CN112883058A - Calibration method, device, equipment, vehicle and medium for vehicle positioning - Google Patents

Calibration method, device, equipment, vehicle and medium for vehicle positioning Download PDF

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Publication number
CN112883058A
CN112883058A CN202110307828.1A CN202110307828A CN112883058A CN 112883058 A CN112883058 A CN 112883058A CN 202110307828 A CN202110307828 A CN 202110307828A CN 112883058 A CN112883058 A CN 112883058A
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China
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road
vehicle
coordinate information
image data
positioning
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王艳波
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Beijing CHJ Automotive Information Technology Co Ltd
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Beijing CHJ Automotive Information Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
    • G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases

Abstract

The disclosed embodiment relates to a calibration method, a device, equipment, a vehicle and a medium for vehicle positioning, wherein the calibration method can comprise the following steps: receiving at least one frame of road image data in the driving process of a vehicle; identifying road characteristic information in at least one frame of road image data; determining a target road section for vehicle driving from the roads in the road network database based on the road characteristic information and the corresponding relation between the roads in the road network database and the road characteristic information; acquiring road coordinate information of a target road section from the coordinate information of the road in the road network database; the road coordinate information is used to calibrate an inertial positioning unit on the vehicle. The embodiment of the disclosure realizes effective correction of the positioning deviation of the inertial positioning device on the vehicle, solves the problem of larger positioning deviation of the vehicle when the global navigation satellite system is unavailable or the signal is unstable, improves the positioning accuracy, reduces the cost of positioning deviation correction, and enhances the user experience.

Description

Calibration method, device, equipment, vehicle and medium for vehicle positioning
Technical Field
The present disclosure relates to the field of navigation positioning technologies, and in particular, to a calibration method, device, apparatus, vehicle, and medium for vehicle positioning.
Background
In the vehicle driving process, when a Global Navigation Satellite System (GNSS) is unavailable or signals are unstable, for example, the vehicle driving environment does not satisfy environmental elements of the global navigation satellite system, specifically, the vehicle drives in a tunnel, a thick cloud layer or a dense high-rise building area, and the like, the following scheme may be adopted to locate the vehicle:
the first scheme is as follows: the method comprises the steps of calculating a road on which a vehicle actually runs by depending on road network data of a Map and an inertial navigation system, namely correcting the deviation of a vehicle positioning module by using a Map matching deviation correcting (MMF) system. However, the position coordinate fed back by the map matching correction system still has a large deviation often compared with the actual position of the vehicle, and even the position coordinate cannot be fed back, so that the vehicle positioning module cannot correct the deviation in time, and further the deviation is larger and larger.
Scheme II: the vehicle positioning is carried out by utilizing a network positioning technology, for example, based on network base stations deployed on roads, the vehicle is positioned according to the transmission of network signals, the scheme is reliable, but has large errors, and the positioning scheme depends on the distribution density of the base stations, has poor universality and is not suitable for remote areas.
The third scheme is as follows: only an Inertial Measurement Unit (IMU) is relied upon for positioning. The scheme is limited to the accuracy of hardware of a gyroscope and an accelerometer, deviation exists during algorithm fusion, and deviation accumulation can be caused when an IMU system is used for positioning the vehicle independently for a long time, so that vehicle positioning deviation is increased.
In summary, for the situation that the global navigation satellite system is unavailable or the signal is unstable, the existing driving positioning scheme cannot achieve the effect of simultaneously considering the positioning accuracy, the positioning cost and the universality.
Disclosure of Invention
In order to solve the technical problems or at least partially solve the technical problems, embodiments of the present disclosure provide a calibration method, device, apparatus, vehicle, and medium for vehicle positioning.
In a first aspect, an embodiment of the present disclosure provides a calibration method for vehicle positioning, including:
receiving at least one frame of road image data in the driving process of a vehicle;
identifying road characteristic information in the at least one frame of road image data;
determining a target road section where the vehicle runs from the roads in the road network database on the basis of the road characteristic information and the corresponding relation between the roads in the road network database and the road characteristic information;
acquiring road coordinate information of the target road section from the coordinate information of the road in the road network database; the road coordinate information is used for calibrating an inertial positioning device on the vehicle.
In a second aspect, an embodiment of the present disclosure further provides a calibration method for vehicle positioning, including:
acquiring at least one frame of road image data in the vehicle driving process, and sending the data to a server; the server is used for identifying road characteristic information in the at least one frame of road image data, determining a target road section where the vehicle runs from roads in a road network database based on the road characteristic information and the corresponding relation between the roads and the road characteristic information in the road network database, and acquiring road coordinate information of the target road section from the coordinate information of the roads in the road network database; or the server is further configured to calculate a calibration coefficient of an inertial positioning device on the vehicle based on the road coordinate information and vehicle positioning coordinate information corresponding to a preset number of frames of road image data in the at least one frame of road image data;
and receiving the road coordinate information or the calibration coefficient sent by the server, and calibrating the inertial positioning device based on the road coordinate information or the calibration coefficient.
In a third aspect, an embodiment of the present disclosure further provides a calibration apparatus for vehicle positioning, including:
the road image data receiving module is used for receiving at least one frame of road image data in the running process of the vehicle;
the road characteristic information identification module is used for identifying road characteristic information in the at least one frame of road image data;
the target road section determining module is used for determining a target road section driven by the vehicle from the roads in the road network database based on the road characteristic information and the corresponding relation between the roads in the road network database and the road characteristic information;
the road coordinate information determining module is used for acquiring the road coordinate information of the target road section from the coordinate information of the roads in the road network database; the road coordinate information is used for calibrating an inertial positioning device on the vehicle.
In a fourth aspect, an embodiment of the present disclosure further provides a calibration apparatus for vehicle positioning, including:
the road image data sending module is used for acquiring at least one frame of road image data in the running process of the vehicle and sending the road image data to the server; the server is used for identifying road characteristic information in the at least one frame of road image data, determining a target road section where the vehicle runs from roads in a road network database based on the road characteristic information and the corresponding relation between the roads and the road characteristic information in the road network database, and acquiring road coordinate information of the target road section from the coordinate information of the roads in the road network database; or the server is further configured to calculate a calibration coefficient of an inertial positioning device on the vehicle based on the road coordinate information and vehicle positioning coordinate information corresponding to a preset number of frames of road image data in the at least one frame of road image data;
and the inertial positioning device calibration module is used for receiving the road coordinate information or the calibration coefficient sent by the server and calibrating the inertial positioning device based on the road coordinate information or the calibration coefficient.
In a fifth aspect, the disclosed embodiment further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the electronic device is enabled to implement any of the calibration methods for vehicle positioning provided by the disclosed embodiment.
In a sixth aspect, the disclosed embodiment further provides a vehicle, which includes a vehicle body, and further includes a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the vehicle is enabled to implement any one of the calibration methods for vehicle positioning provided by the disclosed embodiment.
