CN113147738A

CN113147738A - Automatic parking positioning method and device

Info

Publication number: CN113147738A
Application number: CN202110218257.4A
Authority: CN
Inventors: 彭国旗; 张放; 李晓飞; 张德兆; 王肖; 霍舒豪
Original assignee: Chongqing Zhixing Information Technology Co Ltd
Current assignee: Wuhan Zhixing Technology Co ltd
Priority date: 2021-02-26
Filing date: 2021-02-26
Publication date: 2021-07-23

Abstract

The embodiment of the invention relates to an automatic parking positioning method, which comprises the steps of obtaining acceleration information and angular speed information of a vehicle, obtaining wheel speed information of the vehicle, and obtaining first position information and first course information of the vehicle under a first coordinate system; acquiring point cloud data of the vehicle in a second coordinate system to obtain second position information and second course information; the first position information, the first course information, the second position information and the second course information are subjected to fusion processing through a first algorithm to obtain first position and attitude information of the vehicle in a first coordinate system; acquiring image data of a parking space to be parked, and constructing an equation; determining coordinates of each corner point of the parking space in a third coordinate system, and determining second position and attitude information of the vehicle in the third coordinate system; converting the first posture information of the first coordinate system into third posture information of a third coordinate system; and performing fusion processing on the second attitude information and the third attitude information to determine target position information and target course information of the vehicle.

Description

Automatic parking positioning method and device

Technical Field

The invention relates to the technical field of automatic driving, in particular to an automatic parking positioning method and device.

Background

In the automatic driving technology, an indoor parking lot is an unavoidable place with respect to automatic parking. In an indoor environment, a global positioning system has weak or no signal, cannot provide accurate position information for an automobile, and other technologies, such as ultrasonic radar, monitoring and the like, have insufficient accuracy and stability in the indoor environment.

The existing automatic driving and parking system mostly adopts an ultrasonic radar or a camera to detect the surrounding environment information. In a single detection mode, the ultrasonic radar can only identify the position information of the obstacle, needs to manually participate in identifying the type of the obstacle, and judges whether the obstacle is a parking position; the camera can identify the parking space line, but is greatly influenced by the environment, and the identification effect is often influenced to a certain extent under the condition that the illumination condition is not good or the parking space line is shielded.

The indoor positioning technologies include a bluetooth indoor positioning technology, an infrared indoor positioning technology, an Ultra Wide Band (UWB) indoor positioning technology, a WiFi indoor positioning technology, and the like, but these technologies are inconvenient to use or have high cost, and cannot meet the positioning requirements of mass-production automatic parking. Therefore, a positioning method with higher robustness and precision is urgently needed for automatic parking.

Disclosure of Invention

The invention aims to provide an automatic parking positioning method aiming at the defects in the prior art, and the method can realize indoor accurate navigation positioning by processing and calculating data acquired by sensors such as a laser radar, an image acquisition device, an IMU (inertial measurement Unit), a wheel speed meter and the like.

In order to achieve the above object, a first aspect of the embodiments of the present invention provides an automatic parking positioning method, which is characterized by comprising:

acquiring acceleration information and angular velocity information of a vehicle detected by a vehicle-mounted inertial measurement unit IMU in real time;

acquiring wheel speed information of a vehicle detected by a wheel speed meter in real time;

performing strapdown calculation on the acceleration information, the angular velocity information and the wheel speed information to obtain first position information and first course information of the vehicle in a first coordinate system;

acquiring point cloud data of a vehicle detected by the vehicle-mounted laser radar under a second coordinate system in real time; performing first processing on the point cloud data to obtain second position information and second course information;

the first position information, the first course information, the second position information and the second course information are subjected to fusion processing through a first algorithm to obtain first position and attitude information of the vehicle in a first coordinate system;

acquiring image data of a parking space to be parked, which is acquired by an image acquisition device on a vehicle;

hough processing is carried out on the image data, and an equation corresponding to the parking space mark line of the parking space to be parked is constructed;

according to the equation, determining the coordinates of each corner point of the parking space in a third coordinate system, and determining second position and attitude information of the vehicle in the third coordinate system; the third coordinate system is a coordinate system which is constructed by taking a first angular point of a plurality of angular points of the parking space as an origin;

converting the first posture information of the first coordinate system into third posture information of a third coordinate system;

and performing fusion processing on the second attitude information and the third attitude information to determine target position information and target course information of the vehicle.

