Automatic driving vehicle-mounted sensor fusion positioning method, device, equipment and medium

Abstract

The invention relates to an automatic driving vehicle-mounted sensor fusion positioning method, device and equipment and a medium, and the method comprises the steps: fusing a coarse positioning result corresponding to I MU data and a coarse positioning result corresponding to wheel speed meter data based on a preset Kalman filtering algorithm, recalculating the position deviation, and obtaining the position deviation of the I MU data; carrying out constraint calculation on the relative displacement so as to determine fusion positioning information of the autonomous vehicle; and determining a vehicle moving track of the autonomous vehicle according to the fused positioning information, and performing map matching or visual matching based on the vehicle moving track to complete map matching positioning of the autonomous vehicle. According to the invention, high-precision positioning of data fusion of the automatic driving vehicle-mounted sensor can be realized without the help of high-cost and high-precision inertial navigation equipment under the condition that the GNSS signal is unavailable.

Description

Technical Field

[0001] This application relates to the field of vehicle positioning, and in particular to an autonomous driving vehicle-mounted fusion positioning method, corresponding device, electronic device and computer-readable storage medium. Background Technology

[0002] High-precision positioning and navigation are crucial for intelligent autonomous driving systems. With the increasing development of autonomous driving, relying on single sensors for positioning often faces various limitations, such as unstable or absent GPS signals, laser point cloud drift in scenarios like long straight corridors or tunnels, and instantaneous wheel speed measurement anomalies caused by tire slippage. At the same time, the application demand for high-precision positioning is becoming increasingly widespread, and users are placing increasingly higher requirements on the positioning quality based on vehicle sensors, gradually shifting from navigation-level to lane-level positioning.

[0003] Multi-sensor fusion positioning solutions can overcome the limitations of single-sensor positioning, making the positioning system more robust and maintaining high system stability, reliability, and accuracy even in complex road traffic scenarios. Commonly used vehicle positioning sensors include GNSS, inertial navigation, millimeter-wave radar, ultrasonic radar, high-precision maps, wheel speedometers, lidar, and cameras. Currently, in connected autonomous driving solutions, high-precision positioning under low-cost conditions, based on onboard units (OBUs) and onboard sensors, has significant industrial value, thereby promoting the large-scale application of autonomous driving.

[0004] Currently, the derivation of system motion states based on multiple wheel speed meter (WSM) data often involves treating the WSM data as time-series data and calculating the motion state frame by frame due to differences in the motion models of each system. This calculation model fails to fully consider the impact of the differences in the motion models of each system on the trajectory derivation process, especially in terms of the error model. When fusing WSM and IMU data, trajectory derivation is performed separately, and trajectory deviation is used as a common constraint for position optimization, without joint adjustment. Summary of the Invention

[0005] The purpose of this application is to address existing problems by providing an autonomous driving vehicle sensor fusion localization method, corresponding device, electronic device, and computer-readable storage medium.

[0006] To achieve the various objectives of this application, the following technical solution is adopted:

[0007] An autonomous driving vehicle sensor fusion localization method proposed for one of the purposes of this application includes:

[0008] When the high-precision positioning signal is detected to be unavailable, acquire the linear velocity from the IMU data and wheel speed meter data of the autonomous vehicle;

[0009] The attitude of the autonomous vehicle is calculated by converting the angular velocity data and acceleration data in the IMU data into the attitude information of the autonomous vehicle in three-dimensional space to determine the Euler angles of the autonomous vehicle. Based on the rotation matrix of the pitch angle and the rotation matrix of the roll angle in the Euler angle, the rotation matrix for correcting the IMU data is calculated and determined to determine the coarse positioning information corresponding to the IMU data.

[0010] The linear velocity in the wheel speed meter data is determined, and the angular velocity in the wheel speed meter data is obtained based on the linear velocity according to the preset dual-wheel differential speed model. The pre-integration between two frames of wheel speed meter data is calculated based on the linear velocity and angular velocity in the wheel speed meter data to determine the coarse positioning information corresponding to the wheel speed meter data.

[0011] Based on a preset Kalman filter algorithm, the coarse positioning results corresponding to the IMU data and the coarse positioning results corresponding to the wheel speed meter data are fused together, the position deviation is recalculated, and the constraint calculation of relative displacement is performed to determine the fused positioning information of the autonomous vehicle.