In a seventh aspect, the present disclosure further provides a computer-readable storage medium, where a computer program is stored in the storage medium, and when the computer program is executed by a computing device, the computing device is enabled to implement any one of the calibration methods for vehicle positioning provided by the embodiments of the present disclosure.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has at least the following advantages: in the embodiment of the disclosure, a vision system deployed on a vehicle or any device with an image acquisition function can be used for acquiring road image data in a vehicle driving environment, a server performs matching in a road network database based on road characteristic information identified from the road image data to determine a target road section and uses coordinate information of the target road section in the road network database as standard coordinate information for calibrating an inertial positioning device on the vehicle, that is, the effective correction of positioning deviation of the inertial positioning device on the vehicle is realized by comprehensively utilizing the vehicle vision system and the road network database or comprehensively utilizing the image acquisition device and the road network database, the problem of large vehicle positioning deviation when a global navigation satellite system is unavailable or signals are unstable is solved, the positioning accuracy is improved, and the deviation rectifying cost of vehicle positioning is reduced, the universality of vehicle positioning and deviation rectifying is improved, and the user experience is enhanced. In addition, in the embodiment of the present disclosure, the road image data may be sent to the server in real time for identification processing, and the server may quickly determine the coordinate information of the current running target road segment of the vehicle and feed back the coordinate information to the vehicle in real time, or may quickly calculate the calibration coefficient of the inertial positioning device on the vehicle and feed back the calibration coefficient to the vehicle in real time, so as to achieve the effect of calibrating the inertial positioning device on the vehicle in real time.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a flow chart of a calibration method for vehicle positioning provided by an embodiment of the present disclosure;
FIG. 2 is a flow chart of another calibration method for vehicle positioning provided by the embodiments of the present disclosure;
FIG. 3 is a flow chart of yet another calibration method for vehicle positioning provided by the embodiments of the present disclosure;
FIG. 4 is a schematic diagram of a calibration processing architecture for vehicle positioning according to an embodiment of the present disclosure;
FIG. 5 is a schematic structural diagram of a calibration device for vehicle positioning according to an embodiment of the present disclosure;
FIG. 6 is a schematic structural diagram of another calibration apparatus for vehicle positioning according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of a vehicle according to an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that the embodiments and features of the embodiments of the present disclosure may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways than those described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments.
Fig. 1 is a flowchart of a calibration method for vehicle positioning according to an embodiment of the present disclosure, where the method may be performed by a calibration apparatus for vehicle positioning, where the apparatus may be implemented by software and/or hardware, and may be integrated on any electronic device with computing capability, such as a server (e.g., a cloud server) capable of interacting with a vehicle.
The embodiment of the disclosure can be applied to the situation that in the running process of a vehicle, when a global navigation satellite system is unavailable or signals are unstable, for example, the running environment of the vehicle does not meet the environmental elements of the work of the global navigation satellite system, specifically, the vehicle runs in a tunnel, a thick cloud layer or a region with dense high-rise buildings and the like, the vehicle is positioned by relying on an inertial positioning device (for example, an inertial measurement unit) on the vehicle, and the obtained positioning coordinates have large deviation, and the effective correction of the positioning deviation of the inertial positioning device on the vehicle is realized by combining a visual system deployed on the vehicle or an available image acquisition device. Moreover, aiming at the situation that the road condition of the current driving road of the vehicle is complex, the technical scheme of the embodiment of the disclosure can also realize effective correction of the positioning deviation of the inertial positioning device on the vehicle, and improve the positioning accuracy.
The inertial positioning device mentioned in the embodiments of the present disclosure is a device that measures angular acceleration and linear acceleration of a vehicle by using a gyroscope and an acceleration sensor, and calculates current attitude information of the vehicle with respect to an initial attitude based on the two measurement data, thereby implementing positioning of the vehicle, and may specifically include, for example, an inertial measurement unit and other devices having functions equivalent to those of the inertial measurement unit.
As shown in fig. 1, a calibration method for vehicle positioning provided by the embodiment of the present disclosure may include:
s101, receiving at least one frame of road image data in the vehicle driving process.
In the driving process of the vehicle, a camera (namely a vehicle vision system) deployed on the vehicle can be used for collecting images or videos of the current driving environment, and other image collecting devices independent of vehicle hardware, such as a driving recorder or an intelligent camera, can be used for collecting images or videos of the current driving environment to obtain at least one frame of road image data; at least one frame of road image data may then be uploaded to the server by the vehicle or by the image capture device. For example, multiple cameras are deployed on a vehicle, and the multiple cameras can be used for acquiring road image data at the same time, and then the road image data acquired by the cameras are fused and sent to a server. The specific deployment situation of the camera on the vehicle can be determined according to actual needs, and the embodiment of the disclosure is not particularly limited.
Optionally, the receiving multiple frames of road image data sent during the driving process of the vehicle includes:
receiving at least one frame of road image data and vehicle identification in the vehicle driving process; wherein the vehicle identification is used to uniquely characterize the vehicle.
The vehicle or the acquisition device can send the vehicle identification while sending the road image data to the server, so that the server stores the road image data of the vehicle according to the corresponding relation between the vehicle identification and the road image data, the storage error of the road image data is avoided, and the subsequent transmission error of the road coordinate information or the calibration coefficient is further avoided. The vehicle identifier may include any information that can be used to uniquely characterize the vehicle, such as a license plate number.
S102, identifying road characteristic information in at least one frame of road image data.
Illustratively, the vehicle or the acquisition device encodes the road image data before transmitting at least one frame of road image data to the server; and the server receives the road image data and then performs decoding processing, thereby obtaining road image data capable of being identified. The server may then perform recognition processing on the received at least one frame of road image data using any available image recognition technique, such as object detection and recognition, to determine road characteristic information of the road shown in the road image data, where the road characteristic information is used to describe characteristics of the road shown in the road image data. Illustratively, the road characteristic information includes at least one of: road shape, curvature, gradient, course, elevation, roll angle, road traffic signs including but not limited to traffic light signs, direction signs, prohibition signs, lane information including but not limited to lane type, number of lanes, lane line type, and the like, lane information, and median information.
In an alternative embodiment, identifying road characteristic information in at least one frame of road image data includes: and performing road feature point identification on at least one frame of road image data by using Scale-invariant feature transform (SIFT) to determine road feature information in at least one frame of road image data. The scale invariant feature transformation algorithm is a machine vision algorithm that can be used for detecting and describing local features in an image, finds an extreme point in a spatial scale, and extracts information such as a position, a scale, a rotation invariant and the like of the extreme point. By adopting the scale invariant feature conversion algorithm, road feature information in multi-frame road image data can be effectively filtered out, and the accuracy of subsequent road matching is ensured.