Preferably, before acquiring the acceleration information and the angular velocity information of the vehicle detected by the vehicle-mounted inertial measurement unit IMU in real time, the method further includes:

acquiring initial acceleration information of the vehicle detected by the IMU within preset time, and calculating to obtain an initial pitch angle and an initial roll angle of the vehicle according to the initial acceleration information;

acquiring laser point cloud data around a parking space detected by a vehicle-mounted laser radar on a vehicle;

constructing a laser point cloud map according to the laser point cloud data;

and determining the initial position of the vehicle and the zero offset of the IMU according to the initial pitch angle, the initial roll angle and the laser point cloud map of the vehicle.

Further preferably, before the image data of the parking space acquired by the image acquisition device on the vehicle is acquired, the method further includes:

calculating the distance from the real-time position to the parking space according to the real-time position of the vehicle;

and when the distance is not greater than the preset distance threshold, acquiring image data of the parking space acquired by an image acquisition device on the vehicle.

Preferably, the Hough processing is performed on the image data, and the building of an equation corresponding to the parking space marker line of the parking space specifically includes:

carrying out graying, filtering, binaryzation and correction processing on the image data to obtain processed image data;

carrying out Hough transformation on the processed image data to obtain a parking space marking line;

and establishing an equation according to the parking space sign line.

Preferably, the method further comprises:

performing second processing on the point cloud data to obtain Gaussian distribution of the point cloud; the Gaussian distribution comprises a mean and a variance;

comparing the mean with a preset mean threshold and the variance with a preset variance threshold;

when the mean value is not less than a preset mean value threshold value within a preset time length and the variance is not less than a preset variance threshold value, the first position information, the first heading information, the second position information and the second heading information are subjected to fusion processing through a first algorithm to obtain first heading information of the vehicle in a first coordinate system;

and when the mean value is smaller than a preset mean value threshold value and the variance is smaller than a preset variance threshold value within a preset time length, acquiring image data of a parking space to be parked, which is acquired by an image acquisition device on the vehicle.

A second aspect of the embodiments of the present invention provides an automatic parking positioning device, including:

the processing module is used for acquiring the acceleration information and the angular velocity information of the vehicle detected by the vehicle-mounted inertial measurement unit IMU in real time;

the first position information, the first course information, the second position information and the second course information are subjected to fusion processing through a first algorithm to obtain first position and attitude information of the vehicle in a first coordinate system; acquiring image data of a parking space to be parked, which is acquired by an image acquisition device on a vehicle;

according to the equation, determining the coordinates of each corner point of the parking space in a third coordinate system, and determining second position and attitude information of the vehicle in the third coordinate system; the third coordinate system is a coordinate system which is constructed by taking a first angular point of a plurality of angular points of the parking space as an origin; converting the first posture information of the first coordinate system into third posture information of a third coordinate system;

A third aspect of an embodiment of the present invention provides an electronic device, including: a memory, a processor, and a transceiver;

the processor is configured to be coupled to the memory, read and execute instructions in the memory, so as to implement the method steps of the first aspect;

the transceiver is coupled to the processor, and the processor controls the transceiver to transmit and receive messages.

A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a computer, cause the computer to perform the method of the first aspect.

The method comprises the steps of carrying out fusion processing on data detected by a vehicle-mounted laser radar, an inertia measuring unit and a wheel speed meter to obtain high-precision absolute positioning of a vehicle, then acquiring image data near a parking space through an image acquisition device, processing the image data, combining the data measured by the inertia measuring unit and the wheel speed meter to further calculate the position and pose of the vehicle relative to a parking space mark line, and determining target position information and target course information of the vehicle based on a relative position relationship, so that the relative positioning of the vehicle is realized.

