the navigation route to the user. For example, when the first processor 101 determines according to the recognition result that the user is currently returning to the navigation starting position (that is, the end position of the road section) where the user exited the parking lot, then it is determined that the user currently needs to return to pick up the car, so the recognition result is It is sent to the second processor 106, and the second processor 106 pushes the navigation route to the user. There is no need for the user to independently obtain the navigation route, which is more convenient and faster, and improves the user experience.
- the road section further includes a navigation start position.
- the second processor 106 is further configured to receive instruction information sent by the on-board system, where the instruction information is used to indicate that the on-board system has reached the navigation target position; according to the instruction information Wake up the first processor to control the camera module to collect multiple images on the road section. That is, the interaction between the vehicle-mounted system and the navigation device 10 triggers the navigation device 10 to enter the navigation mode, that is, starts to collect and identify multiple images on the road section.
- the vehicle-mounted system may be a vehicle-mounted system on a vehicle, which can control a camera device inside or outside the vehicle to collect and identify surrounding environment images to identify whether the vehicle currently reaches a parking space in a parking lot.
- the second processor 106 is also used to run general operating system software and control the operation of the first processor 101 under the action of the general operating system software. Further, the second processor 106 is also used for processing and completing other related calculation processing and control in the navigation process. It is understandable that the navigation device in Figure 1, Figure 6A or Figure 6B can be located in a terminal (such as a smart phone, tablet, smart wearable device, etc.), a smart camera device (smart camera, smart camera, smart tracking device) , Intelligent monitoring equipment, aerial drones, etc., this application will not list them all.
- a terminal such as a smart phone, tablet, smart wearable device, etc.
- a smart camera device smart camera, smart camera, smart tracking device
- Intelligent monitoring equipment aerial drones, etc.
- the on-board system can use the vehicle's own camera system to capture the parking space image of the parking place and identify the parking space before the vehicle enters the parking lot to find the parking space. , And then send it to the navigation device in this application through communication methods such as Bluetooth or Wi-Fi, and then wake up the first processor through the second processor in the navigation device to control the camera module to start collecting multiple images on the road section.
- the navigation device is a smart phone, smart bracelet or smart glasses that the user carries, before the user drives the vehicle into the parking lot and arrives at the parking space, it is inconvenient to use the navigation device to image the parking lot environment and parking space. Acquisition and recognition.
- the image can be collected through the front or rear camera in the vehicle driven by the user, and recognized through the on-board system; and between the vehicle reaching the parking space of the parking lot and the user exiting the parking lot Because the user will carry a navigation device, it is more convenient to identify the signs on the road section. This ensures that the navigation device can start collecting images at the right time, thereby ensuring the completeness and accuracy of the sign recognition on the road section, helping users find their car easily, and greatly improving the user experience and effect of car-finding navigation .
- the following takes the navigation device as a smart phone or a part of a smart phone as an example, combined with the structure of the navigation device provided in Figure 6A, based on the application scenario of the user parking in the parking lot and car-finding navigation, how the smart phone is implemented in the application Route navigation.
- the first processor 101 is the Sensor hub
- the camera module 102 is the rear camera of the mobile phone
- the memory 103 is shared memory
- the second processor is the main processor, such as a central processing unit (CPU), as an example.
- CPU central processing unit
- Step 1 Scene recognition (camera collects the image and puts it in the shared memory, the Sensor hub calls the NPU to recognize the image, and the NPU returns the identification information to the Sensor hub).
- the camera captures the image of the underground garage environment (such as a large number of parked cars), it can be considered that the user enters the underground garage parking route recording mode; when the image captured by the camera is no longer an underground garage Image of the environment, or when it recognizes that the user enters the elevator/escalator, it can be determined that the user exits the underground garage parking route recording mode; when the camera acquires the image of the underground garage environment again, it determines that the user enters the return to the car-finding mode.
- Step 2 Image collection and recognition (the camera collects the image and stores it in the shared memory, calls the NPU to recognize the image, and the NPU returns the recognition information to the Sensorhub).
- the camera starts to collect images of the surrounding environment (ie multiple images on the road section).
- the camera stops collecting images of the surrounding environment.
- Figure 2 is on the road section collected by the camera.
- FIG. 7 is a distribution diagram of parking spaces in a parking lot provided by an embodiment of the present invention.
- the location information usually marked on the ground, pillars and other objects of the parking lot is the sign in this application, such as area A, Area B, parking space A01, etc.
- the NPU can recognize the signs in the image through recognition algorithms such as Single Shot MultiBox Detector (SSD)/fast target detection (fast RCNN) and optical character recognition (OCR), including those entering the parking lot. Signs (navigation target location), parking lot signs (route reference points) in the road section, and elevator number (navigation starting position) the user takes.
- SSD Single Shot MultiBox Detector
- fast RCNN fast target detection
- OCR optical character recognition
- Step 3 Record the departure route (Sensor hub records the information returned by the NPU, as shown in Table 2 below).
- Timestamp information Timestamp 1 H050 Timestamp 2 H084 ... ... Timestamp n M044
- Step 4 Push the pick-up path (implemented by the second processor).
- the first processor wakes up the second processor and pushes User’s parking spot: Push the number corresponding to the parking position (generally considered that the first number identified is the parking space); push the elevator the user takes when entering the mall (label 3); it is recommended that the user take the third elevator to pick up the car, and Push the pick-up path (the reverse of HKLM); the way to notify users can be: text notification, voice notification, multimedia notification, etc.
- the second processor may run software, including but not limited to operating system and application software, to generate a user interface, and use text or image to make the notification on the user interface, which is not limited in the embodiment of the present invention.
- the embodiment of the present invention uses low-power devices to collect information, such as a low-power camera, to obtain path information from the user after parking the car to leaving the parking lot.
- information such as a low-power camera
- the mobile phone automatically pushes the path information.
- the following takes the navigation device as a smart phone or a part of a smart phone as an example, combined with the structure of the navigation device provided in Figure 6B, based on the application scenarios of the user parking in the parking lot and car-finding navigation, how the smart phone is implemented in the application Route navigation.
- the first processor 101 is the Sensor hub
- the camera module 102 is the rear camera of the mobile phone
- the memory 103 is the shared memory
- the measurement module is the MCU
- the second processor is the main processor.
- the functions performed by the functional modules in the mobile phone in time sequence may include the following steps.
- Step 1 Scene recognition, which is the same as the first embodiment.
- Step 2 Image acquisition and recognition are the same as the first embodiment above.
- Step 3 Record the departure route (Sensor hub gets the information returned by the NPU, as shown in Table 3 below).
- Timestamp position Steps and angle Timestamp 1 A100 0 Timestamp 2 A012 30 degrees, 100 steps ... ... ... Timestamp n D032 20 degrees, 200 steps
- FIG. 8 is a schematic diagram of identifying multiple images on another road section provided by an embodiment of the present invention
- FIG. 9 is a schematic diagram of an adjacent route reference point provided by an embodiment of the present invention.
- Schematic diagram of direction and step identification After identifying the starting point A100 (route reference point), start counting steps, use PDR (pedestrian dead reckoning) for trajectory and direction, and record the number of steps from A100 to A102 when A012 (route reference point) is identified Direction; similarly, when a route reference point is identified, the number of steps and directions from the previous route reference point to the next route reference point are recorded; optionally, the count is cleared every time a route reference point is identified, you can Reduce cumulative error.
- PDR pedestrian dead reckoning
- Step 4 Push the pick-up path.
- the process of picking up the car is the same as in the first embodiment.
- the way of informing the user can also display a graphical trajectory, which is not specifically limited in the embodiment of the present invention.
- the navigation device takes the navigation device as a smart phone or a part of a smart phone as an example, combined with the structure of the navigation device provided in Figure 6B, based on the application scenarios of the user parking in the parking lot and car-finding navigation, how the smart phone implements this application In the route navigation.
- the first processor 101 is the Sensor hub
- the camera module 102 is the rear camera of the mobile phone
- the memory 103 is the shared memory
- the measurement module is the MCU
- the second processor is the main processor.
- the functions performed by the functional modules in the mobile phone in time sequence may include the following steps.
- the first step scene recognition, the same as the first embodiment.
- the second step image acquisition and recognition, the same as the first embodiment.
- the third step record the signs in each image (multiple images on the road section), as shown in FIG. 10, which is another schematic diagram of multiple image recognition on the road section provided by the embodiment of the present invention: Set the route reference point in the range, as shown in FIG. 11, which is a schematic diagram of the reference area division provided by the embodiment of the present invention. For example, every 15 parking spaces is a reference area. If there are 120 parking spaces in the H area, the H area is shared 8 reference areas. According to the parking position identified in the second step, such as H050, record the reference area H4 where H050 is located.
- the fourth step record the route to leave the underground garage, the same as the first or second embodiment.
- Step 5 Push the pick-up path. Start to pick up the car, identify the user's other modes ⁇ basement mode, that is, if the first processor recognizes that the elevator to pick up the car is the same as the elevator entering the mall, the second processor will wake up and push the user's parking space to the user (recognized parking on the road section) Location H050) and pick up route.
- the way to notify the user can be: text notification, voice notification, etc.
- Fig. 12 is a schematic diagram of pushing the pick-up route (for example, including navigation route and navigation assistance information) provided by an embodiment of the present invention; pushing the user's parking space H050, pushing the best route OJIH; if the elevator is not recognized, then Push the user's parking space, push the elevator number when entering, push the pick-up path.
- the pick-up route for example, including navigation route and navigation assistance information
- the way to obtain the pick-up path can use the data collected from the local database to obtain the optimal path from each route reference point to each elevator entrance.
- the pedometer is used to determine the path with the least number of steps as the best path, or the path with the least number of key nodes is the best path.
- the user enters the basement and automatically downloads the route library of the optimal path.
- FIG. 13 is a hardware structure diagram of a neural network processor provided by an embodiment of the present invention, in which the core part of the NPU is the arithmetic circuit 1403, and the controller 1404 controls the arithmetic circuit 1403 to extract matrix data in the memory 103 and perform multiplication operations.
- the arithmetic circuit 1403 includes multiple processing units (Process Engine, PE).
- the arithmetic circuit 1403 is a two-dimensional systolic array.
- the arithmetic circuit 1403 may also be a one-dimensional systolic array or other electronic circuit capable of performing mathematical operations such as multiplication and addition.
- the arithmetic circuit 1403 is a general-purpose matrix processor.
- the arithmetic circuit 1403 fetches the weight data corresponding to matrix B from the weight memory 1402 and caches it on each PE in the arithmetic circuit 1403.
- the arithmetic circuit 1403 fetches the matrix A data and matrix B from the input memory 1401 to perform matrix operations, and the partial result or final result of the obtained matrix is stored in the accumulator 1408.
- the unified memory 1406 is used to store input data and output data.
- the weight data is directly transferred to the weight memory 1402 through the direct memory access controller (DMAC) 1405.
- the input data is also transferred to the unified memory 1406 through the DMAC1405.
- the Bus Interface Unit (BIU) 1410 is used to realize the interaction between the Advanced Extensible Interface (AXI) bus and the DMAC 1405 or the instruction fetch buffer (Instruction Fetch Buffer) 1409.
- the bus interface unit 1410 is used for the instruction fetch memory 1409 to obtain instructions from the external memory 103, and is also used for the storage unit access controller 1405 to obtain the original data of the input matrix A or the weight matrix B from the external memory 103.
- DMAC1405 is mainly used to transfer input data from external memory 103, such as double-rate synchronous dynamic random access memory (DDR) to unified memory 1406 or to transfer weight data to weight memory 1402 or to transfer input data to input memory In 1401.
- DDR double-rate synchronous dynamic random access memory
- the vector calculation unit 1407 includes a plurality of arithmetic processing units, and if necessary, further processes the output of the arithmetic circuit, such as vector multiplication, vector addition, exponential operation, logarithmic operation, size comparison and so on. Mainly used for non-convolutional/fully connected layer (FC) network calculations in neural networks, such as Pooling, Batch Normalization, Local Response Normalization, etc. .
- the vector calculation unit 1407 can store the processed output vector to the unified buffer 1406.
- the vector calculation unit 1407 may apply a nonlinear function to the output of the arithmetic circuit 1403, such as a vector of accumulated values, to generate an activation value.
- the vector calculation unit 1407 generates a normalized value, a combined value, or both.
- the processed output vector can be used as an activation input to the arithmetic circuit 1403, for example for use in subsequent layers in a neural network.
- the instruction fetch buffer 1409 connected to the controller 1404 is used to store instructions used by the storage controller 1404; the unified memory 1406, the input memory 1401, the weight memory 1402, and the instruction fetch memory 1409 are all On-Chip memories. It is understandable that the relevant functions such as the recognition of multiple images on the road section described in this application are all implemented by the relevant functional units in the above-mentioned NPU, and will not be repeated here.
- FIG. 14 is a schematic flowchart of a navigation method provided by an embodiment of the present invention.
- the navigation method is applicable to any one of the navigation devices in FIGS. 1 and 6A and 6B and includes the navigation device. device of.
- the method may include the following steps S201-S203, and optionally, may also include step S204-step S205.
- step S201 acquiring multiple images on a road section, wherein the navigation device moves along the road section, and the road section includes a navigation target position;
- step S202 identifying the multiple images to obtain multiple signs, each A mark is used to mark a position on the road section;
- step S203 according to the multiple signs, determine multiple route reference points in the road section, and generate a navigation route for the road section based on the multiple route reference points , The navigation route is used to guide the user to the navigation target location.
- the multiple signs are M signs
- the multiple route reference points are N signs in the M signs, N is less than or equal to M, and N and M are greater than An integer of 1
- the navigation route includes the N signs.
- the multiple signs are M signs
- the multiple route reference points are L reference areas, L is less than or equal to M, and N and M are integers greater than 1
- the The navigation route includes the L reference areas, and each reference area includes at least one sign.
- Step S204 Measure the direction and the number of steps the navigation device moves between any two adjacent route reference points among the multiple route reference points;
- Step S205 Generate the navigation route according to the direction and the number of steps Navigation assistance information.
- the navigation route further includes a time stamp corresponding to at least one route reference point.
- the method further includes: pushing the navigation route to the user.
- receiving instruction information sent by an in-vehicle system where the instruction information is used to indicate that the in-vehicle system has reached the navigation target position; the navigation target position is located at the starting position of the road section, That is, when the device is along the cloud on the road section, it starts to collect images and record the starting position; according to the instruction information, trigger execution of the acquisition of multiple images on the road section.
- the road section further includes a navigation start position.
- FIG. 15 is a schematic structural diagram of another navigation device provided by an embodiment of the present invention.
- the navigation device 30 may include an acquisition unit 301, an identification unit 302, and a navigation unit 303.
- it may also include a measuring unit 304, an auxiliary unit 305, a pushing unit 306, an indicating unit 307, and a triggering unit 308.
- the acquiring unit 301 is used to acquire multiple images on the road section; and the identifying unit 302 is used to identify The multiple images are used to obtain multiple signs, and each sign is used to mark a position on the road section; the navigation unit 303 is configured to determine multiple route reference points in the road section according to the multiple signs, and A navigation route of the road segment is generated based on the multiple route reference points, and the navigation route is used to guide the user to the navigation target location.
- the device 30 further includes: a measuring unit 304, configured to measure the movement direction and the number of steps of the navigation device between any two adjacent route reference points among the multiple route reference points ; Auxiliary unit 305 for generating navigation assistance information of the navigation route according to the direction and the number of steps.
- the multiple signs are M signs
- the multiple route reference points are N signs in the M signs, N is less than or equal to M, and N and M are greater than An integer of 1
- the navigation route includes the N signs.
- the multiple signs are M signs
- the multiple route reference points are L reference areas, L is less than or equal to M
- the N and M bits are integers greater than 1, so The navigation route includes the L reference areas, and each reference area includes at least one sign.
- the navigation route further includes a time stamp corresponding to at least one route reference point.
- the device further includes: a pushing unit 306, configured to push the navigation route to the user.
- the device 30 further includes: an indication unit 307, configured to receive indication information sent by an on-board system, the indication information being used to indicate that the on-board system has reached the navigation target location; The target position is located at the start position of the road section; the trigger unit 308 is configured to trigger the execution of the acquisition of multiple images on the road section according to the indication information.
- the road section further includes a navigation start position.
- Each unit in FIG. 15 can be implemented by software, hardware, or a combination thereof.
- the hardware-implemented units can include circuits and electric furnaces, algorithm circuits or analog circuits, etc.
- a unit implemented in software may include program instructions, which is regarded as a software product, is stored in a memory, and can be run by a processor to implement related functions. For details, refer to the previous introduction.
- An embodiment of the present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and the program includes part or all of the steps of any one of the above method embodiments when executed.
- the embodiment of the present invention further provides a computer program, which includes instructions, when the computer program is executed by a computer, the computer can execute part or all of the steps of any navigation method.
- the disclosed device may be implemented in other ways.
- the device embodiments described above are merely illustrative.
- the division of the above-mentioned units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components can be combined or integrated. To another system, or some features can be ignored or not implemented.