And S103, determining a target road section for vehicle driving from the roads in the road network database based on the road characteristic information and the corresponding relation between the roads in the road network database and the road characteristic information.
The road network database records the feature information of each road in detail according to the corresponding relationship between the road and the road feature information, and records the coordinate information (for example, longitude and latitude coordinates) of each road in detail according to the corresponding relationship between the road and the coordinate information, so that the specific road section shown in the road image data, that is, the specific road section where the vehicle is currently running, or the specific running position can be determined by using the road feature information identified from the road image data and the corresponding relationship between the road and the road feature information in the road network database. In the process of determining the target road section where the vehicle travels, any available feature matching algorithm may be adopted, for example, a K-nearest neighbor classification algorithm (KNN) or the like is used to match the obtained road feature information with the feature information of each road in the road network database, so as to determine the target road section where the vehicle travels. The K nearest neighbor classification algorithm is a method that can classify each record in the data set, and the specific implementation principle can refer to the prior art, and the embodiment of the present disclosure is not particularly limited.
In the process of determining the target road section for the vehicle to run, the more the feature information participating in matching, the more accurate the determined target road section is. Illustratively, matching is performed among the feature information of each road in the road network database based on road feature information recognized from the road image data, such as road shape, curvature, gradient, road traffic sign, lane information, etc., and the target link on which the vehicle is currently traveling is determined to be a specific link on the road a.
Further, the method provided by the embodiment of the present disclosure may further include: receiving the driving speed of a vehicle, and receiving the acquisition time of road image data at least comprising a first frame and a last frame in at least one frame of road image data; and determining the area length of the target road section driven by the vehicle based on the driving speed and the acquisition time, and acquiring the road coordinate information of the target road section from the coordinate information of the roads in the road network database based on the area length.
Illustratively, the road image data includes a plurality of frames (i.e., at least two frames) of road image data, and the vehicle may transmit the traveling speed of the vehicle and the acquisition time of each frame of road image data (or at least the acquisition times of the road image data of the first frame and the last frame) to the server while transmitting the plurality of frames of road image data to the server; the server can determine the current driving time of the vehicle based on the acquisition time of the first frame and the last frame of road image data in the currently received multi-frame road image data, further determine the current driving distance of the vehicle by combining the driving speed of the vehicle, namely the area length of the current target road section, and subsequently acquire the road coordinate information of the target road section from the coordinate information of the road in the road network database based on the area length.
The server can verify the acquisition time of the first frame and the last frame of road image data in the multi-frame road image data by receiving the acquisition time of each frame of road image data, so that the error of the acquisition time of the first frame and the last frame of road image data is avoided, and further the error of the calculation of the region length of the target road section is avoided.
S104, acquiring road coordinate information of a target road section from the coordinate information of the road in the road network database; the road coordinate information is used to calibrate an inertial positioning unit on the vehicle.
In an alternative embodiment, the server first obtains road coordinate information of the target road segment from the coordinate information of the roads in the road network database, where the road coordinate information may be, for example, a series of longitude and latitude coordinates for representing road positions, and then may send the road coordinate information to the vehicle according to the corresponding relationship between the road coordinate information of the vehicle and the vehicle identifier; and the vehicle receives the road coordinate information and uses the road coordinate information as standard coordinate information for the calibration process of the inertial positioning device.
For example, the vehicle may calculate a calibration coefficient (for representing a current positioning error of the inertial positioning device) of the inertial positioning device by using vehicle positioning coordinate information corresponding to a preset number of frames of road image data in at least one frame of road image data (the positioning coordinate information is obtained based on the inertial positioning device in the case that the global navigation satellite system is unavailable or the signal is unstable), and the received road coordinate information, and after the vehicle acquires positioning coordinate information determined based on the inertial positioning device later, the vehicle may correct the positioning coordinate information by using the previously obtained calibration coefficient, thereby obtaining accurate positioning coordinate information. The specific calculation of the calibration coefficient may be implemented by referring to the existing calibration calculation principle, and the embodiment of the present disclosure is not particularly limited. The specific value of the preset frame number may be set according to a requirement, for example, the value of the preset frame number may be equal to the total frame number of the currently received road image data, or may be any value smaller than the total frame number.
In another optional embodiment, the server may also calculate a calibration coefficient of an inertial positioning device on the vehicle by combining vehicle positioning coordinate information corresponding to a preset number of frames of road image data in at least one frame of road image data after obtaining the road coordinate information of the target road segment, and then may send the calibration coefficient to the vehicle according to a corresponding relationship between the calibration coefficient of the inertial positioning device on the vehicle and the vehicle identifier, so that the vehicle directly calibrates the inertial positioning device by using the calibration coefficient, that is, the vehicle corrects subsequent positioning coordinate information determined based on the inertial positioning device by using the calibration coefficient, thereby obtaining accurate positioning coordinate information. That is, optionally, the method provided by the embodiment of the present disclosure may further include: receiving vehicle positioning coordinate information corresponding to road image data with preset frame numbers in at least one frame of road image data; calculating a calibration coefficient of an inertial positioning device on the vehicle based on the vehicle positioning coordinate information and the road coordinate information of the target road section; the calibration coefficients are used to calibrate inertial positioning units on the vehicle. The server calculates the calibration coefficient, so that the data processing pressure of the vehicle can be relieved, and particularly, the consumption of vehicle calculation resources can be reduced for the vehicle with weak calculation force.
With respect to the specific use cases of the above examples, it may be determined according to the needs, and the embodiments of the present disclosure are not particularly limited.
In the embodiment of the disclosure, a vision system deployed on a vehicle or any device with an image acquisition function can be used for acquiring road image data in a vehicle driving environment, a server performs matching in a road network database based on road characteristic information identified from the road image data to determine a target road section and uses coordinate information of the target road section in the road network database as standard coordinate information for calibrating an inertial positioning device on the vehicle, that is, the effective correction of positioning deviation of the inertial positioning device on the vehicle is realized by comprehensively utilizing the vehicle vision system and the road network database or comprehensively utilizing the image acquisition device and the road network database, the problem of large vehicle positioning deviation when a global navigation satellite system is unavailable or signals are unstable is solved, the positioning accuracy is improved, and the deviation rectifying cost of vehicle positioning is reduced, the universality of vehicle positioning and deviation rectifying is improved, and the user experience is enhanced. In addition, in the embodiment of the present disclosure, the road image data may be sent to the server in real time for identification processing, and the server may quickly determine the coordinate information of the current running target road segment of the vehicle and feed back the coordinate information to the vehicle in real time, or may quickly calculate the calibration coefficient of the inertial positioning device on the vehicle and feed back the calibration coefficient to the vehicle in real time, so as to achieve the effect of calibrating the inertial positioning device on the vehicle in real time.