Drawings

Fig. 1 illustrates an automatic parking positioning method according to an embodiment of the present invention;

fig. 2 is a block diagram of an automatic parking positioning device according to a second embodiment of the present invention;

fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.

Detailed Description

The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be further noted that, for the convenience of description, only the portions related to the present invention are shown in the drawings.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

Fig. 1 is a schematic flow chart of an automatic parking positioning method according to an embodiment of the present invention, where the method is applied to a terminal equipped with a laser radar, an Inertial Measurement Unit (IMU), a wheel speed meter, an image acquisition device, and the like, such as an unmanned vehicle equipped with the above devices, and an execution subject of the present application is a terminal, a server, or a processor with a computing function. The present application will be described by taking an example of applying the method to an unmanned Vehicle, and when the method is applied to an unmanned Vehicle, an execution subject of the method is an Automated Vehicle Control Unit (AVCU), that is, a central processing Unit of the unmanned Vehicle corresponds to a "brain" of the unmanned Vehicle. As shown in fig. 1, the present application includes the steps of:

step 101, acquiring acceleration information and angular velocity information of a vehicle detected by a vehicle-mounted inertial measurement unit IMU in real time;

specifically, the vehicle is provided with a laser radar, an Inertial Measurement Unit (IMU), a wheel speed meter, a camera, and other devices. An inertial measurement unit is a device that measures the three-axis angular velocity (or angular rate) and acceleration of an object. Generally, an IMU includes three single-axis accelerometers and three single-axis gyroscopes, the accelerometers detecting acceleration information of an object in three independent axes of a carrier coordinate system, and the gyroscopes detecting angular velocity information of the carrier relative to a navigation coordinate system.

Wherein before step 101, vehicle pose initialization is required, and the detailed process of pose initialization is described below.

In one example, first, initial acceleration information of the vehicle detected by the IMU within a preset time is acquired, and an initial pitch angle and an initial roll angle of the vehicle are calculated according to the initial acceleration information.

Specifically, an accelerometer in the IMU outputs acceleration values of xyz three axes, the ground plane is used as a reference, the z axis is a gravity acceleration value under the condition that the vehicle is completely horizontal, other axes (xy) are 0, a measurement value of the xy axis is decomposed from the gravity acceleration, so that an initial pitch angle and a roll angle of the vehicle can be obtained, and the preset time is 2S.

Secondly, acquiring laser point cloud data around the parking space detected by a vehicle-mounted laser radar on the vehicle;

specifically, laser point cloud data around the parking space can be acquired through a single-line or multi-line laser radar.

Thirdly, constructing a laser point cloud map according to the laser point cloud data;

specifically, the purpose of laser point cloud map construction is to generate the entire parking path.

And finally, determining the initial position of the vehicle and the zero offset of the IMU according to the initial pitch angle, the initial roll angle and the laser point cloud map of the vehicle.

Step 102, acquiring wheel speed information of a vehicle detected by a wheel speed meter in real time;

specifically, the wheel speed meter on the vehicle can detect the wheel speed information of the vehicle in real time and upload the wheel speed information to the automatic driving vehicle control unit, so that the automatic driving vehicle control unit can conveniently process data.

103, carrying out strapdown calculation on the acceleration information, the angular speed information and the wheel speed information to obtain first position information and first course information of the vehicle in a first coordinate system;

specifically, the information measured by the accelerometer, the gyroscope and the wheel speed meter cannot be directly integrated, and each of the information includes various redundant information, and the information needs to be removed during calculation to obtain the acceleration and the angular velocity of the carrier system relative to the navigation system, and then the calculation is performed. In this embodiment, the navigation system is the first coordinate system, which can also be understood as a local horizontal coordinate system, and in this embodiment, the northeast coordinate system is adopted, that is, the initial position of the vehicle is taken as the origin, and the x axis points to the north; the y-axis points to the east; the z-axis points in the sky direction. The first position information and the first heading information are both the position and the heading of the vehicle obtained in the northeast coordinate system.