- the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical or other forms.
- the units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
- the functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
- the above-mentioned integrated unit can be realized in the form of hardware or software functional unit.
- the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium.
- the technical solution of the present application essentially or the part that contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , Including several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc., specifically a processor in a computer device) execute all or part of the steps of the above methods in the various embodiments of the present application.
- the aforementioned storage media may include: U disk, mobile hard disk, magnetic disk, optical disk, read-only memory (Read-Only Memory, abbreviation: ROM) or Random Access Memory (Random Access Memory, abbreviation: RAM), etc.
- U disk mobile hard disk
- magnetic disk magnetic disk
- optical disk read-only memory
- Read-Only Memory abbreviation: ROM
- Random Access Memory Random Access Memory
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Abstract
A navigation apparatus and method, and a related device, wherein the navigation apparatus (10) comprises a first processor (101), and a neural network processor (104) coupled to the first processor. The first processor (101) is used to acquire a plurality of images on a road segment collected by a camera module (102) and instruct the neural network processor (104) to identify the plurality of images, wherein the navigation apparatus (10) moves along the road segment, and the road segment comprises a navigation target position. The neural network processor (104) is used to identify a plurality of images to obtain a plurality of signs, and each sign is used to mark a position on the road segment. The first processor (101) is further used to determine a plurality of route reference points in the road segment according to the plurality of signs, and generate a navigation route of the road segment on the basis of the plurality of route reference points, and the navigation route is used to guide a user towards the navigation target position. The navigation apparatus may guide the user to find a vehicle.
Description
本申请涉及导航技术领域,尤其涉及一种导航装置、方法及相关设备。This application relates to the field of navigation technology, and in particular to a navigation device, method and related equipment.
在现有的导航系统中,用户通常可以通过在线地图或者离线地图的导航方式,获取导航起始地址和导航目标地址之间的导航路线,并根据该导航路线以及当前的位置信息,进行实时导航,以到达导航目标地址。但是,在一些场景或环境中,服务端可能无法提供导航地图,或者可以提供但无法提供精准的导航地图。例如,在大型商超、写字楼、车站、停车场、游乐园、学校等场所中,用户常常由于场所过大、标志物不显著、地图无法精准定位等原因,导致用户无法快速找到目标地址、更无法快速折返至某个目标地址寻找存放物品或处理相关事务等,使得用户体验差。In the existing navigation system, the user can usually obtain the navigation route between the navigation start address and the navigation target address through online map or offline map navigation, and conduct real-time navigation based on the navigation route and current location information To reach the navigation destination address. However, in some scenarios or environments, the server may not be able to provide a navigation map, or it can provide but cannot provide an accurate navigation map. For example, in large-scale shopping malls, office buildings, stations, parking lots, amusement parks, schools and other places, users are often unable to find the target address quickly due to reasons such as large places, inconspicuous markers, and inaccurate positioning on maps. Unable to quickly return to a certain target address to find storage items or deal with related affairs, which makes the user experience poor.
以用户在停车场找车的应用场景为例,新建的住宅小区、商场、写字楼等大型建筑多数都将停车场纳入建设规划之中,停车场的建筑面积也越建越大,车位数动辄上千。由于发展速度过快,停车场内部的管理设施难以跟上,出现了因为停车场内部空间较大、环境和标志物较为类似导致的方向不易辨别的问题,往往使得车主返回停车场时难以快速找到自己的车辆,催生了新的“找车难”的问题。在这种情况下,许多停车场基于划分的停车区域和车位编码推出了反向寻车的方式,如人工寻车,刷卡寻车,监控寻车等措施帮助车主找车。但上述寻车方法,均通过大量的人力或大量的硬件设施来支撑实现,花费较大的资金,且对于用户来说,使用起来仍然较麻烦,效果不佳。其中的刷卡寻车,更离不开用户的“主动记忆行为”(主动刷卡),一旦车主下车比较匆忙,下车后没有留意停车位置,未主动刷卡,则只能凭记忆盲目地四处寻找。Take the application scenario of a user looking for a car in a parking lot as an example. Most of the newly-built residential quarters, shopping malls, office buildings and other large buildings include parking lots in the construction plan. The building area of the parking lot is also getting bigger and bigger, and the number of parking spaces is easily increased. thousand. Due to the rapid development speed, it is difficult for the management facilities inside the parking lot to keep up. There is a problem that the direction is difficult to distinguish due to the large internal space of the parking lot and the similar environment and markers, which often makes it difficult for car owners to find quickly when returning to the parking lot. Own vehicle has given birth to a new "difficult to find a car" problem. In this case, many parking lots have introduced a reverse car search method based on the divided parking area and parking space code, such as manual car search, swiping card search, monitoring car search and other measures to help car owners find cars. However, the above-mentioned car-finding methods are all supported by a large amount of manpower or a large amount of hardware facilities, which cost a large amount of money, and for users, it is still troublesome to use, and the effect is not good. The user's "active memory behavior" (active swiping card) is inseparable from the swiping card to find a car. Once the owner gets off the car in a hurry, he does not pay attention to the parking position after getting off the car, and does not actively swipe the card, so he can only blindly search around based on memory .
因此,亟需提供一种功能性和实用性较强的导航方法,为用户解决在一些环境或场所中无法快速找到导航目标地址的问题。Therefore, there is an urgent need to provide a navigation method with strong functionality and practicability to solve the problem that the user cannot quickly find the navigation target address in some environments or places.
发明内容Summary of the invention
本发明实施例提供一种导航装置、方法及相关设备,以引导用户寻找导航目标位置。The embodiment of the present invention provides a navigation device, a method and related equipment to guide a user to find a navigation target location.
第一方面,本发明实施例提供了一种导航装置,可包括:包括:第一处理器,以及耦合于所述第一处理器的神经网络处理器,其中所述第一处理器,用于获取由摄像模块采集的路段上的多个图像,指示所述神经网络处理器对所述多个图像进行识别,其中,所述导航装置沿所述路段运动,且所述路段包括导航目标位置;所述神经网络处理器,用于识别所述多个图像以得到多个标志,每个标志用于标记所述路段上一个位置;所述第一处理器,还用于根据所述多个标志,确定所述路段中的多个路线参考点,并基于所述多个路线参考点生成所述路段的导航路线,所述导航路线用于引导用户去往所述导航目标位置。In the first aspect, an embodiment of the present invention provides a navigation device, which may include: a first processor, and a neural network processor coupled to the first processor, wherein the first processor is configured to Acquiring multiple images on a road segment collected by the camera module, and instructing the neural network processor to recognize the multiple images, wherein the navigation device moves along the road segment, and the road segment includes a navigation target position; The neural network processor is configured to recognize the multiple images to obtain multiple signs, and each sign is used to mark a position on the road section; the first processor is also configured to , Determining a plurality of route reference points in the road section, and generating a navigation route of the road section based on the plurality of route reference points, and the navigation route is used to guide the user to the navigation target location.
本发明实施例,通过导航装置中的第一处理器获取由摄像模块采集的导航装置所运动的路段上的多个图像,并指示神经网络处理器对所述路段上的多个图像进行识别,最终,第一处理器根据神经网络处理器识别出的标志,确定路段中的路线参考点,并基于路线参考点生成导航路线,以帮助用户在返回时根据该导航路线可以轻松找到导航目标位置。即本发明实施例实现了仅利用导航装置就完成了寻找目标地址的导航功能,无需大量人力物 力、无需网络、也无需布局大量的硬件设备,成本低;且全程无需用户的繁琐操作,不依赖用户的主动记忆和场所环境的地图数据、网络信号等,便捷可行,极大的提升了用户导航的体验和效果。In the embodiment of the present invention, the first processor in the navigation device acquires multiple images on the road segment moved by the navigation device collected by the camera module, and instructs the neural network processor to recognize the multiple images on the road segment, Finally, the first processor determines the route reference points in the road section according to the signs recognized by the neural network processor, and generates a navigation route based on the route reference points, so as to help the user to easily find the navigation target location according to the navigation route when returning. That is, the embodiment of the present invention realizes the navigation function of finding the target address only by using the navigation device, without a lot of manpower and material resources, no network, and no need to lay out a lot of hardware equipment, and the cost is low; and the whole process does not require user's cumbersome operation and does not rely on The user's active memory, map data of the site environment, network signals, etc. are convenient and feasible, which greatly improves the user's navigation experience and effects.
在一种可能的实现方式中,所述装置还包括所述摄像模块,用于采集所述路段上的所述多个图像。在一种可能的实现方式中,所述装置还包括耦合于所述第一处理器和所述摄像模块的存储器;所述存储器,用于存储所述摄像模块采集的所述多个图像。In a possible implementation, the device further includes the camera module, configured to collect the multiple images on the road section. In a possible implementation manner, the device further includes a memory coupled to the first processor and the camera module; the memory is configured to store the multiple images collected by the camera module.
本发明实施例,通过导航装置中的摄像模块,采集导航装置所运动的路段上的多个图像,并存储至存储器中,第一处理器指示神经网络处理器对所述路段上的多个图像进行识别,最终,第一处理器根据神经网络处理器识别出的标志,确定路段中的路线参考点,并基于路线参考点生成导航路线,以帮助用户在返回时,根据该导航路线可以轻松找到导航目标位置。即本发明实施例实现了仅利用导航装置就完成了寻找目标地址的导航功能,无需大量人力物力、无需网络、也无需布局大量的硬件设备,成本低;且全程无需用户的繁琐操作,不依赖用户的主动记忆和场所环境的地图数据、网络信号等,便捷可行,极大的提升了用户导航的体验和效果。In the embodiment of the present invention, multiple images on the road section moved by the navigation device are collected through the camera module in the navigation device, and stored in the memory. The first processor instructs the neural network processor to perform the processing on the multiple images on the road section. Finally, the first processor determines the route reference point in the road segment according to the signs recognized by the neural network processor, and generates a navigation route based on the route reference point to help the user to easily find the navigation route according to the navigation route when returning Navigate to the target location. That is, the embodiment of the present invention realizes the navigation function of finding the target address only by using the navigation device, without a lot of manpower and material resources, no network, and no need to lay out a lot of hardware equipment, and the cost is low; and the whole process does not require user's cumbersome operation and does not rely on The user's active memory, map data of the site environment, network signals, etc. are convenient and feasible, which greatly improves the user's navigation experience and effects.
在一种可能的实现方式中,所述装置还包括耦合于所述第一处理器的测量模块:所述测量模块,用于测量所述导航装置在所述多个路线参考点中任意两个相邻路线参考点之间运动的方向和步数;所述第一处理器,还用于根据所述方向和步数生成所述导航路线的导航辅助信息。In a possible implementation manner, the device further includes a measurement module coupled to the first processor: the measurement module is configured to measure the navigation device at any two of the multiple route reference points The movement direction and the number of steps between adjacent route reference points; the first processor is further configured to generate navigation assistance information of the navigation route according to the direction and the number of steps.
本发明实施例,通过导航装置中的测量模块,测量导航装置在运动的路段上的任意两个相邻路线参考线之间的方向和步数,以便于第一处理器根据该方向和步数确定导航路线的导航辅助信息,以减小路线的累积误差,提高准确性。例如,该导航装置向用户推送的导航路线上包括了多个路线参考点,以及基于步行者航位推算PDR技术计算出的路线参考点之间的方向和步数。In the embodiment of the present invention, the measurement module in the navigation device measures the direction and the number of steps between any two adjacent route reference lines on the moving section of the navigation device, so that the first processor can use the direction and the number of steps Determine the navigation assistance information of the navigation route to reduce the cumulative error of the route and improve the accuracy. For example, the navigation route pushed by the navigation device to the user includes multiple route reference points, as well as the direction and number of steps between the route reference points calculated based on the pedestrian dead reckoning PDR technology.
在一种可能的实现方式中,所述多个标志是M个标志,所述多个路线参考点是所述M个标志中的N个标志,N小于或等于M,且N和M为大于1的整数,所述导航路线包括所述N个标志。可选的,所述导航路线还包括至少一个路线参考点对应的时间戳。例如,所述神经网络处理器从所述路段上的多个图像中识别出M个标志,M为大于0的整数;所述第一处理器,具体用于:从所述M个标志中确定N个标志,将所述N个标志确定为所述路段上的N个路线参考点;N为大于0且小于或者等于M的整数;根据所述N个路线参考点以及对应的时间戳,生成导航路线。In a possible implementation, the multiple signs are M signs, the multiple route reference points are N signs in the M signs, N is less than or equal to M, and N and M are greater than An integer of 1, the navigation route includes the N signs. Optionally, the navigation route further includes a time stamp corresponding to at least one route reference point. For example, the neural network processor recognizes M signs from multiple images on the road section, where M is an integer greater than 0; the first processor is specifically configured to: determine from the M signs N signs, the N signs are determined as N route reference points on the road section; N is an integer greater than 0 and less than or equal to M; generated according to the N route reference points and the corresponding time stamp Navigation route.
本发明实施例,通过导航装置中的第一处理器从神经网络处理器识别出的M个标志中,选出N个作为路段上的路线参考点,并依据该N个路线参考点的时间戳生成导航路线。即本发明实施例可以通过将部分无用或者指示效果不显著的标志进行剔除,而将指示效果更好、更显著的标志作为最终导航路线上的路线参考点,从而保证导航更准确、效果更佳。In the embodiment of the present invention, the first processor in the navigation device selects N from the M signs recognized by the neural network processor as the route reference points on the road section, and according to the timestamps of the N route reference points Generate navigation routes. That is to say, the embodiment of the present invention can eliminate some useless or insignificant signs, and use signs with better and more significant indications as the route reference points on the final navigation route, thereby ensuring more accurate navigation and better results. .
在一种可能的实现方式中,所述多个标志是M个标志,所述多个路线参考点是L个参考区域,L小于或等于M,且N和M位大于1的整数,所述导航路线包括所述L个参考区域,每个参考区域包括至少一个标志。可选的,所述导航路线还包括至少一个路线参考点对应的时间戳。例如,所述神经网络处理器从所述路段上的多个图像中识别出M个标志, M为大于0的整数;所述第一处理器,具体用于:根据所述M个标志,确定L个参考区域;其中,L为大于0且小于M的整数,任意一个所述参考区域内包括至少一个所述标志;在所述L个参考区域中的每一个参考区域中确定一个路线参考点,得到L个路线参考点;根据所述L个路线参考点以及对应的时间戳,生成导航路线。In a possible implementation manner, the multiple signs are M signs, the multiple route reference points are L reference areas, L is less than or equal to M, and N and M are integers greater than 1, the The navigation route includes the L reference areas, and each reference area includes at least one sign. Optionally, the navigation route further includes a time stamp corresponding to at least one route reference point. For example, the neural network processor recognizes M signs from multiple images on the road section, where M is an integer greater than 0; the first processor is specifically configured to: determine according to the M signs L reference areas; where L is an integer greater than 0 and less than M, any one of the reference areas includes at least one of the signs; a route reference point is determined in each of the L reference areas , Obtain L route reference points; generate a navigation route according to the L route reference points and the corresponding time stamp.
本发明实施例,通过导航装置中的第一处理器从神经网络处理器识别出的M个标志中,确定出L个参考区域,并最终在每个参考区域中确定一个路线参考点。即将用户的视线范围作为一个参考区域,在每个参考区域中,选定一个标志作为路线参考点,将部分重复指示的标志进行剔除,去除冗余信息,不仅能保证用户在视线范围内均有路线参考点,也能保证导航效果更佳。In the embodiment of the present invention, the first processor in the navigation device determines L reference areas from the M signs recognized by the neural network processor, and finally determines a route reference point in each reference area. That is, the user's line of sight is taken as a reference area. In each reference area, a sign is selected as the route reference point, and some of the signs with repeated indications are removed, and redundant information is removed, which not only ensures that the user has the sight range Route reference points can also ensure better navigation effects.
在一种可能的实现方式中,所述装置还包括耦合于所述第一处理器的第二处理器;所述第一处理器还用于,将所述导航路线发送至所述第二处理器;所述第二处理器,还用于将所述导航路线推送给所述用户。In a possible implementation manner, the device further includes a second processor coupled to the first processor; the first processor is further configured to send the navigation route to the second processor The second processor is also used to push the navigation route to the user.
本发明实施例中,第一处理器可以作为较低功耗的协处理器,第二处理器可以作为正常功耗的主处理器,当协处理器生成了用户所需的导航路线之后,并判断出用户达到返回导航起始地址时,唤醒主处理器,并将判断结果发送给主处理器;主处理器则可以将该导航路线通过文本、图像、语音等多媒体方式推送给用户,以保证用户能够轻松找到导航目标地址,且能保证导航装置的低功耗性。In the embodiment of the present invention, the first processor can be used as a coprocessor with lower power consumption, and the second processor can be used as a main processor with normal power consumption. After the coprocessor generates the navigation route required by the user, and When it is judged that the user reaches the return navigation starting address, the main processor is awakened and the judgment result is sent to the main processor; the main processor can push the navigation route to the user through multimedia methods such as text, image, and voice to ensure The user can easily find the navigation target address, and can ensure the low power consumption of the navigation device.