Fig. 2 is a flowchart of another calibration method for vehicle positioning provided in an embodiment of the present disclosure, which further optimizes and expands the technical solution in the above embodiment, and as shown in fig. 2, the calibration method for vehicle positioning provided in an embodiment of the present disclosure may include:
s201, receiving at least one frame of road image data in the vehicle driving process.
S202, identifying road characteristic information in at least one frame of road image data.
S203, receiving vehicle positioning coordinate information corresponding to road image data with preset frame number in at least one frame of road image data.
The vehicle positioning coordinate information is obtained based on an inertial positioning device on the vehicle under the condition that a global navigation satellite system is unavailable or the signal is unstable. The specific value of the preset frame number may be set according to a requirement, for example, the value of the preset frame number may be equal to the total frame number of the currently received road image data, or may be any value smaller than the total frame number.
In addition, the vehicle positioning coordinate information may be received at the same time as the server receives the road image data, or may be received separately, which is not specifically limited in this disclosure. In other words, there is no strict execution sequence limitation between the operation S201 and the operation S203, and fig. 2 is an example of an implementation logic and should not be understood as a specific limitation to the embodiments of the present disclosure.
And S204, determining the road area range where the vehicle runs by using the vehicle positioning coordinate information.
For the applicable scene of the embodiment of the present disclosure, when the vehicle collects each frame of road image data, although the positioning coordinate information output by the vehicle has a large positioning deviation, the positioning coordinate information may still be used to determine the area range of road matching. For example, the server may determine, based on the received vehicle positioning coordinate information, an area of a preset size (a specific value may be adaptively determined) surrounding the vehicle positioning coordinate information as a road area range, thereby improving efficiency of matching roads in the road network database.
S205, determining candidate roads in the road area range based on the coordinate information of the roads in the road network database.
And S206, determining a target road section where the vehicle runs from the candidate roads based on the road characteristic information and the corresponding relation between the candidate roads and the road characteristic information.
S207, acquiring road coordinate information of the target road section from the coordinate information of the candidate road; the road coordinate information is used to calibrate an inertial positioning unit on the vehicle.
In the embodiment of the disclosure, the server determines the road area range where the vehicle runs firstly, then determines the candidate road in the road area range based on the coordinate information of the road in the road network database, and finally determines the target road section where the vehicle runs from the candidate road, and obtains the road coordinate information of the target road section from the coordinate information of the candidate road, so as to be used as standard coordinate information for calibration of the inertial positioning device on the vehicle, thereby not only realizing effective correction of the positioning deviation of the inertial positioning device on the vehicle, but also improving the efficiency of road matching.
Fig. 3 is a flowchart of another calibration method for vehicle positioning according to an embodiment of the present disclosure, which may be performed by a calibration apparatus for vehicle positioning, where the calibration apparatus may be implemented by software and/or hardware, and may be integrated as a functional module on a vehicle (the vehicle may be any vehicle supporting a positioning function, such as a general vehicle or an autonomous vehicle, etc.), for example, on a vehicle end.
The calibration method for vehicle positioning shown in fig. 3 is executed in cooperation with the calibration method for vehicle positioning in the above-described embodiment, and details not explained in detail in the following embodiment may be referred to the explanations in the above-described embodiment.
As shown in fig. 3, a calibration method for vehicle positioning provided by the embodiment of the present disclosure may include:
s301, at least one frame of road image data in the vehicle driving process is obtained and sent to a server.
The server is used for identifying road characteristic information in at least one frame of road image data, determining a target road section for vehicle driving from roads in the road network database based on the road characteristic information and the corresponding relation between the roads and the road characteristic information in the road network database, and acquiring road coordinate information of the target road section from the coordinate information of the roads in the road network database; or the server is further used for calculating a calibration coefficient of an inertial positioning device on the vehicle based on the road coordinate information and vehicle positioning coordinate information corresponding to the road image data with the preset number of frames in the at least one frame of road image data.
Optionally, acquiring at least one frame of road image data during the driving process of the vehicle, and sending the data to the server, including:
acquiring a vehicle identifier and at least one frame of road image data in the vehicle driving process; the vehicle identification is used for uniquely characterizing the vehicle;
and sending the vehicle identification and at least one frame of road image data to a server according to the corresponding relation between the vehicle identification and the road image data.
In this disclosure, while the vehicle sends the road image data to the server, the vehicle identifier, the vehicle positioning coordinate information corresponding to the preset number of frames of road image data in the at least one frame of road image data, the driving speed, the acquisition time of the road image data at least including the first frame and the last frame in the at least one frame of road image data, and other information may be packaged and sent to the server, or may be sent as needed according to the processing requirement of the server, which is not specifically limited in this disclosure.
S302, receiving the road coordinate information or the calibration coefficient sent by the server, and calibrating the inertial positioning device based on the road coordinate information or the calibration coefficient.
For example, the vehicle may receive road coordinate information or calibration coefficients transmitted by the server based on the vehicle identification. If the vehicle receives the calibration coefficient sent by the server, the calibration coefficient can be directly used for correcting the subsequent vehicle positioning coordinate information determined based on the inertial positioning device; if the vehicle receives the road coordinate information sent by the server, the calibration coefficient of the inertial positioning device is calculated by combining the positioning coordinate information corresponding to the road image data with the preset frame number in at least one frame of road image data, and then the calibration coefficient is used for correcting the subsequent vehicle positioning coordinate information determined based on the inertial positioning device, namely calibrating the inertial positioning device.
Optionally, calibrating the inertial positioning device based on the road coordinate information includes:
sending the road coordinate information to a vehicle-mounted positioning chip so as to calibrate the inertial positioning device through the vehicle-mounted positioning chip; or
And sending the road coordinate information to a map positioning module so as to calibrate the inertial positioning device through the map positioning module.
The method comprises the following steps of realizing calibration through a vehicle-mounted positioning chip, belonging to a front-end fusion processing realization mode, specifically, executing calibration calculation about an inertial positioning device based on road coordinate information or a calibration coefficient by using the vehicle-mounted positioning chip; the method for realizing calibration through a map positioning module belongs to a rear-end fusion processing realization mode, and specifically, a map positioning module integrated on a vehicle is utilized to execute calibration calculation about an inertial positioning device based on road coordinate information or a calibration coefficient. The embodiment of the disclosure can be compatible with the front-end fusion processing scene or the rear-end fusion processing scene, and has higher universality for most vehicles.
On the basis of the above technical solution, optionally, before obtaining the multiple frames of road image data collected by the vehicle camera during the driving process and sending the data to the server, the calibration method for vehicle positioning provided by the embodiment of the present disclosure further includes:
determining whether a vehicle travel track formed by the historical vehicle positioning coordinate information matches a road track shown in a navigation map based on the historical vehicle positioning coordinate information within a preset time;
and if not, acquiring at least one frame of road image data in the driving process of the vehicle, and sending the data to the server.