[0012] The vehicle movement trajectory of the autonomous vehicle is determined based on the fused positioning information, and map matching or visual matching is performed based on the vehicle movement trajectory to complete the map matching and positioning of the autonomous vehicle.

[0013] Optionally, the step of calculating and determining the rotation matrix for correcting the IMU data based on the rotation matrix of the pitch angle and the rotation matrix of the roll angle in the Euler angles, in order to determine the coarse positioning information corresponding to the IMU data, includes:

[0014] In response to a coarse positioning command based on IMU data, the angular velocity data and acceleration data in the IMU data are determined, and the Euler angles of the autonomous vehicle are calculated and determined based on the angular velocity data and acceleration data in the IMU data.

[0015] Determine the pitch angle and roll angle in the Euler angles of the autonomous vehicle, and calculate and determine the rotation matrix for correcting the IMU data based on the product between the rotation matrix of the pitch angle and the rotation matrix of the roll angle.

[0016] The coarse positioning information corresponding to the IMU data is determined based on the rotation matrix of the IMU data.

[0017] Optionally, the step of determining the linear velocity in the wheel speed gauge data and obtaining the angular velocity in the wheel speed gauge data based on the linear velocity using a preset dual-wheel differential speed model includes:

[0018] Determine the linear velocity of the right wheel, the linear velocity of the left wheel, and the track width of the rear wheel in the wheel speedometer;

[0019] The preset dual-wheel differential speed model is invoked to calculate and determine the difference between the linear velocity of the right wheel and the linear velocity of the left wheel;

[0020] The angular velocity in the wheel speedometer data is determined based on the ratio between the difference and the rear wheel track.

[0021] Optionally, the step of calculating and determining the pre-integral between two frames of wheel speed meter data based on the linear velocity and angular velocity in the wheel speed meter data to determine the coarse positioning information corresponding to the wheel speed meter data includes:

[0022] The linear velocity and angular velocity in the wheel speed meter data are determined, and the pre-integration between the wheel speed meter data of the i-th frame and the j-th frame is calculated and determined based on the linear velocity and angular velocity in the wheel speed meter data according to the preset pre-integration model.

[0023] Based on the pre-integration between the two frames of wheel speed meter data, the relative displacement and relative rotation components between the wheel speed meter data of the i-th frame and the j-th frame are determined.

[0024] Based on the relative displacement and relative rotation components between the wheel speed meter data of the i-th and j-th frames, the coarse positioning information corresponding to the wheel speed meter data is determined.

[0025] Optionally, before the step of acquiring the linear velocity from the IMU data and wheel speedometer data of the autonomous vehicle when the high-precision positioning signal is detected to be unavailable, the following steps are included:

[0026] In response to sensor status monitoring commands, the number of satellites and GNSS positioning signals of the autonomous vehicle at the current moment are obtained;

[0027] If the number of satellites at the current moment reaches a preset threshold and the GNSS positioning signal is a fixed solution, the signal state of the high-precision positioning signal is determined to be available.

[0028] Otherwise, the high-precision positioning signal is determined to be unavailable.

[0029] Optionally, after the step of determining that the signal state of the high-precision positioning signal is in an available state, the method includes:

[0030] Acquire GNSS high-precision positioning data, IMU data, and wheel speed meter data, and calculate and determine the joint adjustment of the GNSS high-precision positioning data, IMU data, and wheel speed meter data;

[0031] Based on the joint adjustment, the rotation and translation speed, IMU bias, and local corresponding points in the autonomous vehicle are optimized to perform map matching and localization of the autonomous vehicle.

[0032] Optionally, the step of fusing the coarse positioning results corresponding to the IMU data and the coarse positioning results corresponding to the wheel speedometer data based on a preset Kalman filter algorithm, recalculating the position deviation, and performing a constraint calculation of the relative displacement to determine the fused positioning information of the autonomous vehicle includes:

[0033] Based on a preset Kalman filter algorithm, the coarse positioning results corresponding to the IMU data and the coarse positioning results corresponding to the wheel speed meter data are fused together, including:

[0034] State variables:

[0035] X K =[v x ,v y ,θ] T ,

[0036] Among them, v x ,、v y Let θ be the vehicle's velocity in the X and Y directions, and θ be the angle between the vehicle's direction of motion and the due east direction. The input values ​​are:

[0037] U k =[a′ x ,a′ y ,ω yaw ] T ,

[0038] Where, a′ x ,a′ y Let ω be the acceleration of the vehicle relative to the inertial navigation coordinate system. yaw yaw is the angular acceleration;

[0039] Transform the inertial navigation coordinate system to the geographic coordinate system, where O is the origin of the geographic coordinate system, O' is the origin of the IMU coordinate system, v is the vehicle speed, and the angle between the inertial navigation coordinate system and the geographic coordinate system is θ.