在一种可能的实现方式中,所述路段还包括导航起始位置,所述导航起始位置位于所述路段的终点位置。In a possible implementation manner, the road section further includes a navigation start position, and the navigation start position is located at the end position of the road section.
本发明实施例,可以通过导航装置中的第一处理器指示神经网络处理器根据摄像模块采集的图像识别路段的终点位置,例如,若导航目标位置是路段上的起始位置,为停车场的停车位,导航起始位置则是路段上用来采集和识别图像的终点位置,为用户退出停车场的位置,那么神经网络处理器可以通过识别用户的停车点和退出停车场的点,并根据识别结果,判断用户是否返回停车场寻找停车位,以使得第一处理器可以在恰当的时机唤醒第二处理器向用户推送导航路线,帮助用户轻松找到自己的车,极大的提升了用户找车导航的体验和效果。In the embodiment of the present invention, the neural network processor can be instructed by the first processor in the navigation device to recognize the end position of the road section according to the image collected by the camera module. For example, if the navigation target position is the starting position on the road section, it is the parking lot. For parking spaces, the navigation start position is the end position used to collect and recognize images on the road section, and is the position where the user exits the parking lot. Then the neural network processor can identify the user’s parking point and the exit point of the parking lot, and according to According to the recognition result, it is judged whether the user returns to the parking lot to find a parking space, so that the first processor can wake up the second processor at the right time to push the navigation route to the user, help the user find his car easily, and greatly improve the user search The experience and effects of car navigation.
在一种可能的实现方式中,所述第二处理器,还用于:接收车载系统发送的指示信息,所述指示信息用于指示所述车载系统到达了所述导航目标位置;所述导航目标位置位于所述路段上用于采集和识别图像的起始位置;根据所述指示信息唤醒所述第一处理器以获取由所述摄像模块采集的所述路段上的所述多个图像。可选的,所述导航目标位置为车辆在停车场的停车位。In a possible implementation manner, the second processor is further configured to: receive instruction information sent by an in-vehicle system, where the instruction information is used to indicate that the in-vehicle system has reached the navigation target location; The target position is located at the starting position for collecting and recognizing images on the road section; the first processor is awakened according to the instruction information to acquire the multiple images on the road section collected by the camera module. Optionally, the navigation target location is a parking space of the vehicle in a parking lot.
本发明实施例,可应用于停车场找车场景中,通过导航装置和车载系统的交互,使得车载系统可以在车辆进入停车场找到停车位之前,利用车辆自身的摄像系统,抓取停车地点的停车位图像,并识别出停车位,然后通过蓝牙或Wi-Fi等通信方式发送给本申请中的导航装置,再通过导航装置中的第二处理器唤醒第一处理器控制摄像模块开始采集路段上的多个图像。例如,当导航装置为用户随身携带的智能手机、智能手环或智能眼镜等时,在用户驾驶车辆进入停车场到达停车位之前,由于不方便利用该导航装置进行停车场环境以及停车位的图像采集与识别,此时可通过用户驾驶的车辆中自带的前置或后置摄像头进 行图像的采集,并通过车载系统进行识别;而在车辆达到停车场的停车位与用户退出停车场之间的路段,由于用户会携带导航装置,则更方便对该路段上的标志进行识别。从而保证了导航装置可以在恰当的时机开始采集图像,从而保证了路段上的标志识别的完整性和准确性,帮助用户轻松找到自己的车,极大的提升了用户找车导航的体验和效果。The embodiment of the present invention can be applied to the scene of finding a car in a parking lot. Through the interaction between the navigation device and the on-board system, the on-board system can use the vehicle's own camera system to capture the parking location before the vehicle enters the parking lot to find a parking space. The parking space image, and the parking space is identified, and then sent to the navigation device in this application through communication methods such as Bluetooth or Wi-Fi, and then the second processor in the navigation device wakes up the first processor to control the camera module to start collecting road sections Multiple images on the. For example, when the navigation device is a smart phone, smart bracelet or smart glasses that the user carries, before the user drives the vehicle into the parking lot and arrives at the parking space, it is inconvenient to use the navigation device to image the parking lot environment and parking space. Acquisition and recognition. At this time, the image can be collected through the front or rear camera in the vehicle driven by the user, and recognized through the on-board system; and between the vehicle reaching the parking space of the parking lot and the user exiting the parking lot Because the user will carry a navigation device, it is more convenient to identify the signs on the road section. This ensures that the navigation device can start collecting images at the right time, thereby ensuring the completeness and accuracy of the sign recognition on the road section, helping users find their car easily, and greatly improving the user experience and effect of car-finding navigation .
第二方面,本发明实施例提供了一种导航方法,应用于导航装置,可包括:所述方法包括:获取路段上的多个图像,其中,所述导航装置沿所述路段运动,且所述路段包括导航目标位置;识别所述多个图像以得到多个标志,每个标志用于标记所述路段上一个位置;根据所述多个标志,确定所述路段中的多个路线参考点,并基于所述多个路线参考点生成所述路段的导航路线,所述导航路线用于引导用户去往所述导航目标位置。In a second aspect, an embodiment of the present invention provides a navigation method applied to a navigation device, which may include: the method includes: acquiring a plurality of images on a road section, wherein the navigation device moves along the road section, and The road segment includes a navigation target location; the multiple images are identified to obtain multiple signs, each of which is used to mark a position on the road segment; and multiple route reference points in the road segment are determined according to the multiple signs , And generate a navigation route of the road segment based on the multiple route reference points, and the navigation route is used to guide the user to the navigation target location.
在一种可能的实现方式中,所述方法还包括:测量所述导航装置在所述多个路线参考点中任意两个相邻路线参考点之间运动的方向和步数;根据所述方向和步数生成所述导航路线的导航辅助信息。In a possible implementation, the method further includes: measuring the direction and number of steps the navigation device moves between any two adjacent route reference points among the multiple route reference points; And the number of steps to generate navigation assistance information of the navigation route.
在一种可能的实现方式中,所述多个标志是M个标志,所述多个路线参考点是所述M个标志中的N个标志,N小于或等于M,且N和M为大于1的整数,所述导航路线包括所述N个标志。In a possible implementation, the multiple signs are M signs, the multiple route reference points are N signs in the M signs, N is less than or equal to M, and N and M are greater than An integer of 1, the navigation route includes the N signs.
在一种可能的实现方式中,所述多个标志是M个标志,所述多个路线参考点是L个参考区域,L小于或等于M,且N和M位大于1的整数,所述导航路线包括所述L个参考区域,每个参考区域包括至少一个标志。In a possible implementation manner, the multiple signs are M signs, the multiple route reference points are L reference areas, L is less than or equal to M, and N and M are integers greater than 1, the The navigation route includes the L reference areas, and each reference area includes at least one sign.
在一种可能的实现方式中,所述导航路线还包括至少一个路线参考点对应的时间戳。In a possible implementation manner, the navigation route further includes a time stamp corresponding to at least one route reference point.
在一种可能的实现方式中,所述方法还包括:将所述导航路线推送给所述用户。In a possible implementation manner, the method further includes: pushing the navigation route to the user.
在一种可能的实现方式中,所述方法还包括:接收车载系统发送的指示信息,所述指示信息用于指示所述车载系统到达了所述导航目标位置;所述导航目标位置位于所述路段的起始位置;根据所述指示信息触发执行所述获取路段上的多个图像。In a possible implementation manner, the method further includes: receiving instruction information sent by an in-vehicle system, where the instruction information is used to indicate that the in-vehicle system has reached the navigation target position; the navigation target position is located in the The starting position of the road section; triggering the execution of the acquiring of multiple images on the road section according to the indication information.
在一种可能的实现方式中,所述路段还包括导航起始位置,所述导航起始位置位于所述路段的终点位置。In a possible implementation manner, the road section further includes a navigation start position, and the navigation start position is located at the end position of the road section.
第三方面,本申请提供一种导航装置,该导航装置具有实现上述任意一种导航方法的功能。该功能可以通过硬件实现,也可以通过硬件执行相应的软件实现。该硬件或软件包括一个或多个与上述功能相对应的模块。In a third aspect, the present application provides a navigation device, which has the function of implementing any of the above-mentioned navigation methods. This function can be realized by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-mentioned functions.
第四方面,本申请提供一种终端,该终端包括处理器,处理器被配置为支持该终端执行第二方面提供的一种导航方法中相应的功能。该终端还可以包括存储器,存储器用于与处理器耦合,其保存终端必要的程序指令和数据。该终端还可以包括通信接口,用于该终端与其它设备或通信网络通信。In a fourth aspect, the present application provides a terminal, the terminal includes a processor, and the processor is configured to support the terminal to perform a corresponding function in a navigation method provided in the second aspect. The terminal may also include a memory, which is used for coupling with the processor and stores necessary program instructions and data for the terminal. The terminal may also include a communication interface for the terminal to communicate with other devices or communication networks.
第五方面,本申请提供一种计算机存储介质,所述计算机存储介质存储有计算机程序,该计算机程序被处理器执行时实现上述第二方面中任意一项所述的导航方法流程。In a fifth aspect, the present application provides a computer storage medium that stores a computer program that, when executed by a processor, implements the navigation method process described in any one of the above-mentioned second aspects.
第六方面,本发明实施例提供了一种计算机程序,该计算机程序包括指令,当该计算机程序被计算机执行时,使得计算机可以执行上述第二方面中任意一项所述的导航方法流 程。In a sixth aspect, an embodiment of the present invention provides a computer program, the computer program includes instructions, when the computer program is executed by a computer, the computer can execute the navigation method flow described in any one of the second aspect.
第七方面,本申请提供了一种芯片系统,该芯片系统包括处理器,用于实现上述第二方面中任意一项所述的导航方法流程所涉及的功能。在一种可能的设计中,所述芯片系统还包括存储器,所述存储器,用于保存导航方法必要的程序指令和数据。该芯片系统,可以由芯片构成,也可以包含芯片和其它分立器件。In a seventh aspect, the present application provides a chip system, which includes a processor, configured to implement the functions involved in the process of the navigation method described in any one of the second aspects. In a possible design, the chip system further includes a memory for storing program instructions and data necessary for the navigation method. The chip system can be composed of chips, or include chips and other discrete devices.
图1是本发明实施例提供的一种导航装置的结构示意图;Figure 1 is a schematic structural diagram of a navigation device provided by an embodiment of the present invention;
图2是本发明实施例提供的一种路段上的多个图像的识别示意图;FIG. 2 is a schematic diagram of identifying multiple images on a road section according to an embodiment of the present invention;
图3是本发明实施例提供的一种停车场导航示意图;Fig. 3 is a schematic diagram of parking lot navigation provided by an embodiment of the present invention;
图4是本发明实施例提供的另一种停车场导航示意图;Figure 4 is a schematic diagram of another parking lot navigation provided by an embodiment of the present invention;
图5是本发明实施例提供的另一种导航装置的结构示意图;Figure 5 is a schematic structural diagram of another navigation device provided by an embodiment of the present invention;
图6A是本发明实施例提供的又一种导航装置的结构示意图;FIG. 6A is a schematic structural diagram of another navigation device provided by an embodiment of the present invention;
图6B是本发明实施例提供的又一种导航装置的结构示意图;6B is a schematic structural diagram of yet another navigation device provided by an embodiment of the present invention;
图7是本发明实施例提供的一种停车场停车位的分布图;FIG. 7 is a distribution diagram of parking spaces in a parking lot provided by an embodiment of the present invention;
图8是本发明实施例提供的另一种路段上的多个图像的识别示意图;FIG. 8 is a schematic diagram of identifying multiple images on another road section according to an embodiment of the present invention;
图9是本发明实施例提供的一种相邻路线参考点之间的方向和步数识别示意图;9 is a schematic diagram of identifying directions and steps between reference points of adjacent routes according to an embodiment of the present invention;
图10是本发明实施例提供的又一种路段上的多个图像识别示意图:FIG. 10 is another schematic diagram of multiple image recognition on a road section according to an embodiment of the present invention:
图11是本发明实施例提供的参考区域划分示意图;FIG. 11 is a schematic diagram of reference area division according to an embodiment of the present invention;
图12是本发明实施例提供的路径推送示意图;FIG. 12 is a schematic diagram of path push provided by an embodiment of the present invention;
图13是本发明实施例提供的一种神经网络处理器硬件结构图;FIG. 13 is a hardware structure diagram of a neural network processor provided by an embodiment of the present invention;
图14是本发明实施例提供的一种导航方法的流程示意图;14 is a schematic flowchart of a navigation method provided by an embodiment of the present invention;
图15是本发明实施例提供的又一种导航装置的结构示意图。FIG. 15 is a schematic structural diagram of another navigation device provided by an embodiment of the present invention.
下面将结合本发明实施例中的附图,对本发明实施例进行描述。The embodiments of the present invention will be described below in conjunction with the drawings in the embodiments of the present invention.
本申请的说明书和权利要求书及所述附图中的术语“第一”、“第二”、“第三”和“第四”等是用于区别不同对象,而不是用于描述特定顺序。此外,术语“包括”和“具有”以及它们任何变形,意图在于覆盖不排他的包含。例如包含了一系列步骤或单元的过程、方法、系统、产品或设备没有限定于已列出的步骤或单元,而是可选地还包括没有列出的步骤或单元,或可选地还包括对于这些过程、方法、产品或设备固有的其它步骤或单元。The terms "first", "second", "third" and "fourth" in the specification and claims of this application and the drawings are used to distinguish different objects, not to describe a specific order . In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes unlisted steps or units, or optionally also includes Other steps or units inherent to these processes, methods, products or equipment.
在本文中提及“实施例”意味着,结合实施例描述的特定特征、结构或特性可以包含在本申请的至少一个实施例中。在说明书中的各个位置出现该短语并不一定均是指相同的实施例,也不是与其它实施例互斥的独立的或备选的实施例。本领域技术人员显式地和隐式地理解的是,本文所描述的实施例可以与其它实施例相结合。Reference to "embodiments" herein means that a specific feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art clearly and implicitly understand that the embodiments described herein can be combined with other embodiments.
在本说明书中使用的术语“部件”、“模块”、“系统”等用于表示计算机相关的实体、硬件、固件、硬件和软件的组合、软件、或执行中的软件。例如,部件可以是但不限于,在处理 器上运行的进程、处理器、对象、可执行文件、执行线程、程序和/或计算机。通过图示,在计算设备上运行的应用和计算设备都可以是部件。一个或多个部件可驻留在进程和/或执行线程中,部件可位于一个计算机上和/或分布在2个或更多个计算机之间。此外,这些部件可从在上面存储有各种数据结构的各种计算机可读介质执行。部件可例如根据具有一个或多个数据分组(例如来自与本地系统、分布式系统和/或网络间的另一部件交互的二个部件的数据,例如通过信号与其它系统交互的互联网)的信号通过本地和/或远程进程来通信。The terms "component", "module", "system", etc. used in this specification are used to denote computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process, a processor, an object, an executable file, an execution thread, a program, and/or a computer running on a processor. Through the illustration, both the application running on the computing device and the computing device can be components. One or more components may reside in processes and/or threads of execution, and components may be located on one computer and/or distributed among two or more computers. In addition, these components can be executed from various computer readable media having various data structures stored thereon. A component can be based on a signal having one or more data packets (for example, data from two components interacting with another component between a local system, a distributed system, and/or a network, such as the Internet that interacts with other systems through signals) Communicate through local and/or remote processes.
首先,对本申请中的部分用语进行解释说明,以便于本领域技术人员理解。First, some terms in this application are explained to facilitate the understanding of those skilled in the art.
(1)停车场,是供车辆停放之场所。停车场有仅画停车格而无人管理及收费的简易停车场,亦有配有出入栏口、泊车管理员及计时收款员的收费停车场。现代化的停车场常有自动化计时收费系统、闭路电视及录影机系统。平面停车场(亦称广场式),具有一定的用地面积,通过交通标线划分成通道和停车泊位,配上指向箭头和标志等交通设施。其停车方式包括垂直式(与通道呈直角)、平行式(与通道呈平行)、斜列式和交错式布置四种方式。本申请中的停车场可包括地下停车场、路面停车场以及立体停车场等,本申请对停车场的具体形式不作限定。(1) The parking lot is a place for vehicles to park. There are simple parking lots that only draw parking grids without management and charge, and there are also paid parking lots equipped with entry and exit barriers, parking guards and hourly cashiers. Modern parking lots often have automated time-toll collection systems, closed-circuit television and video recorder systems. The flat parking lot (also known as the square type) has a certain area of land, divided into passages and parking spaces by traffic markings, and equipped with traffic facilities such as pointing arrows and signs. Its parking methods include vertical (at right angles to the passage), parallel (parallel to the passage), diagonal and staggered arrangements. The parking lot in this application may include an underground parking lot, a road parking lot, a three-dimensional parking lot, etc. The application does not limit the specific form of the parking lot.