The value of the preset time may be determined according to a requirement, and the embodiment of the present disclosure is not particularly limited. The historical vehicle positioning coordinate information can be obtained based on a global navigation satellite system and/or an inertial positioning device, if a vehicle running track formed by the historical vehicle positioning coordinate information is in a non-matching state with a road track shown in a navigation map within a preset time, the global navigation satellite system is considered to be in an unavailable state or a signal unstable state in the current running process of the vehicle, and a large deviation exists in the positioning coordinate information output by the inertial positioning device on the vehicle, the inertial positioning device needs to be calibrated by executing the scheme, so that accurate vehicle positioning coordinate information can be obtained based on the inertial positioning device subsequently. If the vehicle running track formed by the historical vehicle positioning coordinate information is in a matching state with the road track displayed in the navigation map within the preset time, the global navigation satellite system is considered to be in an available or better signal state in the current running process of the vehicle, or the positioning coordinate information output by an inertial positioning device on the vehicle belongs to an accurate positioning result, and the inertial positioning device is not required to be calibrated.
The matching condition of the vehicle positioning track within the preset time and the road track shown in the navigation map is judged, so that the accuracy and timeliness of the execution opportunity of the scheme are ensured, the inertial positioning device can be calibrated in time in the vehicle running process, and the effect of obtaining accurate vehicle positioning coordinate information based on the inertial positioning device even if the global navigation satellite system is unavailable or signals are unstable is achieved.
Further, the calibration method for vehicle positioning provided by the embodiment of the present disclosure further includes: acquiring vehicle positioning coordinate information which corresponds to road image data with preset frame number in at least one frame of road image data and is obtained based on an inertial positioning device, and sending the vehicle positioning coordinate information to a server; the server is also used for determining the road area range where the vehicle runs based on the vehicle positioning coordinate information, so that the efficiency of matching roads in the road network database by the server can be improved.
In the embodiment of the disclosure, a vision system deployed on a vehicle or any device with an image acquisition function can be used for acquiring road image data in a vehicle driving environment, a server performs matching in a road network database based on road characteristic information identified from the road image data to determine a target road section and uses coordinate information of the target road section in the road network database as standard coordinate information for calibrating an inertial positioning device on the vehicle, that is, the effective correction of positioning deviation of the inertial positioning device on the vehicle is realized by comprehensively utilizing the vehicle vision system and the road network database or comprehensively utilizing the image acquisition device and the road network database, the problem of large vehicle positioning deviation when a global navigation satellite system is unavailable or signals are unstable is solved, the positioning accuracy is improved, and the deviation rectifying cost of vehicle positioning is reduced, the universality of vehicle positioning and deviation rectifying is improved, and the user experience is enhanced.
Fig. 4 is a schematic diagram of a calibration processing architecture for vehicle positioning, provided by an embodiment of the present disclosure, for performing an exemplary description on calibration of an inertial positioning device on a vehicle based on interaction between a vehicle side and a service side, but should not be construed as a specific limitation to the embodiment of the present disclosure. In fig. 4, the example is illustrated in which the inertial positioning device is specifically used as an inertial measurement unit, and the road image data is acquired by a vehicle vision system.
As shown in fig. 4, after the positioning coordinate information output by the running vehicle, dotting matching is performed in the map system, if the running track of the vehicle cannot be attached to a certain road for a long time, that is, the running track of the vehicle formed by the positioning coordinate information output by the vehicle is not matched with the track of the road shown in the navigation map, it is considered that the global navigation satellite system is in an unavailable or unstable signal state in the current running process of the vehicle, and a large deviation also exists based on the positioning coordinate information output by the inertial measurement unit on the vehicle; at this time, the map module in the vehicle end may notify the vehicle vision system to perform data acquisition on the driving environment (mainly surrounding road elements) of the current vehicle, for example, image acquisition is performed by using multiple cameras deployed on the vehicle, the vehicle end may bind the image data acquired by the vision system and the current vehicle information, and upload the image data and the current vehicle information to the cloud environment in real time (that is, upload the image data to the cloud server), where the current vehicle information may include, but is not limited to, vehicle identification, positioning coordinate information when acquiring each frame of road image data, driving speed, acquisition time of each frame of road image data, and other information; the cloud receives data uploaded by a vehicle end, road image data capable of being identified and processed is obtained after the data passes through the ffmpeg encoding and decoding process, then effective road network characteristic data (namely the road characteristic information) is identified from the road image data, and meanwhile, a road matching range is determined according to positioning coordinate information with large deviation in current vehicle information; then, matching a target road section currently driven by the vehicle in a road network database based on the road matching range and the effective road network characteristic data; after the target road section is successfully matched, the cloud end can convert a series of coordinate points corresponding to the target road section through geographic coding, and then the coordinate points are transmitted back to the vehicle end through a vehicle map network module (such as a wireless network module) in the vehicle end; the vehicle end can use the received coordinate information of the target road section as standard coordinate information to calibrate the inertia measurement unit, and the calibration can be realized by adopting front-end fusion processing or rear-end fusion processing. Fig. 4 shows a back-end fusion processing implementation manner, that is, a map positioning module is used to calibrate an inertial measurement unit.
As shown in fig. 4, if the driving track formed by the positioning coordinate information output by the vehicle during driving can be successfully attached to a certain road, that is, the driving track of the vehicle is matched with the road track shown in the navigation map, it is determined that the global navigation satellite system is in an available or signal-preferred state during the current driving process of the vehicle, or the positioning coordinate information output by the inertial positioning device on the vehicle belongs to an accurate positioning result, and the vehicle can continue to move forward based on the navigation map shown by the map navigation module without calibrating the inertial positioning device.
Fig. 5 is a schematic structural diagram of a calibration apparatus for vehicle positioning according to an embodiment of the present disclosure, where the calibration apparatus may be implemented by software and/or hardware, and may be integrated on any electronic device with computing capability, such as a server capable of interacting with a vehicle.
As shown in fig. 5, a calibration apparatus 400 for vehicle positioning provided by an embodiment of the present disclosure may include a road image data receiving module 401, a road characteristic information identifying module 402, a target link determining module 403, and a road coordinate information determining module 404, where:
a road image data receiving module 401, configured to receive at least one frame of road image data in a driving process of a vehicle;
a road characteristic information identification module 402, configured to identify road characteristic information in at least one frame of road image data;
a target road section determining module 403, configured to determine a target road section where the vehicle travels from the roads in the road network database based on the road characteristic information and the corresponding relationship between the roads in the road network database and the road characteristic information;
a road coordinate information determining module 404, configured to obtain road coordinate information of a target road segment from the coordinate information of the roads in the road network database; the road coordinate information is used to calibrate an inertial positioning unit on the vehicle.