104, acquiring point cloud data of a vehicle detected by the vehicle-mounted laser radar under a second coordinate system in real time; performing first processing on the point cloud data to obtain second position information and second course information;

in particular, the second coordinate system may be understood as a lidar coordinate system. The coordinate origin of the laser radar coordinate system is the laser emission center, the x axis is forward, the y axis is leftward, and the z axis is upward. The second position information and the second heading information are the position and heading of the vehicle obtained in the lidar coordinate system.

105, performing fusion processing on the first position information, the first course information, the second position information and the second course information through a first algorithm to obtain first position and attitude information of the vehicle in a first coordinate system;

specifically, the first algorithm may be a kalman filter data fusion algorithm. The first pose information is pose information of the vehicle in a northeast coordinate system. That is to say, the pose information is obtained by fusing various data detected by the IMU, the wheel speed meter and the laser radar, and is the absolute pose of the vehicle.

In order to improve the accuracy of the absolute pose, the correction of errors is needed while Kalman filtering time distance fusion is carried out. The error is corrected by a preset error model equation.

The specific calculation of kalman wave fusion is as follows:

wherein, in the formula, A/B: system structure parameters; h: measuring a structural parameter; u: system noise; q: measuring noise; i: an identity matrix; kk: the gain at system time k; zk: a measured value at time k; pk: mean square error at time k; pk^-: predicting the mean square error at the k moment;

a predicted value at the k moment;

an estimated value at time k;

estimate of time k-1. It should be noted that, the mean square error at the time k in the formula; predicting the mean square error at the k moment; a predicted value at the k moment; an estimated value at time k; the estimated value at the time k-1 is calculated by the above formula, and the rest of the data can be directly read.

Step 106, acquiring image data of a parking space to be parked, which is acquired by an image acquisition device on a vehicle;

before step 106, the AVCU first determines whether the vehicle has reached the vicinity of the parking space.

Firstly, calculating the distance from the real-time position to a parking space to be parked according to the real-time position of the vehicle;

specifically, the vehicle automatically plans a path according to the absolute position of the vehicle in the physical world, and automatically drives to a position near the parking space according to the high-precision absolute positioning result after the path planning is finished. And sending the real-time position to an AVCU, and calculating the distance from the real-time position to the parking space by the AVCU by combining the whole parking path constructed by the laser point cloud map.

And when the distance is not greater than the preset distance threshold, acquiring image data of the parking space acquired by an image acquisition device on the vehicle. The image acquisition device may be a camera.

Step 107, Hough processing is carried out on the image data, and an equation corresponding to a parking space mark line of the parking space to be parked is constructed;

specifically, graying, filtering, binarization and correcting the image data to obtain processed image data;

carrying out Hough transformation on the processed image data to obtain a parking space marking line; it should be noted that the parking space marking line transformed by Hough is only a skeleton of the parking space marking line.

And establishing an equation according to the parking space sign line.

Specifically, a K mean value method is adopted to cluster peak points in the Hough space, so that a linear equation corresponding to the parking space marking line is obtained.

Step 108, determining coordinates of each corner point of the parking space in a third coordinate system according to an equation, and determining second position and attitude information of the vehicle in the third coordinate system; the third coordinate system is a coordinate system which is constructed by taking a first angular point of a plurality of angular points of the parking space as an origin;

specifically, each edge of the parking space can be fitted into a straight line according to the equation, the straight line with the intersection point is calculated, the coordinate of the intersection point is calculated, and the coordinate of each angular point of the parking space to be parked can be obtained. The third coordinate system can be understood as a parking space coordinate system.

Step 109, converting the first posture information of the first coordinate system into third posture information of a third coordinate system;

and step 110, carrying out fusion processing on the second attitude information and the third attitude information to determine target position information and target course information of the vehicle.

Specifically, since the first coordinate system is a northeast coordinate system and the third coordinate system is a parking space coordinate system, the first pose information of the vehicle in the first coordinate system must be converted into the pose information in the third coordinate system, and then data fusion is performed to obtain the target position and the target heading information of the vehicle. Since this process relies primarily on the image acquisition device, IMU and wheel speed meter, the positioning is relative.