(2)人工智能(Artificial Intelligence,AI),是利用数字计算机或者数字计算机控制的机器模拟、延伸和扩展人的智能,感知环境、获取知识并使用知识获得最佳结果的理论、方法、技术及应用系统。换句话说,人工智能是计算机科学的一个分支,它企图了解智能的实质,并生产出一种新的能以人类智能相似的方式作出反应的智能机器。人工智能也就是研究各种智能机器的设计原理与实现方法,使机器具有感知、推理与决策的功能。人工智能领域的研究包括机器人,自然语言处理,计算机视觉,决策与推理,人机交互,推荐与搜索,AI基础理论等。(2) Artificial Intelligence (AI) is the use of digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results. operating system. In other words, artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that can react in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that the machines have the functions of perception, reasoning and decision-making. Research in the field of artificial intelligence includes robotics, natural language processing, computer vision, decision-making and reasoning, human-computer interaction, recommendation and search, and basic AI theories.
(3)卷积神经网络(Convolutional Neural Network,CNN)是一种多层的神经网络,每层有多个二维平面组成,而每个平面由多个独立神经元组成,每个平面的多个神经元共享权重,通过权重共享可以降低神经网络中的参数数目。目前,在卷积神经网络中,处理器进行卷积操作通常是将输入信号特征与权重的卷积,转换为信号矩阵与权重矩阵之间的矩阵乘运算。在具体矩阵乘运算时,对信号矩阵和权重矩阵进行分块处理,得到多个分形(Fractional)信号矩阵和分形权重矩阵,然后对多个分形信号矩阵和分形权重矩阵进行矩阵乘和累加运算。(3) Convolutional Neural Network (CNN) is a multi-layer neural network. Each layer is composed of multiple two-dimensional planes, and each plane is composed of multiple independent neurons. Each neuron shares weights, and the number of parameters in the neural network can be reduced through weight sharing. At present, in convolutional neural networks, the convolution operation performed by the processor usually converts the convolution of the input signal characteristics and the weight into a matrix multiplication operation between the signal matrix and the weight matrix. In the specific matrix multiplication operation, the signal matrix and the weight matrix are divided into blocks to obtain multiple fractal signal matrices and fractal weight matrices, and then the multiple fractal signal matrices and fractal weight matrices are subjected to matrix multiplication and accumulation operations.
(4)惯性测量单元(Inertial measurement unit,IMU)是测量物体三轴姿态角(或角速率)以及加速度的装置,一般包含了三个单轴的加速度计和三个单轴的陀螺,其中,加速度计用于检测物体在载体坐标系统独立三轴的加速度信号,陀螺则用于检测载体相对于导航坐标系的角速度信号,并测量物体在三维空间中的角速度和加速度,并以此解算出物体的姿态。IMU大多用在需要进行运动控制的设备,如汽车和机器人上,且一般要安装在被测物体的重心上。(4) Inertial measurement unit (IMU) is a device that measures the three-axis attitude angle (or angular rate) and acceleration of an object. It generally includes three single-axis accelerometers and three single-axis gyroscopes. Among them, The accelerometer is used to detect the acceleration signal of the object in the independent three-axis of the carrier coordinate system, and the gyroscope is used to detect the angular velocity signal of the carrier relative to the navigation coordinate system, and measure the angular velocity and acceleration of the object in the three-dimensional space, and then calculate the object Gesture. IMUs are mostly used in equipment that requires motion control, such as automobiles and robots, and are generally installed on the center of gravity of the object to be measured.
(5)行人航位推算(Pedestrian Dead Reckoning,PDR)主要是在无信标环境下使用惯性测量单元(Inertial Measurement Unit,IMU)感知人员在行进过程中的加速度、角速度、磁力和压力等数据,并利用这些数据对行进人员进行步长与方向的推算,从而达到对人员进 行定位跟踪的目的,其中主要涉及的过程有步态检测、步长和方向计算。(5) Pedestrian Dead Reckoning (PDR) mainly uses Inertial Measurement Unit (IMU) in a beacon-free environment to sense the acceleration, angular velocity, magnetic force, and pressure of a person in the process of traveling. And use these data to calculate the step length and direction of the marching personnel, so as to achieve the purpose of positioning and tracking the personnel. The main processes involved are gait detection, step length and direction calculation.
(6)光学字符识别(Optical Character Recognition,OCR)是指对文本资料的图像文件进行分析识别处理,获取文字及版面信息的过程。一般分为两个步骤:文字定位,即找到文字在图像中的位置;文字识别,即识别出找到的文字。文字定位也可能包含一些二值化,矫正的步骤。(6) Optical character recognition (Optical Character Recognition, OCR) refers to the process of analyzing and recognizing image files of text data to obtain text and layout information. Generally divided into two steps: text positioning, that is, finding the position of the text in the image; text recognition, that is, identifying the found text. Text positioning may also include some binarization and correction steps.
(7)快速目标物检测(Faster-RCNN)可以用于解决复杂的计算机视觉问题,并取得很好的效果。其思想在于利用神经网络中conv+pooling并没有改变特征在图像中的相对位置。其中RCNN(Regions with CNN)目标物检测,可以将卷积神经网络应用region proposal的策略,自底下上训练可以用来定位目标物和图像分。R-CNN是采用Selective Search算法来提取(propose)可能的感兴趣区域(regions of interest,RoIs),然后对每个提取区域采用标准CNN进行分类。(7) Fast object detection (Faster-RCNN) can be used to solve complex computer vision problems and achieve good results. The idea is that using conv+pooling in the neural network does not change the relative position of the features in the image. Among them, RCNN (Regions with CNN) target detection can apply the strategy of region proposal to the convolutional neural network, and training from bottom to top can be used to locate the target and image division. R-CNN uses the Selective Search algorithm to extract (propose) possible regions of interest (regions of interest, RoIs), and then uses standard CNN to classify each extracted region.
(8)导航,是用于确定目标对象位置并引导用户去往所述位置的操作。本实施例的导航技术能够用于引导用户去往其车辆所在位置,即引导用户找车。需要说明的是,本实施涉及的车或车辆泛指交通工具,包括但不限于机动车或非机动车。机动车可包括汽车、电动车、摩托车、智能车、电瓶车或拖拉机等;非机动车包括但不限于自行车、脚踏车或滑板车等。(8) Navigation is an operation used to determine the location of the target object and guide the user to the location. The navigation technology of this embodiment can be used to guide the user to the location of his vehicle, that is, to guide the user to find a car. It should be noted that the vehicles or vehicles involved in this implementation generally refer to vehicles, including but not limited to motor vehicles or non-motor vehicles. Motor vehicles may include automobiles, electric vehicles, motorcycles, smart cars, battery cars or tractors, etc.; non-motor vehicles include but are not limited to bicycles, bicycles, or scooters.
为了便于理解本发明实施例,以下结合停车场找车的应用场景,以及几种常见的找车方法,进一步分析本发明实施例具体所要解决的技术问题。常见的找车方法及应用场景包括如下:传统方法,车主一般通过大脑记忆结合停车场内一些特殊标志物的方式寻车;或者,车主利用手机对停车位以及周围环境进行拍照记录,寻车时根据拍照记录的停车位并结合停车场内部的指示,找到自己的车。上述传统方法的缺点:完全依赖用户的记忆,且通常会因为记忆不准确、指示不明、地下车库环境复杂,消耗大量的时间、精力和体力,效果不佳。In order to facilitate the understanding of the embodiments of the present invention, the following will further analyze the specific technical problems to be solved by the embodiments of the present invention in combination with the application scenarios of car finding in the parking lot and several common car finding methods. Common methods and application scenarios for finding cars include the following: traditional methods, car owners generally find cars through brain memory combined with some special landmarks in the parking lot; or, car owners use their mobile phones to take pictures and record the parking space and the surrounding environment. Find your car according to the parking space recorded by the photo and the instructions inside the parking lot. The shortcomings of the above-mentioned traditional methods: completely dependent on the user's memory, and usually due to inaccurate memory, unclear instructions, and complex underground garage environment, which consumes a lot of time, energy and physical strength, and the effect is not good.
场景一,全球定位系统(Global Positioning System,GPS)导航定位找车:GPS技术广泛用于导航定位,但GPS信号在地下或室内的信号强度弱,定位效果不理想。所以室内定位通常采用Wi-Fi定位技术、蓝牙定位技术、地磁定位技术等。其中Wi-Fi定位在室内定位中运用最广泛。通常可以利用Wi-Fi定位技术实现停车场找车的功能:用户停车结束后,标记停车的位置,当用户取车离开时,通过Wi-Fi定位确定当前的位置,再根据停车场内部地图,确定取车导航路径。Scenario 1: Global Positioning System (GPS) navigation and positioning to find a car: GPS technology is widely used for navigation and positioning, but the signal strength of GPS signals in the underground or indoors is weak, and the positioning effect is not ideal. Therefore, indoor positioning usually uses Wi-Fi positioning technology, Bluetooth positioning technology, and geomagnetic positioning technology. Among them, Wi-Fi positioning is the most widely used in indoor positioning. Wi-Fi positioning technology can usually be used to realize the function of finding a car in a parking lot: after the user stops parking, mark the location of the parking, when the user picks up the car and leaves, the current location is determined through Wi-Fi positioning, and then according to the internal map of the parking lot, Determine the navigation route for the pickup.
上述GPS导航定位的缺点:Wi-Fi定位技术需要在环境内部署大量路由器。如果停车场内部署Wi-Fi系统只用于寻车,则会带来效率低、成本高的问题。类似的,地磁定位、蓝牙定位用于停车场寻车时,也会带来成本高的问题。The disadvantage of the above-mentioned GPS navigation and positioning: Wi-Fi positioning technology requires the deployment of a large number of routers in the environment. If the Wi-Fi system deployed in the parking lot is only used for car search, it will bring about low efficiency and high cost. Similarly, when geomagnetic positioning and Bluetooth positioning are used to find a car in a parking lot, it will also cause high costs.
场景二,车联网导航定位找车:用户停车时,利用汽车的车载系统获取当前停车位对应的标志图像;根据第上述标志图像识别停车位的位置信息,通过车联网并发送给手机;用户返回寻车时,手机扫描人行出口的二维码,获取当前位置(每个二维码信息对应停车场内部地图的某个预设的位置);根据停车位的位置和用户当前的位置,以及停车场内部地图,规划导航路径。Scenario 2: IoV navigation and positioning to find a car: When the user parks, use the car's on-board system to obtain the sign image corresponding to the current parking space; identify the location information of the parking space according to the above-mentioned sign image, and send it to the mobile phone through the Internet of Vehicles; the user returns When looking for a car, the mobile phone scans the QR code of the pedestrian exit to obtain the current location (each QR code information corresponds to a preset location on the map of the parking lot); according to the location of the parking space and the current location of the user, and parking The map inside the field, planning the navigation path.
上述车联网导航定位的缺点:需要用户的车辆都使用车联网,且依赖于停车场的内部地图,成本高、操作繁琐。The disadvantages of the above-mentioned IoV navigation and positioning: all of the users' vehicles are required to use the IoV and rely on the internal map of the parking lot, which is costly and cumbersome to operate.
场景三,通过停车场摄像头找车:每个停车位布置一个或多个摄像头,用户停车时,车位附近的摄像头记录车牌,并将车牌与车位的对应信息发送至服务器;用户取车时,利用手机查询车牌信息,得到相应的停车位置;最终利用停车场地图搜索停车位置,得到取车导航路径。Scenario 3: Finding a car through a parking lot camera: Each parking space is equipped with one or more cameras. When the user parks, the camera near the parking space records the license plate and sends the corresponding information of the license plate and the parking space to the server; when the user picks up the car, use it The mobile phone queries the license plate information to get the corresponding parking location; finally use the parking lot map to search for the parking location, and get the car navigation route.
上述通过停车场摄像头找车的缺点:依赖大量的摄像头,依赖停车场内部地图,且需要用户需要使用APP输入车牌信息,成本高、操作繁琐。本申请提出一种方法,和上述方法的对比见下表1。The above-mentioned disadvantages of finding a car through parking lot cameras: relying on a large number of cameras, relying on the map of the parking lot, and requiring users to use APP to input license plate information, which is costly and cumbersome. This application proposes a method, and the comparison with the above method is shown in Table 1 below.
表1Table 1
因此,针对上述传统方法以及三个常见的应用场景,本发明实施例主要解决的找车问题具体包括以下:针对现有技术成本高、操作繁琐、用户体验不佳的问题,提供一种低成本、操作简易、用户体验佳的导航装置。即本申请需要提供一种不依赖环境部署、不需要用户操作的“低成本、零操作、微感知”的快捷找车方案。Therefore, in view of the above-mentioned traditional methods and three common application scenarios, the car-finding problem that the embodiment of the present invention mainly solves specifically includes the following: in view of the high cost of the prior art, the cumbersome operation, and the poor user experience, a low cost is provided. , Navigation device with easy operation and good user experience. That is, this application needs to provide a "low-cost, zero-operation, and micro-sensing" fast car-finding solution that does not rely on environmental deployment and does not require user operations.
可以理解的是,本申请所解决的技术问题包括但不限于上述停车场找车场景中的导航问题,上述应用场景只是本申请中示例性的应用场景,本申请中的导航装置、方法及相关设备的应用场景,还可以包括例如,在大型商超、写字楼、车站、停车场、游乐园、学校等场所中,用户折返寻找某个标志物、商品、物品存放地等过程中的导航等,后续场景及举例描述中将不再一一列举和赘述。It is understandable that the technical problems solved by this application include, but are not limited to, the navigation problem in the above-mentioned parking lot car-finding scene. The above-mentioned application scene is only an exemplary application scene in this application. The navigation device, method and related The application scenarios of the equipment can also include, for example, in large-scale shopping malls, office buildings, stations, parking lots, amusement parks, schools and other places, the navigation in the process of turning back and looking for a certain landmark, commodity, storage place, etc., The following scenes and example descriptions will not be listed and repeated.
基于上述,下面结合本发明实施例提供的导航装置在停车场寻车场景中的应用为例进行描述。请参见图1,图1是本发明实施例提供的一种导航装置的结构示意图,该导航装置10中可包括:第一处理器101,以及耦合于所述第一处理器的神经网络处理器104;可选的,该导航装置10还可以包括摄像模块102,存储器103;其中,摄像模块102,用于采集路段上的多个图像,并存储至所述存储器,所述导航装置沿所述路段运动,且所述路段包括导航目标位置,例如,所述路段包括车辆达到停车场的停车位与用户退出停车场之间的路段,导航目标位置为车辆达到停车场的停车位。摄像模块101可以为终端设备的后置摄像头,前置摄像头、侧置摄像头,旋转摄像头或可翻转摄像头等。摄像模块101的主要作用是可以在实现导航过程中实时跟踪、捕捉导航装置在运动路段上的环境图像,例如,从停车位到电梯之间的环境图像。可选的,摄像模块102为低功耗、低分辨率帧的摄像模块,或者摄像模块102在采集路段上的多个图像时是以低功耗、低分辨率帧的模式工作的。即只要摄像模块102采集的路段上的多个图像,满足神经网络处理器104可以识别出其中 的标志即可,以减少功耗。Based on the foregoing, the following describes the application of the navigation device provided in the embodiment of the present invention in a car-searching scene in a parking lot as an example. Please refer to FIG. 1, which is a schematic structural diagram of a navigation device provided by an embodiment of the present invention. The navigation device 10 may include: a first processor 101 and a neural network processor coupled to the first processor 104; Optionally, the navigation device 10 may also include a camera module 102 and a memory 103; wherein the camera module 102 is used to collect multiple images on the road section and store them in the memory, and the navigation device along the The road segment is moving, and the road segment includes the navigation target position. For example, the road segment includes the road segment between the parking space where the vehicle reaches the parking lot and the user exiting the parking lot, and the navigation target position is the parking space where the vehicle reaches the parking lot. The camera module 101 may be a rear camera, a front camera, a side camera, a rotating camera or a reversible camera of the terminal device. The main function of the camera module 101 is to track and capture the environment image of the navigation device on the moving road section in real time during the navigation process, for example, the environment image from the parking space to the elevator. Optionally, the camera module 102 is a camera module with low power consumption and low-resolution frames, or the camera module 102 operates in a low-power, low-resolution frame mode when collecting multiple images on a road section. That is, as long as the multiple images on the road section collected by the camera module 102 satisfy the requirement that the neural network processor 104 can recognize the signs, the power consumption can be reduced.
存储器103,用于存储摄像模块102采集的路段上的多个图像。该存储器103可以作为导航装置10中的共享内存,即第一处理器101、摄像模块102以及神经网络处理器104均可与存储器103相连,且存储器103可用于为第一处理器101、摄像模块102以及神经网络处理器104提供相关图像采集、识别以及生成导航路线所需的内存空间。The memory 103 is used to store multiple images on the road section collected by the camera module 102. The memory 103 can be used as a shared memory in the navigation device 10. That is, the first processor 101, the camera module 102, and the neural network processor 104 can all be connected to the memory 103, and the memory 103 can be used for the first processor 101 and the camera module. 102 and the neural network processor 104 provide memory space required for related image collection, recognition, and generation of navigation routes.