Optionally, the calibration apparatus 400 for vehicle positioning provided by the embodiment of the present disclosure further includes:
the positioning coordinate information receiving module is used for receiving vehicle positioning coordinate information corresponding to road image data with preset frame numbers in at least one frame of road image data;
accordingly, the target link determination module 403 includes:
the road area range determining unit is used for determining the road area range in which the vehicle runs by utilizing the vehicle positioning coordinate information;
the candidate road determining unit is used for determining candidate roads in the road area range based on the coordinate information of the roads in the road network database;
a target road determination unit for determining a target road segment on which the vehicle travels from the candidate roads based on the road characteristic information and the correspondence between the candidate roads and the road characteristic information;
and a road coordinate information determining module 404, configured to obtain road coordinate information of the target road segment from the coordinate information of the candidate road.
Optionally, the calibration apparatus 400 for vehicle positioning provided by the embodiment of the present disclosure further includes:
the positioning coordinate information receiving module is used for receiving vehicle positioning coordinate information corresponding to road image data with preset frame numbers in at least one frame of road image data;
the calibration coefficient calculation module is used for calculating the calibration coefficient of the inertial positioning device on the vehicle based on the vehicle positioning coordinate information and the road coordinate information of the target road section; the calibration coefficients are used to calibrate inertial positioning units on the vehicle.
Optionally, the road characteristic information identifying module 402 is specifically configured to:
and performing road characteristic point identification on at least one frame of road image data by using a scale invariant feature conversion algorithm, and determining road characteristic information in at least one frame of road image data.
Optionally, the road characteristic information comprises at least one of: road shape, curvature, slope, heading, elevation, roll angle, road traffic signs, lane information, and median information.
Optionally, the road image data receiving module 401 is specifically configured to:
receiving at least one frame of road image data and vehicle identification in the vehicle driving process; the vehicle identification is used for uniquely characterizing the vehicle;
accordingly, the calibration apparatus 400 for vehicle positioning provided by the embodiment of the present disclosure may further include:
and the data sending module is used for sending the road coordinate information or the calibration coefficient to the vehicle based on the vehicle identification.
Optionally, the calibration apparatus 400 for vehicle positioning provided by the embodiment of the present disclosure further includes:
the speed and time receiving module is used for receiving the running speed of the vehicle and receiving the acquisition time of road image data at least comprising a first frame and a last frame in at least one frame of road image data;
and the region length determining module is used for determining the region length of the target road section driven by the vehicle based on the driving speed and the acquisition time so as to acquire the road coordinate information of the target road section from the coordinate information of the road in the road network database based on the region length.
The calibration device for vehicle positioning provided by the embodiment of the disclosure can execute any calibration method for vehicle positioning provided by the embodiment of the disclosure, and has corresponding functional modules and beneficial effects of the execution method. Reference may be made to the description of any method embodiment of the disclosure that may not be described in detail in the embodiments of the apparatus of the disclosure.
Fig. 6 is a schematic structural diagram of another calibration apparatus for vehicle positioning according to an embodiment of the present disclosure, which may be implemented by software and/or hardware, and may be integrated on a vehicle as a functional module, for example, may be integrated on a vehicle end.
As shown in fig. 6, a calibration apparatus 500 for vehicle positioning provided by the embodiment of the present disclosure may include a road image data sending module 501 and an inertial positioning apparatus calibration module 502, where:
a road image data sending module 501, configured to obtain at least one frame of road image data in a vehicle driving process, and send the frame of road image data to a server; the server is used for identifying road characteristic information in at least one frame of road image data, determining a target road section for vehicle driving from roads in the road network database based on the road characteristic information and the corresponding relation between the roads and the road characteristic information in the road network database, and acquiring road coordinate information of the target road section from the coordinate information of the roads in the road network database; or the server is further used for calculating a calibration coefficient of an inertial positioning device on the vehicle based on the road coordinate information and vehicle positioning coordinate information corresponding to the road image data with the preset frame number in the at least one frame of road image data;
the inertial positioning device calibration module 502 is configured to receive the road coordinate information or the calibration coefficient sent by the server, and calibrate the inertial positioning device based on the road coordinate information or the calibration coefficient.
Optionally, the calibration apparatus 500 for vehicle positioning provided by the embodiment of the present disclosure further includes:
the track matching module is used for determining whether a vehicle running track formed by historical vehicle positioning coordinate information is matched with a road track displayed in a navigation map or not based on the historical vehicle positioning coordinate information within preset time;
the road image data sending module 501 is specifically configured to: and if not, acquiring at least one frame of road image data in the driving process of the vehicle, and sending the data to the server.
Optionally, the calibration apparatus 500 for vehicle positioning provided by the embodiment of the present disclosure further includes:
the positioning coordinate information sending module is used for acquiring vehicle positioning coordinate information which corresponds to road image data with preset frame number in at least one frame of road image data and is obtained based on the inertial positioning device, and sending the vehicle positioning coordinate information to the server; the server is also used for determining the road area range driven by the vehicle based on the vehicle positioning coordinate information.
Alternatively, the road image data transmission module 501 includes:
the data acquisition unit is used for acquiring a vehicle identifier and at least one frame of road image data in the vehicle driving process; the vehicle identification is used for uniquely characterizing the vehicle;
the data sending unit is used for sending the vehicle identification and at least one frame of road image data to the server according to the corresponding relation between the vehicle identification and the road image data;
accordingly, the inertial positioning device calibration module 502 is specifically configured to:
and the receiving server is used for calibrating the inertial positioning device based on the road coordinate information or the calibration coefficient sent by the vehicle identifier.
Optionally, the calibration apparatus 500 for vehicle positioning provided by the embodiment of the present disclosure may further include:
the speed and time sending module is used for acquiring the running speed and the acquisition time of road image data at least comprising a first frame and a last frame in at least one frame of road image data, and sending the acquired road image data to the server; the server is further used for determining the area length of the target road section driven by the vehicle based on the driving speed and the acquisition time, so as to obtain the road coordinate information of the target road section from the coordinate information of the roads in the road network database based on the area length.
Optionally, the inertial positioning device calibration module 502 includes:
the road coordinate information receiving unit is used for receiving the road coordinate information or the calibration coefficient sent by the server;
the first calibration unit is used for sending the road coordinate information or the calibration coefficient to the vehicle-mounted positioning chip so as to calibrate the inertial positioning device through the vehicle-mounted positioning chip; or
And the second calibration unit is used for sending the road coordinate information or the calibration coefficient to the map positioning module so as to calibrate the inertial positioning device through the map positioning module.
The calibration device for vehicle positioning provided by the embodiment of the disclosure can execute any calibration method for vehicle positioning provided by the embodiment of the disclosure, and has corresponding functional modules and beneficial effects of the execution method. Reference may be made to the description of any method embodiment of the disclosure that may not be described in detail in the embodiments of the apparatus of the disclosure.
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure, which is used to exemplarily illustrate an electronic device that implements a calibration method for vehicle positioning according to an embodiment of the present disclosure. The electronic device may be, for example, a computing device such as a server. The electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and occupation ranges of the embodiments of the present disclosure.