[0040] Equations of state:

[0041]

[0042] Where: X k+1 The state vector of the system at time k+1

[0043] The components of velocity v in the x and y directions at time k;

[0044] f(X k Uk+1 ): Nonlinear state transition function

[0045] U k+1 : Control input vector at time k+1

[0046] Q: State covariance matrix

[0047] Observation equation:

[0048] Z = CX k +R,

[0049]

[0050] Since there are no observations in the direction of motion, the Z matrix only contains observations in the velocity direction. Q and R are the state covariance matrix and the observation covariance matrix, respectively. The state equation f is nonlinear, therefore a Kalman filter algorithm is needed to approximate the state equation nonlinearly. The state equation f at point U... k-1 Taylor expansion yields:

[0051] f(X k U k )≈J(U k-1 U k (X) k-1 -U k-1 )+f(U k-1 U k ),

[0052] Where matrix J(U) k-1 U k Let f be the Jacobian matrix of f;

[0053] Predicting covariance estimation:

[0054] P k =J T P k-1 J+Q,

[0055] Kalman Gain Update:

[0056] K = PC T / (C T PC+R),

[0057] Status Update:

[0058]

[0059] Predicting covariance updates:

[0060] P = (I - KC)P.

[0061] An autonomous driving vehicle sensor fusion positioning device provided for another purpose of this application includes:

[0062] The data acquisition module is configured to acquire the linear velocity from the IMU data and wheel speed meter data of the autonomous vehicle when the high-precision positioning signal is detected as unavailable.

[0063] The IMU coarse localization module is configured to perform attitude calculation on the autonomous vehicle, convert the angular velocity data and acceleration data in the IMU data into attitude information of the autonomous vehicle in three-dimensional space, so as to determine the Euler angles of the autonomous vehicle, and calculate and determine the rotation matrix to correct the IMU data based on the rotation matrix of pitch angle and roll angle in the Euler angle, so as to determine the coarse localization information corresponding to the IMU data.

[0064] The wheel speed meter coarse positioning module is configured to determine the linear velocity in the wheel speed meter data, obtain the angular velocity in the wheel speed meter data based on the linear velocity according to the preset dual-wheel differential speed model, and calculate and determine the pre-integration between two frames of wheel speed meter data based on the linear velocity and angular velocity in the wheel speed meter data, so as to determine the coarse positioning information corresponding to the wheel speed meter data.

[0065] The fusion positioning module is configured to fuse the coarse positioning results corresponding to the IMU data and the coarse positioning results corresponding to the wheel speed meter data based on a preset Kalman filter algorithm, recalculate the position deviation, and perform a constraint calculation of the relative displacement to determine the fusion positioning information of the autonomous vehicle.

[0066] The map matching and positioning module is configured to determine the vehicle movement trajectory of the autonomous vehicle based on the fused positioning information, and perform map matching or visual matching based on the vehicle movement trajectory to complete the map matching and positioning of the autonomous vehicle.

[0067] An electronic device provided for another purpose of this application includes a central processing unit and a memory, the central processing unit being configured to invoke and run a computer program stored in the memory to perform the steps of the autonomous vehicle map matching and positioning method of this application.

[0068] A computer-readable storage medium is provided for another purpose of this application, which stores, in the form of computer-readable instructions, a computer program implemented according to the autonomous vehicle map matching and positioning method, which, when called by a computer, executes the steps included in the corresponding method.