第一处理器101,用于指示神经网络处理器104对路段上的多个图像进行识别。可选的,第一处理器101可以作为导航装置10的协处理器,在导航装置10处于待机或者休眠状态下,仍然支持摄像模块102以及神经网络处理器104正常工作,以保证路段上的多个图像的采集与识别。例如,本申请中的第一处理器101可以为智能传感集线器(Sensor hub),而Sensor hub为基于低功耗微控制单元(Microcontroller Unit,MCU)和轻量级实时多任务操作系统(Real Time Operating System,RTOS)之上的软硬件结合的解决方案,可以连接并处理来自各种传感器设备的数据,由于其低功耗和轻量级的特征,因此可以保证导航装置10以较低的功耗为用户进行寻车导航。The first processor 101 is used to instruct the neural network processor 104 to recognize multiple images on the road section. Optionally, the first processor 101 can be used as a co-processor of the navigation device 10. When the navigation device 10 is in a standby or dormant state, it still supports the normal operation of the camera module 102 and the neural network processor 104, so as to ensure that there is more information on the road. Collection and recognition of images. For example, the first processor 101 in this application may be a smart sensor hub (Sensor hub), and the Sensor hub is based on a low-power microcontroller unit (MCU) and a lightweight real-time multitasking operating system (Real Time Operating System (RTOS) is a solution that combines software and hardware, which can connect and process data from various sensor devices. Due to its low power consumption and lightweight features, it can ensure that the navigation device 10 The power consumption is for the user to search and navigate the car.
神经网络处理器(Neutral Processing Unit,NPU)104,用于识别所述多个图像以得到多个标志,每个标志用于标记所述路段上一个位置,例如,标志为停车场里的各类标志图像,比如,数字、字母、直线箭头、斜箭头、转弯箭头、指定图案、出口(EXIT)、方框、入口(ENTRANCE)、标志语等。如图2所示,图2是本发明实施例提供的一种路段上的多个图像的识别示意图,在多个路段上的多个图像中,标志为H050、H084、K108、K120、L005、L100、M008、M070、电梯3等。可以理解的是,神经网络处理器104需要提前收集大量的需要学习的停车场标志等,然后对需要学习的停车场标志进行图像预处理、再利用深度学习引擎对处理后的图像进行训练,得到深度学习的模型,并训练好分类器,最后可以使用训练好的模型和分类器对接收的路段上的多个图像进行识别。A neural network processor (Neutral Processing Unit, NPU) 104 is used to identify the multiple images to obtain multiple signs, and each sign is used to mark a position on the road section. For example, the signs are various types in a parking lot. Logo images, such as numbers, letters, straight arrows, diagonal arrows, turning arrows, designated patterns, exits (EXIT), boxes, entrances (ENTRANCE), signs, etc. As shown in Figure 2, Figure 2 is a schematic diagram of the recognition of multiple images on a road section provided by an embodiment of the present invention. In the multiple images on multiple road sections, the marks are H050, H084, K108, K120, L005, L100, M008, M070, elevator 3, etc. It is understandable that the neural network processor 104 needs to collect a large number of parking lot signs that need to be learned in advance, and then perform image preprocessing on the parking lot signs that need to be learned, and then use the deep learning engine to train the processed images to obtain Deep learning model, and train the classifier, and finally use the trained model and classifier to recognize multiple images on the received road section.
第一处理器102,还用于根据所述神经网络处理器104识别出的标志,确定所述路段中的路线参考点,并基于所述路线参考点生成导航路线。即第一处理器102根据神经网络处理器104识别出的标志,确定用户在停车位与退出停车场地点之间的路线参考点,该路线参考点可以理解为导航路线上的指示节点。例如,当识别出的标志包括:“A027、A029、A030、A032、B005、B006、B007、B008、箭头、ENTRANCE入口”时,则可以按照预设的规则(如,剔除相隔较近的标志、剔除指示不明确的标志等),从其中的标志中确定出路线参考点为“A027、A030、A032、B005、B008、箭头、ENTRANCE出口”,最终依据反向寻车原则,根据路线参考点生成导航路线为:“ENTRANCE→箭头→出口→B008→B005→A032→A030→A027”。本发明实施例,对于如何从标志中确定出路线参考点不作具体限定,对如何根据路线参考点生成导航路线也不作具体限定。The first processor 102 is further configured to determine a route reference point in the road section according to the sign recognized by the neural network processor 104, and generate a navigation route based on the route reference point. That is, the first processor 102 determines the route reference point of the user between the parking space and the exit parking place according to the sign recognized by the neural network processor 104, and the route reference point can be understood as an indication node on the navigation route. For example, when the recognized signs include: "A027, A029, A030, A032, B005, B006, B007, B008, arrows, ENTRANCE entrance", you can follow the preset rules (e.g., exclude signs that are closer, Eliminate signs with unclear indications, etc.), determine the route reference point from the signs as "A027, A030, A032, B005, B008, arrow, ENTRANCE exit", and finally generate it according to the route reference point according to the principle of reverse car search The navigation route is: "ENTRANCE→Arrow→Exit→B008→B005→A032→A030→A027". The embodiment of the present invention does not specifically limit how to determine the route reference point from the sign, nor does it specifically limit how to generate the navigation route according to the route reference point.
可选的,上述神经网络处理器104也可以作为第一处理器101中的一部分集成在第一处理器101中;也可以为耦合于上述第一处理器101,且能实现路段上的多个图像中标志识别的其它功能芯片或芯片内模块;同理,第一处理器101所执行的功能也可以位于一个芯片或分布在多个不同的功能芯片上执行,本发明实施例对此不作具体限定。Optionally, the aforementioned neural network processor 104 can also be integrated in the first processor 101 as a part of the first processor 101; it can also be coupled to the aforementioned first processor 101 and can implement multiple Other functional chips or modules in the chip recognized by the logo in the image; similarly, the functions executed by the first processor 101 can also be located on one chip or distributed on multiple different functional chips, which are not specifically described in the embodiment of the present invention. limited.
本发明实施例,通过导航装置中的摄像模块,采集车辆达到停车场的停车位与用户退出停车场之间的路段上的多个图像,并存储至存储器中,第一处理器指示神经网络处理器 对所述路段上的多个图像进行识别,最终,第一处理器根据神经网络处理器识别出的标志,确定路段中的路线参考点,并基于路线参考点生成导航路线,以帮助用户在返回找车时,根据该导航路线可以轻松找到导航目标位置。即本发明实施例实现了仅利用导航装置就完成了找车的导航功能,无需大量人力物力,也无需布局大量的硬件设备,成本低;且全程无需用户的繁琐操作,不依赖用户的主动记忆和停车场的地图数据、网络信号等,便捷可行,极大的提升了用户找车导航的体验和效果。In the embodiment of the present invention, through the camera module in the navigation device, multiple images on the road section between the vehicle reaching the parking space of the parking lot and the user exiting the parking lot are collected and stored in the memory. The first processor instructs the neural network to process The device recognizes multiple images on the road section. Finally, the first processor determines the route reference point in the road section according to the signs recognized by the neural network processor, and generates a navigation route based on the route reference point to help the user in When returning to find a car, you can easily find the navigation target location according to the navigation route. That is to say, the embodiment of the present invention realizes the navigation function of finding a car only by using the navigation device, without a lot of manpower and material resources, and no need to lay out a lot of hardware equipment, and the cost is low; and the whole process does not require the user's cumbersome operation and does not rely on the user's active memory The map data and network signal of the parking lot are convenient and feasible, which greatly improves the user experience and effect of car navigation.
在一种可能的实现方式中,所述多个标志是M个标志,所述多个路线参考点是所述M个标志中的N个标志,N小于或等于M,且N和M为大于1的整数,所述导航路线包括所述N个标志。例如,神经网络处理器104从所述路段上的多个图像中识别出M个标志;第一处理器101从所述M个标志中确定N个标志,将所述N个标志确定为所述路段上的N个路线参考点,并根据所述N个路线参考点以及对应的时间戳,生成导航路线。如图3所示,图3为本发明实施例提供的一种停车场导航示意图,其中,神经网络处理器104从多个路段上的多个图像中识别出M个标志,例如,为A014、B004、A015、B005、A016、B006、A017、B007、A018、B008、A019、B009、A020、B010、F008、E001、E003、E004、D001、D002、D003、D004、D005、D006;则M等于24。第一处理器101从上述M个标志中确定N个标志,例如,为B004、B005、B006、B007、B008、B009、B010、E001、E003、D001、D002、D003、D004、D005、D006,则N等于15,其确定原则为从相同指示作用的标志中确定出一个即可。最终根据该15个路线参考点以及对应的时间戳,并结合反向取车原则生成导航路线为:“D006→D005→D004→D003→D002→D001→E003→E001→B010→B009→B008→B007→B006→B005→B004”。In a possible implementation, the multiple signs are M signs, the multiple route reference points are N signs in the M signs, N is less than or equal to M, and N and M are greater than An integer of 1, the navigation route includes the N signs. For example, the neural network processor 104 recognizes M signs from multiple images on the road section; the first processor 101 determines N signs from the M signs, and determines the N signs as the N route reference points on the road segment, and a navigation route is generated according to the N route reference points and the corresponding time stamp. As shown in FIG. 3, FIG. 3 is a schematic diagram of parking lot navigation provided by an embodiment of the present invention, in which the neural network processor 104 recognizes M signs from multiple images on multiple road sections, for example, A014, B004, A015, B005, A016, B006, A017, B007, A018, B008, A019, B009, A020, B010, F008, E001, E003, E004, D001, D002, D003, D004, D005, D006; then M is equal to 24 . The first processor 101 determines N signs from the above M signs, for example, B004, B005, B006, B007, B008, B009, B010, E001, E003, D001, D002, D003, D004, D005, D006, then N is equal to 15, and its determination principle is to determine one of the signs with the same indicating function. Finally, based on the 15 route reference points and the corresponding time stamps, combined with the principle of reverse pickup, the navigation route is generated as: "D006→D005→D004→D003→D002→D001→E003→E001→B010→B009→B008→B007 →B006→B005→B004".
在一种可能的实现方式中,所述多个标志是M个标志,所述多个路线参考点是L个参考区域,L小于或等于M,且N和M位大于1的整数,所述导航路线包括所述L个参考区域,每个参考区域包括至少一个标志。例如,神经网络处理器104从所述路段上的多个图像中识别出M个标志;第一处理器103根据所述M个标志,确定L个参考区域,在所述L个参考区域中的每一个参考区域中确定一个路线参考点;根据确定的L个路线参考点以及对应的时间戳,生成导航路线。如图4所示,图4为本发明实施例提供的另一种停车场导航示意图,其中,神经网络处理器104从多个路段上的多个图像中识别出M个标志,为A014、B004、A015、B005、A016、B006、A017、B007、A018、B008、A019、B009、A020、B010、F008、E001、E003、E004、D001、D002、D003、D004、D005、D006;则M等于24。第一处理器101根据上述M个标志,确定L个参考区域,为参考区域1、参考区域2、参考区域3、参考区域4、参考区域5、参考区域6、参考区域7、参考区域8;则L=8;并在上述L个参考区域中的每一个参考区域中确定一个路线参考点,比如参考区域1中的路线参考点为B005、参考区域2中的路线参考点为B007,参考区域3中的路线参考点为B009,参考区域4中的路线参考点为B010,参考区域5中的路线参考点为E011,参考区域6中的路线参考点为D001,参考区域7中的路线参考点为D003,参考区域8中的路线参考点为D005,确定原则为从可视范围内的一个参考区域中确定出一个路线参考点即可。因此,第一处理器101根据确定的L个路线参考点以及对应的时间戳,并结合反向取 车原则生成导航路线为:“D005→D003→D002→D001→E011→B010→B009→B007→B005”。In a possible implementation manner, the multiple signs are M signs, the multiple route reference points are L reference areas, L is less than or equal to M, and N and M are integers greater than 1, the The navigation route includes the L reference areas, and each reference area includes at least one sign. For example, the neural network processor 104 recognizes M signs from a plurality of images on the road section; the first processor 103 determines L reference regions according to the M signs, and among the L reference regions A route reference point is determined in each reference area; a navigation route is generated according to the determined L route reference points and the corresponding time stamp. As shown in FIG. 4, FIG. 4 is a schematic diagram of another parking lot navigation provided by an embodiment of the present invention, in which the neural network processor 104 recognizes M signs from multiple images on multiple road sections, which are A014, B004 , A015, B005, A016, B006, A017, B007, A018, B008, A019, B009, A020, B010, F008, E001, E003, E004, D001, D002, D003, D004, D005, D006; then M is equal to 24. The first processor 101 determines L reference areas according to the above M signs, which are reference area 1, reference area 2, reference area 3, reference area 4, reference area 5, reference area 6, reference area 7, and reference area 8. Then L=8; and determine a route reference point in each of the above L reference areas, for example, the route reference point in reference area 1 is B005, the route reference point in reference area 2 is B007, and the reference area The route reference point in reference area 3 is B009, the route reference point in reference area 4 is B010, the route reference point in reference area 5 is E011, the route reference point in reference area 6 is D001, and the route reference point in reference area 7 It is D003, the route reference point in the reference area 8 is D005, and the determination principle is to determine a route reference point from a reference area within the visible range. Therefore, the first processor 101 generates the navigation route according to the determined L route reference points and the corresponding timestamps, combined with the reverse pick-up principle: "D005→D003→D002→D001→E011→B010→B009→B007→ B005".
在一种可能的实现方式中,所述路段还包括导航起始位置,所述导航起始位置位于所述路段的终点位置。例如,导航起始位置为用户退出停车场的电梯,识别的标志为“EXIT出口”、“电梯B”等。本发明实施例,可以通过导航装置中的第一处理器指示神经网络处理器根据摄像模块采集的图像识别路段的终点位置,例如,若导航目标位置是路段上开始采集和和别图像的起始位置,为停车场的停车位,导航起始位置则是路段上的结束采集和和别图像的所述终点位置,为用户退出停车场的位置,那么神经网络处理器可以通过识别用户的停车点和退出停车场的点,并根据识别结果,判断用户是否返回停车场寻找停车位,以使得第一处理器可以在恰当的时机唤醒第二处理器向用户推送导航路线,帮助用户轻松找到自己的车,极大的提升了用户找车导航的体验和效果。In a possible implementation manner, the road section further includes a navigation start position, and the navigation start position is located at the end position of the road section. For example, the navigation starting position is the elevator where the user exits the parking lot, and the recognized signs are "EXIT", "Elevator B", etc. In the embodiment of the present invention, the first processor in the navigation device may be used to instruct the neural network processor to recognize the end position of the road section according to the image collected by the camera module. For example, if the navigation target position is the start of the collection and the start of the other image on the road section The location is the parking space of the parking lot, and the navigation start location is the end location of the end collection and different images on the road segment. It is the location where the user exits the parking lot. Then the neural network processor can identify the user’s parking spot And the point of exiting the parking lot, and based on the recognition results, determine whether the user returns to the parking lot to find a parking space, so that the first processor can wake up the second processor at the right time to push the navigation route to the user, helping the user find their own The car greatly improves the user experience and effect of car-finding navigation.
请参见图5,图5是本发明实施例提供的另一种导航装置的结构示意图。基于上述图1中的导航装置10的结构,该导航装置10还可以包括与第一处理器101耦合的测量模块105。其中,测量模块105,用于测量所述导航装置在所述路段中任意两个相邻路线参考点之间的方向和步数,并存储至存储器103。可选的,测量模块可以为惯性测量单元(Inertial measurement unit,IMU),IMU是一种通过传感器组合(加速度计、陀螺仪和磁力计)来测量和报告速度、方向和重力中至少一项的电子设备。可实现3自由度(DoF,degrees of freedom)或6DoF的数据测量。因此可以利用IMU基于步行者航位推算PDR技术测算用户在第一路上的方向和步数。可以理解的是,上述IMU可以作为第一处理器101中的一部分,也可以为耦合于上述第一处理器101功能芯片或芯片内模块,本发明实施例对此不作具体限定。Please refer to FIG. 5, which is a schematic structural diagram of another navigation device according to an embodiment of the present invention. Based on the structure of the navigation device 10 in FIG. 1, the navigation device 10 may further include a measurement module 105 coupled with the first processor 101. Wherein, the measurement module 105 is used to measure the direction and the number of steps between any two adjacent route reference points of the navigation device in the road section, and store the direction and the number of steps in the memory 103. Optionally, the measurement module can be an inertial measurement unit (IMU), which is a combination of sensors (accelerometer, gyroscope, and magnetometer) to measure and report at least one of speed, direction, and gravity Electronic equipment. It can achieve 3 degrees of freedom (DoF, degrees of freedom) or 6DoF data measurement. Therefore, IMU can be used to calculate the user's direction and number of steps on the first road based on pedestrian dead reckoning PDR technology. It can be understood that the aforementioned IMU may be used as a part of the first processor 101, or may be coupled to the functional chip or an on-chip module of the first processor 101, which is not specifically limited in the embodiment of the present invention.