In a preferred embodiment, the positioning mode is switchable. When a laser point cloud map of the whole parking path is established in the early stage, the collection path only comprises a main running path and does not comprise a garage reversing path of each parking space. Whether vehicles exist on the left side and the right side of a parking space near the parking space or not influences laser matching results when the vehicles are positioned for parking and warehousing. Therefore, it is necessary to determine whether or not the positioning result of the laser radar is reliable, and to switch the positioning mode. The method comprises the following specific steps:

performing second processing on the point cloud data to obtain Gaussian distribution of the point cloud; the gaussian distribution includes a mean and a variance;

comparing the mean value with a preset mean threshold value, and comparing the variance with a preset variance threshold value;

when the mean value is not less than a preset mean value threshold value within a preset time length and the variance is not less than a preset variance threshold value, performing fusion processing on the first position information, the first heading information, the second position information and the second heading information through a first algorithm to obtain first heading information of the vehicle in a first coordinate system; in a specific example, the preset time period is 0.5 s. That is, within 0.5s, the mean of the gaussian distribution is not less than the preset mean threshold, and the location when the variance is not less than the preset variance threshold is determined as an absolute location.

And when the mean value is smaller than a preset mean value threshold value within a preset time length and the variance is smaller than a preset variance threshold value, acquiring image data of the parking space to be parked, which is acquired by an image acquisition device on the vehicle. That is, the mean of the gaussian distribution is smaller than the preset mean threshold value within 0.5s, and the location when the variance is smaller than the preset variance threshold value is determined as the relative location. Thereby enabling switching of the positioning mode.

According to the automatic parking positioning method provided by the embodiment of the invention, data detected by a vehicle-mounted laser radar, an inertia measurement unit and a wheel speed meter are fused to obtain high-precision absolute positioning of a vehicle, then image data near a parking space to be parked are acquired by an image acquisition device and are processed, and the position and the attitude of the vehicle relative to a parking space mark line are calculated by combining the data measured by the inertia measurement unit and the wheel speed meter, and target position information and target course information of the vehicle are determined based on a relative position relationship, so that the relative positioning of the vehicle is realized.

Fig. 2 is a block diagram of an automatic parking positioning device according to a second embodiment of the present invention, where the device may be a device capable of implementing the method according to embodiment 1 of the present application, such as an automatic parking positioning device or a chip system. As shown in fig. 2, the apparatus includes:

the processing module 201 is configured to obtain acceleration information and angular velocity information of the vehicle detected by the vehicle-mounted inertial measurement unit IMU in real time.

The processing module 201 is further configured to obtain wheel speed information of the vehicle detected by the wheel speed meter in real time;

carrying out strapdown calculation on the acceleration information, the angular speed information and the wheel speed information to obtain first position information and first course information of the vehicle in a first coordinate system;

hough processing is carried out on the image data, and an equation corresponding to a parking space mark line of the parking space to be parked is constructed;

according to an equation, determining the coordinates of each angular point of the parking space in a third coordinate system, and determining second position and attitude information of the vehicle in the third coordinate system; the third coordinate system is a coordinate system which is constructed by taking a first angular point of a plurality of angular points of the parking space as an origin;

In a specific implementation manner provided in this embodiment, the processing module 201 is specifically configured to:

acquiring initial acceleration information of a vehicle detected by an IMU within preset time, and calculating according to the initial acceleration information to obtain an initial pitch angle and an initial roll angle of the vehicle;

constructing a laser point cloud map according to the laser point cloud data;

In another specific implementation manner provided in this embodiment, the processing module 201 is further configured to:

calculating the distance from the real-time position to a parking space according to the real-time position of the vehicle;

In another specific implementation manner provided in this embodiment, the processing module 201 is specifically configured to:

and establishing an equation according to the parking space sign line.

when the mean value is not less than a preset mean value threshold value within a preset time length and the variance is not less than a preset variance threshold value, performing fusion processing on the first position information, the first heading information, the second position information and the second heading information through a first algorithm to obtain first heading information of the vehicle in a first coordinate system;

and when the mean value is smaller than a preset mean value threshold value within a preset time length and the variance is smaller than a preset variance threshold value, acquiring image data of the parking space to be parked, which is acquired by an image acquisition device on the vehicle.