[0069] Compared to existing technologies, this application addresses the limitations of relying on a single sensor for positioning, which is often subject to various constraints, such as unstable or missing GPS signals, laser point cloud drift in scenarios like long straight corridors or tunnels, instantaneous wheel speed measurement anomalies caused by tire slippage, and the fact that wheel speed measurement data is often treated as time-series data, with motion state derivation and calculation performed frame by frame. This calculation model fails to fully consider the differences in motion models of various systems. This application offers advantages including, but not limited to, the following:

[0070] Firstly, when the high-precision positioning signal is detected to be unavailable, this application acquires the linear velocity from the IMU data and wheel speedometer data of the autonomous vehicle; performs attitude calculation on the autonomous vehicle, converting the angular velocity and acceleration data from the IMU data into attitude information of the autonomous vehicle in three-dimensional space to determine the Euler angles of the autonomous vehicle; based on the rotation matrix of the pitch angle and the rotation matrix of the roll angle in the Euler angles, calculates and determines the rotation matrix of the IMU data to determine the coarse positioning information corresponding to the IMU data; determines the linear velocity from the wheel speedometer data, and based on the preset dual-wheel differential speed model, obtains the angular velocity from the wheel speedometer data according to the linear velocity, and then calculates and determines the attitude information of the autonomous vehicle in three-dimensional space to determine the Euler angles of the autonomous vehicle; based on the rotation matrix of the pitch angle and the rotation matrix of the roll angle in the Euler angles, calculates and determines the rotation matrix of the IMU data to correct the IMU data to determine the coarse positioning information corresponding to the IMU data; determines the linear velocity from the wheel speedometer data, and based on the linear velocity from the wheel speedometer data, obtains the angular velocity from the wheel speedometer data to determine the attitude information of the autonomous vehicle in three-dimensional space ... Linear velocity and angular velocity calculations determine the pre-integration between two frames of wheel speed meter data to determine the coarse positioning information corresponding to the wheel speed meter data. Based on a preset Kalman filter algorithm, the coarse positioning results corresponding to the IMU data and the coarse positioning results corresponding to the wheel speed meter data are fused together, the position deviation is recalculated, and the relative displacement is constrained to determine the fused positioning information of the autonomous vehicle. The vehicle movement trajectory of the autonomous vehicle is determined according to the fused positioning information, and map matching or visual matching is performed based on the vehicle movement trajectory. This application can achieve high-precision map matching and positioning of autonomous vehicles without the need for high-cost, high-precision inertial navigation equipment when GNSS signals are unavailable.

[0071] Secondly, by combining GNSS data, IMU data, and wheel speed measurement, high-precision and high-reliability positioning results are provided for autonomous driving and vehicle-to-everything (V2X) communication, significantly improving the data quality of the fused positioning results. This provides a high-quality data foundation for autonomous vehicles to perform environmental perception calculations, such as map matching and visual matching.

[0072] Third, by combining dual wheel speedometer data with the vehicle motion model, a wheel speedometer pre-integration method based on the differential speed of the dual wheel speedometers was derived and calculated, which integrates multiple wheel speedometer measurements between two frames into a single observation of the vehicle's relative motion state.

[0073] Fourth, by combining GNSS, IMU, and wheel speed meter adjustment, the system state, including rotation and translation speed, IMU bias, and local corresponding points, is optimized to achieve high-precision positioning. Furthermore, robust high-precision positioning in complex scenarios is achieved through multi-sensor fusion and complementarity. Attached Figure Description

[0074] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0075] Figure 1 This is an exemplary network architecture for the autonomous vehicle map matching and positioning method of the present invention;

[0076] Figure 2 This is a flowchart illustrating the map matching and positioning method for autonomous vehicles in an embodiment of the present invention.

[0077] Figure 3 This is a schematic diagram of obtaining the angular velocity ω' of the yaw angle θ1 in an embodiment of the present invention;

[0078] Figure 4 This is a schematic diagram illustrating the transformation from the inertial navigation coordinate system to the geographic coordinate system in an embodiment of the present invention;

[0079] Figure 5 This is a schematic diagram of the autonomous vehicle map matching and positioning device in an embodiment of the present invention;

[0080] Figure 6 This is a schematic diagram of the structure of a computer device in an embodiment of the present invention. Detailed Implementation