存储器103,还用于存储测量模块105测量的所述方向和步数。该存储器103可以作为导航装置10中的共享内存,例如,用于存储摄像模块102所采集的图像以及存储测量模块所测量的所述方向和步数等。第一处理器101,还用于根据所述方向和步数生成所述导航路线的导航辅助信息。例如,为车主提供导航路线的同时,还为其提供导航辅助信息,用于指示任意两个相邻的路线参考点之间的方向和步数,强化导航路线的指示效果,帮助车主轻松找到导航目标位置。The memory 103 is also used to store the direction and the number of steps measured by the measurement module 105. The memory 103 can be used as a shared memory in the navigation device 10, for example, for storing images collected by the camera module 102 and storing the directions and steps measured by the measurement module. The first processor 101 is further configured to generate navigation assistance information of the navigation route according to the direction and the number of steps. For example, while providing navigation routes for car owners, they also provide navigation assistance information for them to indicate the direction and number of steps between any two adjacent route reference points, enhancing the indication effect of navigation routes, and helping car owners find navigation easily target location.
本发明实施例,通过导航装置中的测量模块,测量所述导航装置在停车场的停车位与用户退出停车场之间的路段上的任意两个相邻路线参考线之间的方向和步数,以便于第一处理器根据该方向和步数确定导航路线的导航辅助信息,以减小路线的累积误差,提高准确性。例如,该导航装置向用户推送的导航路线上包括了多个路线参考点,以及基于步行者航位推算PDR技术计算出的路线参考点之间的方向和步数。In the embodiment of the present invention, the measurement module in the navigation device measures the direction and the number of steps of the navigation device between any two adjacent route reference lines on the road section between the parking space of the parking lot and the user exiting the parking lot. , So that the first processor determines the navigation assistance information of the navigation route according to the direction and the number of steps, so as to reduce the accumulated error of the route and improve the accuracy. For example, the navigation route pushed by the navigation device to the user includes multiple route reference points, as well as the direction and number of steps between the route reference points calculated based on the pedestrian dead reckoning PDR technology.
请参见图6A,图6A是本发明实施例提供的又一种导航装置的结构示意图。基于上述图1中的导航装置10的结构,该导航装置10还可以包括耦合于第一处理器101的第二处理器106,且第一处理器101的功耗低于第二处理器106的功耗;可选的,如图6B所示,图6B是本发明实施例提供的又一种导航装置的结构示意图,基于上述图6A中的导航装置10的结构,该导航装置10还可以包括测量模块105,具体可参照图5。假设所述用户退出 停车场的地点为导航起始位置。其中,第一处理器101,还用于将所述导航路线发送至所述第二处理器。在发明实施例中,由于第一处理器相对于第二处理器来说功耗更低,因此第一处理器101可以作为协处理器,以较低功耗进行导航路线生成的相关操作,而第二处理器106则可以作为主处理器执行对运算能力要求更高的其他相关操作,因此可以处理除生成导航路线以外的其他流程。Please refer to FIG. 6A, which is a schematic structural diagram of another navigation device provided by an embodiment of the present invention. Based on the structure of the navigation device 10 in FIG. 1 above, the navigation device 10 may also include a second processor 106 coupled to the first processor 101, and the power consumption of the first processor 101 is lower than that of the second processor 106. Power consumption; optionally, as shown in FIG. 6B, FIG. 6B is a schematic structural diagram of another navigation device provided by an embodiment of the present invention. Based on the structure of the navigation device 10 in FIG. 6A, the navigation device 10 may also include For the measurement module 105, refer to FIG. 5 for details. It is assumed that the location where the user exits the parking lot is the starting position of the navigation. Wherein, the first processor 101 is further configured to send the navigation route to the second processor. In the embodiment of the invention, since the first processor has lower power consumption than the second processor, the first processor 101 can be used as a co-processor to perform operations related to navigation route generation with lower power consumption. The second processor 106 can be used as the main processor to perform other related operations that require higher computing capabilities, and therefore can process other processes than generating the navigation route.
第二处理器106,用于将所述导航路线推送给送至所述用户。例如,当第一处理器101根据识别结果确定用户当前返回到了之前退出停车场的导航起始位置(即路段的终点位置)时,那么则判定为该用户当前需要返回取车,因此将识别结果发送给第二处理器106,第二处理器106则将导航路线推送给用户,则无需用户自主去获取导航路线,更加方便快捷,提升用户体验。在一种可能的实现方式中,所述路段还包括导航起始位置。The second processor 106 is configured to push the navigation route to the user. For example, when the first processor 101 determines according to the recognition result that the user is currently returning to the navigation starting position (that is, the end position of the road section) where the user exited the parking lot, then it is determined that the user currently needs to return to pick up the car, so the recognition result is It is sent to the second processor 106, and the second processor 106 pushes the navigation route to the user. There is no need for the user to independently obtain the navigation route, which is more convenient and faster, and improves the user experience. In a possible implementation manner, the road section further includes a navigation start position.
在一种可能的实现方式中,第二处理器106,还用于接收车载系统发送的指示信息,所述指示信息用于指示所述车载系统到达了所述导航目标位置;根据所述指示信息唤醒所述第一处理器控制所述摄像模块采集所述路段上的多个图像。即通过车载系统和导航装置10之间的交互触发导航装置10进入导航模式,即开始采集识别路段上的多个图像。其中,车载系统可以为车辆上的车载系统,其可以控制其车载内部或外部的摄像装置来采集识别周围的环境图像,以识别车辆当前是否达到停车场的停车位。In a possible implementation, the second processor 106 is further configured to receive instruction information sent by the on-board system, where the instruction information is used to indicate that the on-board system has reached the navigation target position; according to the instruction information Wake up the first processor to control the camera module to collect multiple images on the road section. That is, the interaction between the vehicle-mounted system and the navigation device 10 triggers the navigation device 10 to enter the navigation mode, that is, starts to collect and identify multiple images on the road section. Among them, the vehicle-mounted system may be a vehicle-mounted system on a vehicle, which can control a camera device inside or outside the vehicle to collect and identify surrounding environment images to identify whether the vehicle currently reaches a parking space in a parking lot.
可以理解的是,第二处理器106还用于运行通用操作系统软件,并在通用操作系统软件的作用下控制第一处理器101的运行。进一步地,第二处理器106还用于处理完成导航过程中其他相关的计算处理和控制等。可以理解的是,上述图1、图6A或图6B中的导航装置,可以位于终端(如智能手机、平板、智能可穿戴设备等)、智能拍照设备(智能相机、智能摄像机、智能追踪设备)、智能监控设备、航拍无人机中等,本申请对此不再一一列举。It can be understood that the second processor 106 is also used to run general operating system software and control the operation of the first processor 101 under the action of the general operating system software. Further, the second processor 106 is also used for processing and completing other related calculation processing and control in the navigation process. It is understandable that the navigation device in Figure 1, Figure 6A or Figure 6B can be located in a terminal (such as a smart phone, tablet, smart wearable device, etc.), a smart camera device (smart camera, smart camera, smart tracking device) , Intelligent monitoring equipment, aerial drones, etc., this application will not list them all.
本发明实施例中,通过导航装置和车载系统的交互,使得车载系统可以在车辆进入停车场找到停车位之前,利用车辆自身的摄像系统,抓取停车地点的停车位图像,并识别出停车位,然后通过蓝牙或Wi-Fi等通信方式发送给本申请中的导航装置,再通过导航装置中的第二处理器唤醒第一处理器控制摄像模块开始采集路段上的多个图像。例如,当导航装置为用户随身携带的智能手机、智能手环或智能眼镜等时,在用户驾驶车辆进入停车场到达停车位之前,由于不方便利用该导航装置进行停车场环境以及停车位的图像采集与识别,此时可通过用户驾驶的车辆中自带的前置或后置摄像头进行图像的采集,并通过车载系统进行识别;而在车辆达到停车场的停车位与用户退出停车场之间的路段,由于用户会携带导航装置,则更方便对该路段上的标志进行识别。从而保证了导航装置可以在恰当的时机开始采集图像,从而保证了路段上的标志识别的完整性和准确性,帮助用户轻松找到自己的车,极大的提升了用户找车导航的体验和效果。In the embodiment of the present invention, through the interaction between the navigation device and the on-board system, the on-board system can use the vehicle's own camera system to capture the parking space image of the parking place and identify the parking space before the vehicle enters the parking lot to find the parking space. , And then send it to the navigation device in this application through communication methods such as Bluetooth or Wi-Fi, and then wake up the first processor through the second processor in the navigation device to control the camera module to start collecting multiple images on the road section. For example, when the navigation device is a smart phone, smart bracelet or smart glasses that the user carries, before the user drives the vehicle into the parking lot and arrives at the parking space, it is inconvenient to use the navigation device to image the parking lot environment and parking space. Acquisition and recognition. At this time, the image can be collected through the front or rear camera in the vehicle driven by the user, and recognized through the on-board system; and between the vehicle reaching the parking space of the parking lot and the user exiting the parking lot Because the user will carry a navigation device, it is more convenient to identify the signs on the road section. This ensures that the navigation device can start collecting images at the right time, thereby ensuring the completeness and accuracy of the sign recognition on the road section, helping users find their car easily, and greatly improving the user experience and effect of car-finding navigation .
下面以导航装置为智能手机或为智能手机中的一部分为例,结合上述图6A所提供的导航装置的结构,基于用户在停车场停车、寻车导航的应用场景描述智能手机是如何实现申请中的路线导航的。以下实施例一中以第一处理器101为Sensor hub、摄像模块102为手机的后置摄像头camera,存储器103为共享内存、第二处理器为主处理器,如中央处理单元(CPU)为例,根据智能手机中的功能模块在时序上所执行的功能,可以包括以下步 骤。The following takes the navigation device as a smart phone or a part of a smart phone as an example, combined with the structure of the navigation device provided in Figure 6A, based on the application scenario of the user parking in the parking lot and car-finding navigation, how the smart phone is implemented in the application Route navigation. In the following embodiment 1, the first processor 101 is the Sensor hub, the camera module 102 is the rear camera of the mobile phone, the memory 103 is shared memory, and the second processor is the main processor, such as a central processing unit (CPU), as an example. According to the functions performed by the functional modules in the smart phone in time sequence, the following steps can be included.
步骤1:情景识别(camera采集图像放到共享内存,Sensor hub调用NPU识别图像,NPU将识别信息返回给Sensor hub)。用户下车,此时摄像模块(camera)抓取到地下车库环境的图像(例如大量停放的汽车),则可以认为用户进入地下车库停车路线记录模式;当camera获取到的图像不再是地下车库环境的图像,或者识别到用户进入电梯/扶梯时,则可以判定用户退出地下车库停车路线记录模式;当camera再次获取到地下车库环境的图像时,则判定用户进入返回找车模式。Step 1: Scene recognition (camera collects the image and puts it in the shared memory, the Sensor hub calls the NPU to recognize the image, and the NPU returns the identification information to the Sensor hub). When the user gets off the car, when the camera captures the image of the underground garage environment (such as a large number of parked cars), it can be considered that the user enters the underground garage parking route recording mode; when the image captured by the camera is no longer an underground garage Image of the environment, or when it recognizes that the user enters the elevator/escalator, it can be determined that the user exits the underground garage parking route recording mode; when the camera acquires the image of the underground garage environment again, it determines that the user enters the return to the car-finding mode.
步骤2:图像采集与识别(camera采集图像存放到共享内存,调用NPU识别图像,NPU将识别信息返回给Sensor hub)。当进入地下车库识别模式时,camera开始采集周围环境的图像(即路段上的多个图像),当退出地下车库模式时,camera停止采集周围环境的图像,假设图2是camera采集到的路段上的多个图像的集合。图7是本发明实施例提供的一种停车场停车位的分布图,则停车场的地面、柱子等物体上通常会被标注的位置信息即为本申请中的所述标志,例如A区、B区、停车位A01等。其中,NPU可以通过单发多盒监测器(Single Shot MultiBox Detector,SSD)/快速目标物检测(fast RCNN)和光学字符识别(OCR)等识别算法识别出图像中的标志,包括进入停车场的标志(导航目标位置)、路段中的停车场标志(路线参考点)以及用户乘坐的电梯号(导航起始位置)等。Step 2: Image collection and recognition (the camera collects the image and stores it in the shared memory, calls the NPU to recognize the image, and the NPU returns the recognition information to the Sensorhub). When entering the underground garage recognition mode, the camera starts to collect images of the surrounding environment (ie multiple images on the road section). When exiting the underground garage mode, the camera stops collecting images of the surrounding environment. Assume that Figure 2 is on the road section collected by the camera. A collection of multiple images. FIG. 7 is a distribution diagram of parking spaces in a parking lot provided by an embodiment of the present invention. The location information usually marked on the ground, pillars and other objects of the parking lot is the sign in this application, such as area A, Area B, parking space A01, etc. Among them, the NPU can recognize the signs in the image through recognition algorithms such as Single Shot MultiBox Detector (SSD)/fast target detection (fast RCNN) and optical character recognition (OCR), including those entering the parking lot. Signs (navigation target location), parking lot signs (route reference points) in the road section, and elevator number (navigation starting position) the user takes.
步骤3:记录离库路线(Sensor hub记录NPU返回的信息,如下表2所示)。Step 3: Record the departure route (Sensor hub records the information returned by the NPU, as shown in Table 2 below).
表2Table 2
时间戳 | 信息information |
时间戳1Timestamp 1 |
H050 |
时间戳2Timestamp 2 | H084H084 |
…… | …… |
时间戳nTimestamp n | M044M044 |
按时间戳记录路线确定路段中的路线参考点,可以简单的只给区号例如H-K-L-M,详细的,也可以给出:H050→H080→H118→K010→K055→K112→L120→L077→L020→M005→M030→M044。Record the route according to the timestamp to determine the route reference point in the road segment. You can simply give the area code such as HKLM. For details, you can also give: H050→H080→H118→K010→K055→K112→L120→L077→L020→M005→ M030→M044.
步骤4:取车路径推送(通过第二处理器实现)。停车完毕,即识别用户从地下车库模式→其它模式(电梯、直梯、人行出口),或开始取车,即识别用户其它模式→地下车库模式时,第一处理器唤醒第二处理器,推送用户的停车点:即推送停车位置对应的编号,(一般认为识别的第一个编号为停车位);推送用户进入商场时乘坐的电梯(标号3);建议用户乘坐第3电梯取车,并推送取车路径(H-K-L-M的逆向);通知用户的方式可以是:利用文本通知、语音通知、多媒体通知等。具体地,第二处理器可以运行软件,包括但不限于操作系统和应用软件,来生成用户界面,并在用户界面上通过文本或图像来做所述通知,本发明实施例对此不限定。Step 4: Push the pick-up path (implemented by the second processor). When parking is completed, it recognizes the user from underground garage mode→other modes (elevator, elevator, pedestrian exit), or starts to pick up the car, namely recognizes the user’s other modes→underground garage mode, the first processor wakes up the second processor and pushes User’s parking spot: Push the number corresponding to the parking position (generally considered that the first number identified is the parking space); push the elevator the user takes when entering the mall (label 3); it is recommended that the user take the third elevator to pick up the car, and Push the pick-up path (the reverse of HKLM); the way to notify users can be: text notification, voice notification, multimedia notification, etc. Specifically, the second processor may run software, including but not limited to operating system and application software, to generate a user interface, and use text or image to make the notification on the user interface, which is not limited in the embodiment of the present invention.
本发明实施例利用低功耗的的器件采集信息,例如低功耗的camera,获取用户从停车完毕下车到离开停车场的路径信息,当用户寻车时,手机自动推送路径信息。The embodiment of the present invention uses low-power devices to collect information, such as a low-power camera, to obtain path information from the user after parking the car to leaving the parking lot. When the user finds a car, the mobile phone automatically pushes the path information.
下面以导航装置为智能手机或为智能手机中的一部分为例,结合上述图6B所提供的导 航装置的结构,基于用户在停车场停车、寻车导航的应用场景描述智能手机是如何实现申请中的路线导航的。以下实施例二中以第一处理器101为Sensor hub、摄像模块102为手机的后置摄像头camera、存储器103为共享内存、测量模块为MCU、第二处理器为主处理器为例,根据智能手机中的功能模块在时序上所执行的功能,可以包括以下步骤。The following takes the navigation device as a smart phone or a part of a smart phone as an example, combined with the structure of the navigation device provided in Figure 6B, based on the application scenarios of the user parking in the parking lot and car-finding navigation, how the smart phone is implemented in the application Route navigation. In the second embodiment below, the first processor 101 is the Sensor hub, the camera module 102 is the rear camera of the mobile phone, the memory 103 is the shared memory, the measurement module is the MCU, and the second processor is the main processor. The functions performed by the functional modules in the mobile phone in time sequence may include the following steps.