The automatic parking positioning device provided by the embodiment of the invention can execute the method steps in the method embodiment, the implementation principle and the technical effect are similar, and the detailed description is omitted.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the determining module may be a processing element separately set up, or may be implemented by being integrated in a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the function of the determining module is called and executed by a processing element of the apparatus. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), etc. For another example, when some of the above modules are implemented in the form of a Processing element scheduler code, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can invoke the program code. As another example, these modules may be integrated together and implemented in the form of a System-on-a-chip (SOC).

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optics, Digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, bluetooth, microwave, etc.). DVD), or semiconductor media (e.g., Solid State Disk (SSD)), etc.

Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. As shown in fig. 3, the electronic device 300 may include: a processor 31 (e.g., CPU), a memory 32, a transceiver 33; the transceiver 33 is coupled to the processor 31, and the processor 31 controls the transceiving operation of the transceiver 33. Various instructions may be stored in memory 32 for performing various processing functions and implementing method steps performed by the electronic device of embodiments of the present invention. Preferably, the electronic device according to an embodiment of the present invention may further include: a power supply 34, a system bus 35, and a communication port 36. The system bus 35 is used to implement communication connections between the elements. The communication port 36 is used for connection communication between the electronic device and other peripherals.

The system bus mentioned in fig. 3 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The communication interface is used for realizing communication between the database access device and other equipment (such as a client, a read-write library and a read-only library). The Memory may include a Random Access Memory (RAM) and may also include a Non-Volatile Memory (Non-Volatile Memory), such as at least one disk Memory.

The Processor may be a general-purpose Processor, including a central processing unit CPU, a Network Processor (NP), and the like; but also a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components.

It should be noted that the embodiment of the present invention also provides a computer-readable storage medium, which stores instructions that, when executed on a computer, cause the computer to execute the method and the processing procedure provided in the above-mentioned embodiment.

The embodiment of the invention also provides a chip for running the instructions, and the chip is used for executing the method and the processing process provided by the embodiment.

Embodiments of the present invention also provide a program product, which includes a computer program stored in a storage medium, from which the computer program can be read by at least one processor, and the at least one processor executes the methods and processes provided in the embodiments.

Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, it should be understood that the above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims

1. An automatic parking positioning method, characterized by comprising:

according to the equation, determining the coordinates of each corner point of the parking space in a fourth coordinate system, and determining second position and attitude information of the vehicle in the third coordinate system; the third coordinate system is a coordinate system which is constructed by taking a first angular point of a plurality of angular points of the parking space as an origin;

2. The automatic parking positioning method according to claim 1, wherein before the obtaining acceleration information and angular velocity information of the vehicle detected by the vehicle-mounted inertial measurement unit IMU in real time, the method further comprises:

constructing a laser point cloud map according to the laser point cloud data;

3. The automatic parking positioning method according to claim 2, wherein before acquiring image data of a parking space acquired by an image acquisition device on a vehicle, the method further comprises:

4. The automatic parking positioning method according to claim 1, wherein the Hough processing is performed on the image data, and the constructing of the equation corresponding to the parking space marker line of the parking space specifically includes:

and establishing an equation according to the parking space sign line.

5. The automatic parking positioning method as claimed in claim 1, characterized in that the method further comprises:

6. An automatic parking positioning device, comprising:

the processing module is used for acquiring the acceleration information and the angular velocity information of the vehicle detected by the vehicle-mounted inertial measurement unit IMU in real time; and the number of the first and second groups,

7. An electronic device, comprising: a memory, a processor, and a transceiver;

the processor is used for being coupled with the memory, reading and executing the instructions in the memory to realize the method steps of any one of the claims 1-5;

8. A computer-readable storage medium having stored thereon computer instructions which, when executed by a computer, cause the computer to perform the method of any of claims 1-5.