As shown in fig. 7, the electronic device 600 includes one or more processors 601 and memory 602.
The processor 601 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 600 to perform desired functions.
The memory 602 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, Random Access Memory (RAM), cache memory (or the like). The non-volatile memory may include, for example, Read Only Memory (ROM), a hard disk, flash memory, and the like. One or more computer program instructions may be stored on a computer readable storage medium and executed by processor 601 to implement the calibration method for vehicle positioning provided by the embodiments of the present disclosure, as well as to implement other desired functions. Various contents such as an input signal, a signal component, a noise component, etc. may also be stored in the computer-readable storage medium.
The calibration method for vehicle positioning provided by the embodiment of the disclosure may include: receiving at least one frame of road image data in the driving process of a vehicle; identifying road characteristic information in at least one frame of road image data; determining a target road section for vehicle driving from the roads in the road network database based on the road characteristic information and the corresponding relation between the roads in the road network database and the road characteristic information; acquiring road coordinate information of a target road section from the coordinate information of the road in the road network database; the road coordinate information is used to calibrate an inertial positioning unit on the vehicle. It should be understood that electronic device 600 may also perform other alternative embodiments provided by the disclosed method embodiments.
In one example, the electronic device 600 may further include: an input device 603 and an output device 604, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
The input device 603 may also include, for example, a keyboard, a mouse, and the like.
The output device 604 may output various information including the determined distance information, direction information, and the like to the outside. The output devices 604 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, among others.
Of course, for simplicity, only some of the components of the electronic device 600 relevant to the present disclosure are shown in fig. 7, omitting components such as buses, input/output interfaces, and the like. In addition, electronic device 600 may include any other suitable components depending on the particular application.
Fig. 8 is a schematic structural diagram of a vehicle according to an embodiment of the present disclosure, which is used to exemplarily illustrate a vehicle implementing the calibration method for vehicle positioning according to the embodiment of the present disclosure. The vehicle shown in fig. 8 is only an example, and should not bring any limitation to the functions and occupation ranges of the embodiments of the present disclosure.
As shown in fig. 8, a vehicle 700 includes a body 705, and also includes one or more processors 701 and memory 702. The specific implementation of the vehicle body 705 can refer to the prior art, and the embodiments of the present disclosure are not particularly limited.
The processor 701 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the vehicle 700 to perform desired functions.
Memory 702 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, Random Access Memory (RAM), cache memory (or the like). The non-volatile memory may include, for example, Read Only Memory (ROM), a hard disk, flash memory, and the like. One or more computer program instructions may be stored on a computer-readable storage medium and executed by the processor 701 to implement the calibration method for vehicle positioning provided by the embodiments of the present disclosure, as well as to implement other desired functions. Various contents such as an input signal, a signal component, a noise component, etc. may also be stored in the computer-readable storage medium.
The calibration method for vehicle positioning provided by the embodiment of the disclosure may include: acquiring at least one frame of road image data in the vehicle driving process, and sending the data to a server; the server is used for identifying road characteristic information in at least one frame of road image data, determining a target road section for vehicle driving from roads in the road network database based on the road characteristic information and the corresponding relation between the roads and the road characteristic information in the road network database, and acquiring road coordinate information of the target road section from the coordinate information of the roads in the road network database; or the server is further used for calculating a calibration coefficient of an inertial positioning device on the vehicle based on the road coordinate information and vehicle positioning coordinate information corresponding to the road image data with the preset frame number in the at least one frame of road image data; and receiving the road coordinate information or the calibration coefficient sent by the server, and calibrating the inertial positioning device based on the road coordinate information or the calibration coefficient. It should be understood that the vehicle 700 may also perform other alternative embodiments provided by the disclosed method embodiments.
In one example, the vehicle 700 may further include: an input device 703 and an output device 704, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
The input device 703 may include, for example, a keyboard, a mouse, and the like.
The output device 704 may output various information including the determined distance information, direction information, and the like to the outside. The output devices 704 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, among others.
Of course, for simplicity, only some of the components of the vehicle 700 relevant to the present disclosure are shown in fig. 8, omitting components such as buses, input/output interfaces, and the like. In addition, the vehicle 700 may include any other suitable components, depending on the particular application.
In addition to the above-described methods, apparatus and vehicles, the disclosed embodiments also provide a computer program product comprising a computer program or computer program instructions that, when executed by a computing device, cause the computing device to implement any of the calibration methods for vehicle positioning provided by the disclosed embodiments.
The computer program product may write program code for performing the operations of embodiments of the present disclosure in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the consumer electronic device, partly on the consumer electronic device, as a stand-alone software package, partly on the consumer electronic device and partly on a remote electronic device, or entirely on the remote electronic device.
Furthermore, embodiments of the present disclosure may also provide a computer-readable storage medium having stored thereon computer program instructions that, when executed by a computing device, cause the computing device to implement any of the calibration methods for vehicle positioning provided by embodiments of the present disclosure.
In one aspect, a calibration method for vehicle positioning provided by the embodiments of the present disclosure may include: receiving at least one frame of road image data in the driving process of a vehicle; identifying road characteristic information in at least one frame of road image data; determining a target road section for vehicle driving from the roads in the road network database based on the road characteristic information and the corresponding relation between the roads in the road network database and the road characteristic information; acquiring road coordinate information of a target road section from the coordinate information of the road in the road network database; the road coordinate information is used to calibrate an inertial positioning unit on the vehicle.
On the other hand, the calibration method for vehicle positioning provided by the embodiment of the disclosure may include: acquiring at least one frame of road image data in the vehicle driving process, and sending the data to a server; the server is used for identifying road characteristic information in at least one frame of road image data, determining a target road section for vehicle driving from roads in the road network database based on the road characteristic information and the corresponding relation between the roads and the road characteristic information in the road network database, and acquiring road coordinate information of the target road section from the coordinate information of the roads in the road network database; or the server is further used for calculating a calibration coefficient of an inertial positioning device on the vehicle based on the road coordinate information and vehicle positioning coordinate information corresponding to the road image data with the preset frame number in the at least one frame of road image data; and receiving the road coordinate information or the calibration coefficient sent by the server, and calibrating the inertial positioning device based on the road coordinate information or the calibration coefficient.
It should be understood that the computer program instructions, when executed by a computing device, may also cause the computing device to implement other alternative embodiments provided by the disclosed method embodiments.
A computer-readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (16)

1. A calibration method for vehicle positioning, comprising:
receiving at least one frame of road image data in the driving process of a vehicle;
identifying road characteristic information in the at least one frame of road image data;
determining a target road section where the vehicle runs from the roads in the road network database on the basis of the road characteristic information and the corresponding relation between the roads in the road network database and the road characteristic information;
acquiring road coordinate information of the target road section from the coordinate information of the road in the road network database; the road coordinate information is used for calibrating an inertial positioning device on the vehicle.