步骤1:情景识别,与上述实施例一相同。步骤2:图像采集与识别,与上述实施例一相同。步骤3:记录离库路线(Sensor hub得到由NPU返回的信息,如下表3所示)。Step 1: Scene recognition, which is the same as the first embodiment. Step 2: Image acquisition and recognition are the same as the first embodiment above. Step 3: Record the departure route (Sensor hub gets the information returned by the NPU, as shown in Table 3 below).
表3table 3
时间戳Timestamp | 位置position |
步数和角度Steps and |
时间戳1Timestamp 1 | A100A100 | 00 |
时间戳2 |
A012A012 | 30度,100步30 degrees, 100 steps |
…… | …… | …… |
时间戳nTimestamp n | D032D032 | 20度,200步20 degrees, 200 steps |
如图8和图9所示,图8为本发明实施例提供的另一种路段上的多个图像的识别示意图,图9为本发明实施例提供的一种相邻路线参考点之间的方向和步数识别示意图。从识别出起始点A100(路线参考点),开始计步,轨迹和方向使用PDR(步行者航位推算),当识别出A012(路线参考点)时,记录从A100开始到A102的步数和方向;类似的,当识别出一个路线参考点时,记录上一个路线参考点到下个路线参考点的步数和方向;可选的,当每识别一个路线参考点时,计数清零,可以减少累积误差。As shown in FIGS. 8 and 9, FIG. 8 is a schematic diagram of identifying multiple images on another road section provided by an embodiment of the present invention, and FIG. 9 is a schematic diagram of an adjacent route reference point provided by an embodiment of the present invention. Schematic diagram of direction and step identification. After identifying the starting point A100 (route reference point), start counting steps, use PDR (pedestrian dead reckoning) for trajectory and direction, and record the number of steps from A100 to A102 when A012 (route reference point) is identified Direction; similarly, when a route reference point is identified, the number of steps and directions from the previous route reference point to the next route reference point are recorded; optionally, the count is cleared every time a route reference point is identified, you can Reduce cumulative error.
第四步:取车路径推送。取车的流程与实施例一相同,通知用户的方式,除了文字和语音,也可以显示图像化的轨迹等,本发明实施例对此不作具体限定。Step 4: Push the pick-up path. The process of picking up the car is the same as in the first embodiment. In addition to text and voice, the way of informing the user can also display a graphical trajectory, which is not specifically limited in the embodiment of the present invention.
下面以导航装置为智能手机或为智能手机中的一部分为例,结合上述图6B所提供的导航装置的结构,基于用户在停车场停车、寻车导航的应用场景描述智能手机是如何实现本申请中的路线导航的。以下实施例三中以第一处理器101为Sensor hub、摄像模块102为手机的后置摄像头camera,存储器103为共享内存、测量模块为MCU、第二处理器为主处理器为例,根据智能手机中的功能模块在时序上所执行的功能,可以包括以下步骤。The following takes the navigation device as a smart phone or a part of a smart phone as an example, combined with the structure of the navigation device provided in Figure 6B, based on the application scenarios of the user parking in the parking lot and car-finding navigation, how the smart phone implements this application In the route navigation. In the third embodiment below, the first processor 101 is the Sensor hub, the camera module 102 is the rear camera of the mobile phone, the memory 103 is the shared memory, the measurement module is the MCU, and the second processor is the main processor. The functions performed by the functional modules in the mobile phone in time sequence may include the following steps.
第一步:情景识别,与实施例一相同。第二步:图像采集与识别,与实施例一相同。第三步:记录每张图像(路段上的多个图像)中的标志,如图10所示,图10为本发明实施例提供的又一种路段上的多个图像识别示意图:以视距范围设定路线参考点,如图11所示,图11为本发明实施例提供的参考区域划分示意图,例如每15个停车位为一个参考区域,假如H区共120个车位,则H区共有8个参考区域。根据第二步识别出的停车位置例如H050,记录H050所在的参考区域H4。The first step: scene recognition, the same as the first embodiment. The second step: image acquisition and recognition, the same as the first embodiment. The third step: record the signs in each image (multiple images on the road section), as shown in FIG. 10, which is another schematic diagram of multiple image recognition on the road section provided by the embodiment of the present invention: Set the route reference point in the range, as shown in FIG. 11, which is a schematic diagram of the reference area division provided by the embodiment of the present invention. For example, every 15 parking spaces is a reference area. If there are 120 parking spaces in the H area, the H area is shared 8 reference areas. According to the parking position identified in the second step, such as H050, record the reference area H4 where H050 is located.
第四步:记录离开地下车库的路线,同实施例一或二。第五步:取车路径推送。开始取车,识别用户其它模式→地库模式,即若第一处理器识别出取车电梯与进入商场电梯相同,则唤醒第二处理器向用户推送用户的停车位(识别的路段上的停车位置H050)和取车路径。通知用户的方式可以是:利用文本通知、语音通知等。The fourth step: record the route to leave the underground garage, the same as the first or second embodiment. Step 5: Push the pick-up path. Start to pick up the car, identify the user's other modes→basement mode, that is, if the first processor recognizes that the elevator to pick up the car is the same as the elevator entering the mall, the second processor will wake up and push the user's parking space to the user (recognized parking on the road section) Location H050) and pick up route. The way to notify the user can be: text notification, voice notification, etc.
若识别出取车电梯与进入商场电梯不同,则根据采集路段上的多个图像时得到的参考区域H4,以及进入地库的电梯4,通过服务器获取电梯4到H4的最佳路径:O-J-I-H,如 图12所示,图12为本发明实施例提供的取车路径(如包括导航路线和导航辅助信息)推送示意图;推送用户停车位H050,推送最佳路径O-J-I-H;若未识别出电梯,则推送用户的停车位,以及推送进入时所乘电梯号,推送取车路径。If it is recognized that the elevator to take the car is different from the elevator entering the mall, then according to the reference area H4 obtained when multiple images on the road section are collected, and the elevator 4 entering the basement, the server obtains the best path from elevator 4 to H4: OJIH, As shown in Fig. 12, Fig. 12 is a schematic diagram of pushing the pick-up route (for example, including navigation route and navigation assistance information) provided by an embodiment of the present invention; pushing the user's parking space H050, pushing the best route OJIH; if the elevator is not recognized, then Push the user's parking space, push the elevator number when entering, push the pick-up path.
取车路径的获取方式,可利用各地库通过众采数据,得到每个路线参考点到每部电梯口的最优路径。其中,使用计步器判断步数最少的路径为最佳路径,或者根据关键节点数最少为最佳路径。用户进入地库,自动下载最优路径的线路库。The way to obtain the pick-up path can use the data collected from the local database to obtain the optimal path from each route reference point to each elevator entrance. Among them, the pedometer is used to determine the path with the least number of steps as the best path, or the path with the least number of key nodes is the best path. The user enters the basement and automatically downloads the route library of the optimal path.
基于图1、图6A和图6B中的导航装置10的结构,图13是本发明实施例提供的一种神经网络处理器硬件结构图,其中,NPU的核心部分为运算电路1403,通过控制器1404控制运算电路1403提取存储器103中的矩阵数据并进行乘法运算。在一些实现中,运算电路1403内部包括多个处理单元(Process Engine,PE)。在一些实现中,运算电路1403是二维脉动阵列。运算电路1403还可以是一维脉动阵列或者能够执行例如乘法和加法这样的数学运算的其它电子线路。在一些实现中,运算电路1403是通用的矩阵处理器。Based on the structure of the navigation device 10 in FIG. 1, FIG. 6A and FIG. 6B, FIG. 13 is a hardware structure diagram of a neural network processor provided by an embodiment of the present invention, in which the core part of the NPU is the arithmetic circuit 1403, and the controller 1404 controls the arithmetic circuit 1403 to extract matrix data in the memory 103 and perform multiplication operations. In some implementations, the arithmetic circuit 1403 includes multiple processing units (Process Engine, PE). In some implementations, the arithmetic circuit 1403 is a two-dimensional systolic array. The arithmetic circuit 1403 may also be a one-dimensional systolic array or other electronic circuit capable of performing mathematical operations such as multiplication and addition. In some implementations, the arithmetic circuit 1403 is a general-purpose matrix processor.
举例来说,假设有输入矩阵A,权重矩阵B,输出矩阵C。运算电路1403从权重存储器1402中取矩阵B相应的权重数据,并缓存在运算电路1403中每一个PE上。运算电路1403从输入存储器1401中取矩阵A数据与矩阵B进行矩阵运算,得到的矩阵的部分结果或最终结果,保存在累加器1408accumulator中。For example, suppose there is an input matrix A, a weight matrix B, and an output matrix C. The arithmetic circuit 1403 fetches the weight data corresponding to matrix B from the weight memory 1402 and caches it on each PE in the arithmetic circuit 1403. The arithmetic circuit 1403 fetches the matrix A data and matrix B from the input memory 1401 to perform matrix operations, and the partial result or final result of the obtained matrix is stored in the accumulator 1408.
统一存储器1406用于存放输入数据以及输出数据。权重数据直接通过存储单元访问控制器(Direct Memory Access Controller,DMAC)1405被搬运到权重存储器1402中。输入数据也通过DMAC1405被搬运到统一存储器1406中。即总线接口单元(Bus Interface Unit,BIU)1410,用于实现先进可扩展接口(Advanced eXtensible Interface,AXI)总线与DMAC1405或取指存储器(Instruction Fetch Buffer)1409之间的交互。总线接口单元1410,用于取指存储器1409从外部存储器103获取指令,还用于存储单元访问控制器1405从外部存储器103获取输入矩阵A或者权重矩阵B的原数据。DMAC1405主要用于将外部存储器103,例如双倍速率同步动态随机存取存储器(DDR)中的输入数据搬运到统一存储器1406或将权重数据搬运到权重存储器1402中或将输入数据数据搬运到输入存储器1401中。The unified memory 1406 is used to store input data and output data. The weight data is directly transferred to the weight memory 1402 through the direct memory access controller (DMAC) 1405. The input data is also transferred to the unified memory 1406 through the DMAC1405. That is, the Bus Interface Unit (BIU) 1410 is used to realize the interaction between the Advanced Extensible Interface (AXI) bus and the DMAC 1405 or the instruction fetch buffer (Instruction Fetch Buffer) 1409. The bus interface unit 1410 is used for the instruction fetch memory 1409 to obtain instructions from the external memory 103, and is also used for the storage unit access controller 1405 to obtain the original data of the input matrix A or the weight matrix B from the external memory 103. DMAC1405 is mainly used to transfer input data from external memory 103, such as double-rate synchronous dynamic random access memory (DDR) to unified memory 1406 or to transfer weight data to weight memory 1402 or to transfer input data to input memory In 1401.
向量计算单元1407包括多个运算处理单元,在需要的情况下,对运算电路的输出做进一步处理,如向量乘,向量加,指数运算,对数运算,大小比较等等。主要用于神经网络中非卷积/全连接层(fully connected layer,FC)网络计算,如Pooling(池化),Batch Normalization(批归一化),Local Response Normalization(局部响应归一化)等。在一些实现中,向量计算单元1407能将经处理的输出的向量存储到统一缓存器1406。例如,向量计算单元1407可以将非线性函数应用到运算电路1403的输出,例如累加值的向量,用以生成激活值。在一些实现中,向量计算单元1407生成归一化的值、合并值,或二者均有。在一些实现中,处理过的输出的向量能够用作到运算电路1403的激活输入,例如用于在神经网络中的后续层中的使用。The vector calculation unit 1407 includes a plurality of arithmetic processing units, and if necessary, further processes the output of the arithmetic circuit, such as vector multiplication, vector addition, exponential operation, logarithmic operation, size comparison and so on. Mainly used for non-convolutional/fully connected layer (FC) network calculations in neural networks, such as Pooling, Batch Normalization, Local Response Normalization, etc. . In some implementations, the vector calculation unit 1407 can store the processed output vector to the unified buffer 1406. For example, the vector calculation unit 1407 may apply a nonlinear function to the output of the arithmetic circuit 1403, such as a vector of accumulated values, to generate an activation value. In some implementations, the vector calculation unit 1407 generates a normalized value, a combined value, or both. In some implementations, the processed output vector can be used as an activation input to the arithmetic circuit 1403, for example for use in subsequent layers in a neural network.
控制器1404连接的取指存储器(instruction fetch buffer)1409,用于存储存储控制器1404使用的指令;统一存储器1406,输入存储器1401,权重存储器1402以及取指存储器1409均为On-Chip存储器。可以理解的是,本申请中所述的关于所述路段上的多个图像的 识别等相关功能,均由上述NPU中相关的功能单元进行实现,在此不再赘述。The instruction fetch buffer 1409 connected to the controller 1404 is used to store instructions used by the storage controller 1404; the unified memory 1406, the input memory 1401, the weight memory 1402, and the instruction fetch memory 1409 are all On-Chip memories. It is understandable that the relevant functions such as the recognition of multiple images on the road section described in this application are all implemented by the relevant functional units in the above-mentioned NPU, and will not be repeated here.
请参见图14,图14是本发明实施例提供的一种导航方法的流程示意图,该导航方法,适用于上述图1和图6A和图6B中的任意一种导航装置以及包含所述导航装置的设备。该方法可以包括以下步骤S201-步骤S203,可选的,还可以包括步骤S204-步骤S205。其中,步骤S201:获取路段上的多个图像,其中,所述导航装置沿所述路段运动,且所述路段包括导航目标位置;步骤S202:识别所述多个图像以得到多个标志,每个标志用于标记所述路段上一个位置;步骤S203:根据所述多个标志,确定所述路段中的多个路线参考点,并基于所述多个路线参考点生成所述路段的导航路线,所述导航路线用于引导用户去往所述导航目标位置。Please refer to FIG. 14, which is a schematic flowchart of a navigation method provided by an embodiment of the present invention. The navigation method is applicable to any one of the navigation devices in FIGS. 1 and 6A and 6B and includes the navigation device. device of. The method may include the following steps S201-S203, and optionally, may also include step S204-step S205. Wherein, step S201: acquiring multiple images on a road section, wherein the navigation device moves along the road section, and the road section includes a navigation target position; step S202: identifying the multiple images to obtain multiple signs, each A mark is used to mark a position on the road section; step S203: according to the multiple signs, determine multiple route reference points in the road section, and generate a navigation route for the road section based on the multiple route reference points , The navigation route is used to guide the user to the navigation target location.
在一种可能的实现方式中,所述多个标志是M个标志,所述多个路线参考点是所述M个标志中的N个标志,N小于或等于M,且N和M为大于1的整数,所述导航路线包括所述N个标志。在一种可能的实现方式中,所述多个标志是M个标志,所述多个路线参考点是L个参考区域,L小于或等于M,且N和M位大于1的整数,所述导航路线包括所述L个参考区域,每个参考区域包括至少一个标志。In a possible implementation, the multiple signs are M signs, the multiple route reference points are N signs in the M signs, N is less than or equal to M, and N and M are greater than An integer of 1, the navigation route includes the N signs. In a possible implementation manner, the multiple signs are M signs, the multiple route reference points are L reference areas, L is less than or equal to M, and N and M are integers greater than 1, the The navigation route includes the L reference areas, and each reference area includes at least one sign.
步骤S204:测量所述导航装置在所述多个路线参考点中任意两个相邻路线参考点之间运动的方向和步数;步骤S205:根据所述方向和步数生成所述导航路线的导航辅助信息。Step S204: Measure the direction and the number of steps the navigation device moves between any two adjacent route reference points among the multiple route reference points; Step S205: Generate the navigation route according to the direction and the number of steps Navigation assistance information.
在一种可能的实现方式中,所述导航路线还包括至少一个路线参考点对应的时间戳。在一种可能的实现方式中,所述方法还包括:将所述导航路线推送给所述用户。在一种可能的实现方式中,接收车载系统发送的指示信息,所述指示信息用于指示所述车载系统到达了所述导航目标位置;所述导航目标位置位于所述路段的起始位置,即装置沿所述路段云端的时候,开始采集图像和记录的起始位置;根据所述指示信息触发执行所述获取路段上的多个图像。在一种可能的实现方式中,所述路段还包括导航起始位置。需要说明的是,本发明实施例中所描述的导航方法中的具体流程,可参见上述图1-图13中所述的发明实施例中的相关描述,此处不再赘述。In a possible implementation manner, the navigation route further includes a time stamp corresponding to at least one route reference point. In a possible implementation manner, the method further includes: pushing the navigation route to the user. In a possible implementation manner, receiving instruction information sent by an in-vehicle system, where the instruction information is used to indicate that the in-vehicle system has reached the navigation target position; the navigation target position is located at the starting position of the road section, That is, when the device is along the cloud on the road section, it starts to collect images and record the starting position; according to the instruction information, trigger execution of the acquisition of multiple images on the road section. In a possible implementation manner, the road section further includes a navigation start position. It should be noted that, for the specific process in the navigation method described in the embodiment of the present invention, please refer to the relevant description in the embodiment of the invention described in Figs. 1 to 13, and will not be repeated here.
请参见图15,图15是本发明实施例提供的又一种导航装置的结构示意图,该导航装置30可包括获取单元301、识别单元302、导航单元303。可选的,还可以包括测量单元304、辅助单元305、推送单元306、指示单元307、触发单元308,其中,获取单元301,用于获取路段上的多个图像;识别单元302,用于识别所述多个图像以得到多个标志,每个标志用于标记所述路段上一个位置;导航单元303,用于根据所述多个标志,确定所述路段中的多个路线参考点,并基于所述多个路线参考点生成所述路段的导航路线,所述导航路线用于引导用户去往所述导航目标位置。Please refer to FIG. 15. FIG. 15 is a schematic structural diagram of another navigation device provided by an embodiment of the present invention. The navigation device 30 may include an acquisition unit 301, an identification unit 302, and a navigation unit 303. Optionally, it may also include a measuring unit 304, an auxiliary unit 305, a pushing unit 306, an indicating unit 307, and a triggering unit 308. The acquiring unit 301 is used to acquire multiple images on the road section; and the identifying unit 302 is used to identify The multiple images are used to obtain multiple signs, and each sign is used to mark a position on the road section; the navigation unit 303 is configured to determine multiple route reference points in the road section according to the multiple signs, and A navigation route of the road segment is generated based on the multiple route reference points, and the navigation route is used to guide the user to the navigation target location.
在一种可能的实现方式中,装置30还包括:测量单元304,用于测量所述导航装置在所述多个路线参考点中任意两个相邻路线参考点之间运动的方向和步数;辅助单元305,用于根据所述方向和步数生成所述导航路线的导航辅助信息。In a possible implementation manner, the device 30 further includes: a measuring unit 304, configured to measure the movement direction and the number of steps of the navigation device between any two adjacent route reference points among the multiple route reference points ; Auxiliary unit 305 for generating navigation assistance information of the navigation route according to the direction and the number of steps.
在一种可能的实现方式中,所述多个标志是M个标志,所述多个路线参考点是所述M个标志中的N个标志,N小于或等于M,且N和M为大于1的整数,所述导航路线包括 所述N个标志。在另一种可能的实现方式中,所述多个标志是M个标志,所述多个路线参考点是L个参考区域,L小于或等于M,且N和M位大于1的整数,所述导航路线包括所述L个参考区域,每个参考区域包括至少一个标志。In a possible implementation, the multiple signs are M signs, the multiple route reference points are N signs in the M signs, N is less than or equal to M, and N and M are greater than An integer of 1, the navigation route includes the N signs. In another possible implementation manner, the multiple signs are M signs, the multiple route reference points are L reference areas, L is less than or equal to M, and the N and M bits are integers greater than 1, so The navigation route includes the L reference areas, and each reference area includes at least one sign.
在一种可能的实现方式中,所述导航路线还包括至少一个路线参考点对应的时间戳。在一种可能的实现方式中,所述装置还包括:推送单元306,用于将所述导航路线推送给所述用户。在一种可能的实现方式中,装置30还包括:指示单元307,用于接收车载系统发送的指示信息,所述指示信息用于指示所述车载系统到达了所述导航目标位置;所述导航目标位置位于所述路段的起始位置;触发单元308,用于根据所述指示信息触发执行所述获取路段上的多个图像。在一种可能的实现方式中,所述路段还包括导航起始位置。In a possible implementation manner, the navigation route further includes a time stamp corresponding to at least one route reference point. In a possible implementation manner, the device further includes: a pushing unit 306, configured to push the navigation route to the user. In a possible implementation manner, the device 30 further includes: an indication unit 307, configured to receive indication information sent by an on-board system, the indication information being used to indicate that the on-board system has reached the navigation target location; The target position is located at the start position of the road section; the trigger unit 308 is configured to trigger the execution of the acquisition of multiple images on the road section according to the indication information. In a possible implementation manner, the road section further includes a navigation start position.
需要说明的是,本发明实施例中所描述的导航装置30中相关单元的功能可参见上述图1-图13中所述的相关装置实施例,以及图14中所述的方法实施例中的相关描述,此处不再赘述。图15中每个单元可以以软件、硬件、或其结合实现。以硬件实现的单元可以包括路及电炉、算法电路或模拟电路等。以软件实现的单元可以包括程序指令,被视为是一种软件产品,被存储于存储器中,并可以被处理器运行以实现相关功能,具体参见之前的介绍。It should be noted that the functions of the relevant units in the navigation device 30 described in the embodiments of the present invention can be referred to the relevant device embodiments described in Figures 1 to 13 and the method embodiments described in Figure 14 Related descriptions will not be repeated here. Each unit in FIG. 15 can be implemented by software, hardware, or a combination thereof. The hardware-implemented units can include circuits and electric furnaces, algorithm circuits or analog circuits, etc. A unit implemented in software may include program instructions, which is regarded as a software product, is stored in a memory, and can be run by a processor to implement related functions. For details, refer to the previous introduction.
本发明实施例还提供一种计算机存储介质,其中,该计算机存储介质可存储有程序,该程序执行时包括上述方法实施例中记载的任意一种的部分或全部步骤。An embodiment of the present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and the program includes part or all of the steps of any one of the above method embodiments when executed.
本发明实施例还提供一种计算机程序,该计算机程序包括指令,当该计算机程序被计算机执行时,使得计算机可以执行任意一种导航方法的部分或全部步骤。The embodiment of the present invention further provides a computer program, which includes instructions, when the computer program is executed by a computer, the computer can execute part or all of the steps of any navigation method.
在上述实施例中,对各个实施例的描述都各有侧重,某个实施例中没有详述的部分,可以参见其它实施例的相关描述。In the above-mentioned embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in an embodiment, reference may be made to related descriptions of other embodiments.
需要说明的是,对于前述的各方法实施例,为了简单描述,故将其都表述为一系列的动作组合,但是本领域技术人员应该知悉,本申请并不受所描述的动作顺序的限制,因为依据本申请,某些步骤可能可以采用其它顺序或者同时进行。其次,本领域技术人员也应该知悉,说明书中所描述的实施例均属于优选实施例,所涉及的动作和模块并不一定是本申请所必须的。It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited by the described sequence of actions. Because according to this application, some steps may be performed in other order or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.
在本申请所提供的几个实施例中,应该理解到,所揭露的装置,可通过其它的方式实现。例如,以上所描述的装置实施例仅仅是示意性的,例如上述单元的划分,仅仅为一种逻辑功能划分,实际实现时可以有另外的划分方式,例如多个单元或组件可以结合或者可以集成到另一个系统,或一些特征可以忽略,或不执行。另一点,所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口,装置或单元的间接耦合或通信连接,可以是电性或其它的形式。In the several embodiments provided in this application, it should be understood that the disclosed device may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the above-mentioned units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components can be combined or integrated. To another system, or some features can be ignored or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical or other forms.
上述作为分离部件说明的单元可以是或者也可以不是物理上分开的,作为单元显示的部件可以是或者也可以不是物理单元,即可以位于一个地方,或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
另外,在本申请各实施例中的各功能单元可以集成在一个处理单元中,也可以是各个单元单独物理存在,也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可 以采用硬件的形式实现,也可以采用软件功能单元的形式实现。In addition, the functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above-mentioned integrated unit can be realized in the form of hardware or software functional unit.
上述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时,可以存储在一个计算机可读取存储介质中。基于这样的理解,本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来,该计算机软件产品存储在一个存储介质中,包括若干指令用以使得一台计算机设备(可以为个人计算机、服务器或者网络设备等,具体可以是计算机设备中的处理器)执行本申请各个实施例上述方法的全部或部分步骤。其中,而前述的存储介质可包括:U盘、移动硬盘、磁碟、光盘、只读存储器(Read-Only Memory,缩写:ROM)或者随机存取存储器(Random Access Memory,缩写:RAM)等各种可以存储程序代码的介质。If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solution of the present application essentially or the part that contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , Including several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc., specifically a processor in a computer device) execute all or part of the steps of the above methods in the various embodiments of the present application. Among them, the aforementioned storage media may include: U disk, mobile hard disk, magnetic disk, optical disk, read-only memory (Read-Only Memory, abbreviation: ROM) or Random Access Memory (Random Access Memory, abbreviation: RAM), etc. A medium that can store program codes.
以上所述,以上实施例仅用以说明本申请的技术方案,而非对其限制;尽管参照前述实施例对本申请进行了详细的说明,本领域的普通技术人员应当理解:其依然可以对前述各实施例所记载的技术方案进行修改,或者对其中部分技术特征进行等同替换;而这些修改或者替换,并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围。As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, a person of ordinary skill in the art should understand that: The technical solutions recorded in the embodiments are modified, or some of the technical features are equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (18)
- 一种导航装置,其特征在于,包括:第一处理器,以及耦合于所述第一处理器的神经网络处理器,其中A navigation device, characterized by comprising: a first processor, and a neural network processor coupled to the first processor, wherein所述第一处理器,用于获取由摄像模块采集的路段上的多个图像,指示所述神经网络处理器对所述多个图像进行识别,其中,所述导航装置沿所述路段运动,且所述路段包括导航目标位置;The first processor is configured to acquire multiple images on the road section collected by the camera module, and instruct the neural network processor to recognize the multiple images, wherein the navigation device moves along the road section, And the road section includes a navigation target position;所述神经网络处理器,用于识别所述多个图像以得到多个标志,每个标志用于标记所述路段上一个位置;The neural network processor is configured to recognize the multiple images to obtain multiple signs, and each sign is used to mark a position on the road section;所述第一处理器,还用于根据所述多个标志,确定所述路段中的多个路线参考点,并基于所述多个路线参考点生成所述路段的导航路线,所述导航路线用于引导用户去往所述导航目标位置。The first processor is further configured to determine multiple route reference points in the road section according to the multiple signs, and generate a navigation route of the road section based on the multiple route reference points, the navigation route Used to guide the user to the navigation target location.
- 根据权利要求1所述的装置,其特征在于,所述装置还包括:所述摄像模块,用于采集所述路段上的所述多个图像。The device according to claim 1, wherein the device further comprises: the camera module, configured to collect the multiple images on the road section.
- 根据权利要求1或2所述的装置,其特征在于,所述装置还包括耦合于所述第一处理器和所述摄像模块的存储器;The device according to claim 1 or 2, wherein the device further comprises a memory coupled to the first processor and the camera module;所述存储器,用于存储所述摄像模块采集的所述多个图像。The memory is used to store the multiple images collected by the camera module.
- 根据权利要求1-3任意一项所述的装置,其特征在于,所述装置还包括耦合于所述第一处理器的测量模块:The device according to any one of claims 1 to 3, wherein the device further comprises a measurement module coupled to the first processor:所述测量模块,用于测量所述导航装置在所述多个路线参考点中任意两个相邻路线参考点之间运动的方向和步数;The measurement module is used to measure the direction and the number of steps the navigation device moves between any two adjacent route reference points among the multiple route reference points;所述第一处理器,还用于根据所述方向和步数生成所述导航路线的导航辅助信息。The first processor is further configured to generate navigation assistance information of the navigation route according to the direction and the number of steps.
- 根据权利要求1-4任意一项所述的装置,其特征在于,所述多个标志是M个标志,所述多个路线参考点是所述M个标志中的N个标志,N小于或等于M,且N和M为大于1的整数,所述导航路线包括所述N个标志。The device according to any one of claims 1 to 4, wherein the multiple signs are M signs, the multiple route reference points are N signs in the M signs, and N is less than or Equal to M, and N and M are integers greater than 1, and the navigation route includes the N signs.
- 根据权利要求1-4任意一项所述的装置,其特征在于,所述多个标志是M个标志,所述多个路线参考点是L个参考区域,L小于或等于M,且N和M位大于1的整数,所述导航路线包括所述L个参考区域,每个参考区域包括至少一个标志。The device according to any one of claims 1-4, wherein the multiple signs are M signs, the multiple route reference points are L reference areas, L is less than or equal to M, and N and M bits are an integer greater than 1, the navigation route includes the L reference areas, and each reference area includes at least one sign.
- 根据权利要求5或6所述的装置,所述导航路线还包括至少一个路线参考点对应的时间戳。The device according to claim 5 or 6, wherein the navigation route further includes a time stamp corresponding to at least one route reference point.
- 根据权利要求1-7任意一项所述的装置,其特征在于,所述装置还包括耦合于所述 第一处理器的第二处理器;The device according to any one of claims 1-7, wherein the device further comprises a second processor coupled to the first processor;所述第一处理器还用于,将所述导航路线发送至所述第二处理器;The first processor is further configured to send the navigation route to the second processor;所述第二处理器,还用于将所述导航路线推送给所述用户。The second processor is further configured to push the navigation route to the user.
- 根据权利要求8所述的装置,其特征在于,所述第二处理器,还用于:The device according to claim 8, wherein the second processor is further configured to:接收车载系统发送的指示信息,所述指示信息用于指示所述车载系统到达了所述导航目标位置;所述导航目标位置位于所述路段的起始位置;Receiving instruction information sent by an in-vehicle system, where the instruction information is used to indicate that the in-vehicle system has reached the navigation target position; the navigation target position is located at the start position of the road section;根据所述指示信息唤醒所述第一处理器以获取由所述摄像模块采集的所述路段上的所述多个图像。Wake up the first processor according to the instruction information to acquire the multiple images on the road section collected by the camera module.
- 一种导航方法,其特征在于,应用于导航装置,所述方法包括:A navigation method, characterized by being applied to a navigation device, the method comprising:获取路段上的多个图像,其中,所述导航装置沿所述路段运动,且所述路段包括导航目标位置;Acquiring multiple images on a road section, wherein the navigation device moves along the road section, and the road section includes a navigation target position;识别所述多个图像以得到多个标志,每个标志用于标记所述路段上一个位置;Identifying the multiple images to obtain multiple signs, each sign being used to mark a position on the road section;根据所述多个标志,确定所述路段中的多个路线参考点,并基于所述多个路线参考点生成所述路段的导航路线,所述导航路线用于引导用户去往所述导航目标位置。According to the multiple signs, determine multiple route reference points in the road segment, and generate a navigation route of the road segment based on the multiple route reference points, the navigation route is used to guide the user to the navigation target position.
- 根据权利要求10所述的方法,其特征在于,所述方法还包括:The method of claim 10, wherein the method further comprises:测量所述导航装置在所述多个路线参考点中任意两个相邻路线参考点之间运动的方向和步数;Measuring the moving direction and the number of steps of the navigation device between any two adjacent route reference points among the multiple route reference points;根据所述方向和步数生成所述导航路线的导航辅助信息。The navigation assistance information of the navigation route is generated according to the direction and the number of steps.
- 根据权利要求10或11所述的方法,其特征在于,所述多个标志是M个标志,所述多个路线参考点是所述M个标志中的N个标志,N小于或等于M,且N和M为大于1的整数,所述导航路线包括所述N个标志。The method according to claim 10 or 11, wherein the multiple signs are M signs, the multiple route reference points are N signs in the M signs, and N is less than or equal to M, And N and M are integers greater than 1, and the navigation route includes the N signs.
- 根据权利要求10或11所述的方法,其特征在于,所述多个标志是M个标志,所述多个路线参考点是L个参考区域,L小于或等于M,且N和M位大于1的整数,所述导航路线包括所述L个参考区域,每个参考区域包括至少一个标志。The method according to claim 10 or 11, wherein the multiple signs are M signs, the multiple route reference points are L reference areas, L is less than or equal to M, and N and M bits are greater than An integer of 1, the navigation route includes the L reference areas, and each reference area includes at least one sign.
- 根据权利要求12或13所述的方法,其特征在于,所述导航路线还包括至少一个路线参考点对应的时间戳。The method according to claim 12 or 13, wherein the navigation route further includes a time stamp corresponding to at least one route reference point.
- 根据权利要求10-14任意一项所述的方法,其特征在于,所述方法还包括:The method according to any one of claims 10-14, wherein the method further comprises:将所述导航路线推送给所述用户。Push the navigation route to the user.
- 根据权利要求10-15任意一项所述的方法,其特征在于,所述方法还包括:The method according to any one of claims 10-15, wherein the method further comprises:接收车载系统发送的指示信息,所述指示信息用于指示所述车载系统到达了所述导航 目标位置;所述导航目标位置位于所述路段的起始位置;Receiving instruction information sent by an in-vehicle system, where the instruction information is used to indicate that the in-vehicle system has reached the navigation target position; the navigation target position is located at the start position of the road section;根据所述指示信息触发执行所述获取路段上的多个图像。Triggering the execution of the acquiring of multiple images on the road section according to the instruction information.
- 一种计算机存储介质,其特征在于,所述计算机存储介质存储有计算机程序,该计算机程序被处理器执行时实现上述权利要求10-16任意一项所述的方法。A computer storage medium, characterized in that the computer storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 10-16 is implemented.
- 一种计算机程序,其特征在于,所述计算机程序包括指令,当所述计算机程序被计算机执行时,使得所述计算机执行如权利要求10-16中任意一项所述的方法。A computer program, characterized in that the computer program includes instructions, which when the computer program is executed by a computer, cause the computer to execute the method according to any one of claims 10-16.
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