2. The method of claim 1, wherein prior to determining the target road segment traveled by the vehicle from the roads in the road network database, further comprising:
receiving vehicle positioning coordinate information corresponding to road image data with preset frame numbers in the at least one frame of road image data;
accordingly, determining a target link traveled by the vehicle from the roads in the road network database based on the road characteristic information and the correspondence between the roads in the road network database and the road characteristic information includes:
determining a road area range in which the vehicle runs by using the vehicle positioning coordinate information;
determining candidate roads in the road area range based on the coordinate information of the roads in the road network database;
determining a target road section where the vehicle runs from the candidate roads on the basis of the road characteristic information and the corresponding relation between the candidate roads and the road characteristic information;
acquiring the road coordinate information of the target road section from the coordinate information of the roads in the road network database, wherein the road coordinate information comprises:
and acquiring the road coordinate information of the target road section from the coordinate information of the candidate road.
3. The method of claim 1 or 2, further comprising:
receiving vehicle positioning coordinate information corresponding to road image data with preset frame numbers in the at least one frame of road image data;
calculating a calibration coefficient of an inertial positioning device on the vehicle based on the vehicle positioning coordinate information and the road coordinate information of the target road section; the calibration coefficient is used for calibrating an inertial positioning device on the vehicle.
4. The method of claim 1 or 2, wherein identifying road characteristic information in the at least one frame of road image data comprises:
and performing road characteristic point identification on the at least one frame of road image data by using a scale invariant feature conversion algorithm, and determining road characteristic information in the at least one frame of road image data.
5. The method according to claim 1 or 2, characterized in that the road characteristic information comprises at least one of the following: road shape, curvature, slope, heading, elevation, roll angle, road traffic signs, lane information, and median information.
6. The method of claim 1 or 2, further comprising:
receiving the driving speed of the vehicle and the acquisition time of road image data at least comprising a first frame and a last frame in the at least one frame of road image data;
and determining the area length of a target road section driven by the vehicle based on the driving speed and the acquisition time, so as to acquire the road coordinate information of the target road section from the coordinate information of the road in the road network database based on the area length.
7. A calibration method for vehicle positioning, comprising:
acquiring at least one frame of road image data in the vehicle driving process, and sending the data to a server; the server is used for identifying road characteristic information in the at least one frame of road image data, determining a target road section where the vehicle runs from roads in a road network database based on the road characteristic information and the corresponding relation between the roads and the road characteristic information in the road network database, and acquiring road coordinate information of the target road section from the coordinate information of the roads in the road network database; or the server is further configured to calculate a calibration coefficient of an inertial positioning device on the vehicle based on the road coordinate information and vehicle positioning coordinate information corresponding to a preset number of frames of road image data in the at least one frame of road image data;
and receiving the road coordinate information or the calibration coefficient sent by the server, and calibrating the inertial positioning device based on the road coordinate information or the calibration coefficient.
8. The method of claim 7, before acquiring at least one frame of road image data during the driving of the vehicle and sending the data to the server, further comprising:
determining whether a vehicle running track formed by historical vehicle positioning coordinate information is matched with a road track displayed in a navigation map or not based on the historical vehicle positioning coordinate information within a preset time;
and if not, acquiring at least one frame of road image data in the driving process of the vehicle, and sending the data to the server.
9. The method of claim 7 or 8, further comprising:
acquiring vehicle positioning coordinate information which corresponds to road image data with preset frame number in the at least one frame of road image data and is obtained based on the inertial positioning device, and sending the vehicle positioning coordinate information to the server; the server is also used for determining the road area range driven by the vehicle based on the vehicle positioning coordinate information.
10. The method of claim 7 or 8, further comprising:
acquiring the running speed and the acquisition time of road image data at least comprising a first frame and a last frame in the at least one frame of road image data, and sending the acquired road image data to the server; the server is further used for determining the area length of a target road section driven by the vehicle based on the driving speed and the acquisition time, so as to acquire the road coordinate information of the target road section from the coordinate information of the roads in the road network database based on the area length.
11. The method of claim 7 or 8, wherein calibrating the inertial positioning device based on the road coordinate information or the calibration coefficients comprises:
sending the road coordinate information or the calibration coefficient to a vehicle-mounted positioning chip so as to calibrate the inertial positioning device through the vehicle-mounted positioning chip; or
And sending the road coordinate information or the calibration coefficient to a map positioning module so as to calibrate the inertial positioning device through the map positioning module.
12. A calibration arrangement for vehicle positioning, comprising:
the road image data receiving module is used for receiving at least one frame of road image data in the running process of the vehicle;
the road characteristic information identification module is used for identifying road characteristic information in the at least one frame of road image data;
the target road section determining module is used for determining a target road section driven by the vehicle from the roads in the road network database based on the road characteristic information and the corresponding relation between the roads in the road network database and the road characteristic information;
the road coordinate information determining module is used for acquiring the road coordinate information of the target road section from the coordinate information of the roads in the road network database; the road coordinate information is used for calibrating an inertial positioning device on the vehicle.
13. A calibration arrangement for vehicle positioning, comprising:
the road image data sending module is used for acquiring at least one frame of road image data in the running process of the vehicle and sending the road image data to the server; the server is used for identifying road characteristic information in the at least one frame of road image data, determining a target road section where the vehicle runs from roads in a road network database based on the road characteristic information and the corresponding relation between the roads and the road characteristic information in the road network database, and acquiring road coordinate information of the target road section from the coordinate information of the roads in the road network database; or the server is further configured to calculate a calibration coefficient of an inertial positioning device on the vehicle based on the road coordinate information and vehicle positioning coordinate information corresponding to a preset number of frames of road image data in the at least one frame of road image data;
and the inertial positioning device calibration module is used for receiving the road coordinate information or the calibration coefficient sent by the server and calibrating the inertial positioning device based on the road coordinate information or the calibration coefficient.
14. An electronic device, characterized in that it comprises a memory and a processor, wherein the memory has stored therein a computer program which, when being executed by the processor, causes the electronic device to carry out a calibration method for vehicle positioning according to any one of claims 1-6.
15. A vehicle comprising a body, further comprising a memory and a processor, wherein the memory has stored thereon a computer program which, when executed by the processor, causes the vehicle to carry out a calibration method for vehicle positioning according to any one of claims 7-11.
16. A computer-readable storage medium, characterized in that the storage medium has stored therein a computer program which, when executed by a computing device, causes the computing device to carry out the calibration method for vehicle positioning of any one of claims 1-6, or the calibration method for vehicle positioning of any one of claims 7-11.
CN202110307828.1A 2021-03-23 2021-03-23 Calibration method, device, equipment, vehicle and medium for vehicle positioning Pending CN112883058A (en)

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