[0081] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0082] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0083] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0084] Those skilled in the art will understand that the terms "client," "terminal," and "terminal device" as used herein include both devices that receive wireless signals, devices that only possess wireless signal receiver capabilities without transmission capabilities, and devices with receiving and transmitting hardware, devices that have receiving and transmitting hardware capable of bidirectional communication over a bidirectional communication link. Such devices may include: cellular or other communication devices such as personal computers or tablets, having single-line displays, multi-line displays, or cellular or other communication devices without multi-line displays; PCS (Personal Communications Service) that can combine voice, data processing, fax, and / or data communication capabilities; PDA (Personal Digital Assistant) that may include a radio frequency receiver, pager, internet / intranet access, web browser, notepad, calendar, and / or GPS (Global Positioning System) receiver; and conventional laptops and / or handheld computers or other devices that have and / or include radio frequency receivers. As used herein, "client," "terminal," and "terminal device" can be portable, transportable, installed in a means of transportation (air, sea, and / or land), or suitable and / or configured to operate locally and / or in a distributed manner, operating in any other location on Earth and / or in space. "Client," "terminal," and "terminal device" as used herein can also be a communication terminal, an internet access terminal, or a music / video playback terminal, such as a PDA, a MID (Mobile Internet Device), and / or a mobile phone with music / video playback capabilities, or a smart TV, set-top box, etc.

[0085] The hardware referred to by the names "server," "client," and "service node" in this application is essentially an electronic device with the equivalent capabilities of a personal computer. It is a hardware device with the necessary components revealed by the von Neumann architecture, such as a central processing unit (including an arithmetic logic unit and a control unit), memory, input devices, and output devices. The computer program is stored in its memory, and the central processing unit loads the program stored in the secondary storage into the main memory to run it, execute the instructions in the program, and interact with the input and output devices to complete specific functions.

[0086] It should be noted that the concept of "server" used in this application can also be extended to the case of server clusters. Based on the network deployment principles understood by those skilled in the art, the servers should be logically divided. Physically, these servers can be independent of each other but accessible through interfaces, or they can be integrated into a single physical computer or a computer cluster. Those skilled in the art should understand this flexibility and should not use it to constrain the implementation of the network deployment method in this application.

[0087] One or more of the technical features of this application, unless explicitly specified herein, can be deployed on a server and accessed by a client remotely calling the online service interface provided by the server, or can be directly deployed and run on a client for access.

[0088] Unless otherwise specified, the neural network models referenced or potentially referenced in this application may be deployed on a remote server and invoked remotely on the client, or deployed on a client with the capability to invoke directly. In some embodiments, when running on the client, the corresponding intelligence may be acquired through transfer learning in order to reduce the requirements on the client's hardware resources and avoid excessive consumption of the client's hardware resources.

[0089] Unless otherwise specified, all data involved in this application may be stored remotely on a server or on a local terminal device, as long as it is suitable for use by the technical solution of this application.

[0090] Those skilled in the art will understand that although the various methods in this application are described based on the same concept and thus present commonality among them, they can be performed independently unless otherwise specified. Similarly, the various embodiments disclosed in this application are all based on the same inventive concept; therefore, concepts expressed in the same way, as well as concepts that are appropriately changed for convenience but are expressed differently, should be understood equivalently.

[0091] Unless otherwise expressly stated, the various embodiments disclosed in this application can be combined in a cross-cutting manner to flexibly construct new embodiments, as long as such combination does not depart from the inventive spirit of this application and can meet the needs of the prior art or solve a certain deficiency in the prior art. Those skilled in the art should be aware of such modifications.

[0092] Please see Figure 1The autonomous vehicle map matching and positioning method of this application can be implemented based on an autonomous vehicle map matching and positioning system, which may include a sensor data preprocessing module, a sensor status monitoring and initialization module, a coarse positioning module, and a fine positioning module.

[0093] In some embodiments, the computational tasks of the sensor data preprocessing module include GNSS data reception and parsing, extraction of GNSS positioning status and positioning observation results, IMU data reception and pre-integration, and vehicle movement status based on wheel speedometer and vehicle mechanics model.

[0094] In some embodiments, the sensor status monitoring and initialization module includes sensor status monitoring, which mainly refers to the status of GNSS data reception, i.e., the GNSS positioning status caused by signal obstruction. When signal obstruction occurs and accurate GNSS positioning cannot be performed, the GNSS positioning result is marked, and a position derivation calculation relying solely on IMU and wheel speed meter data is performed. When the signal is restored, the GNSS positioning status is restored to valid positioning, and a combined positioning calculation based on GNSS, IMU, and wheel speed meter data is performed again.

[0095] In some embodiments, the coarse positioning module mainly performs relative positioning calculations on the IMU data and wheel speed meter data used for relative positioning.

[0096] In some embodiments, the fine positioning module integrates the IMU-based coarse positioning and the wheel speed meter-based coarse positioning results, recalculates the position deviation, and performs a constraint calculation of the relative displacement to estimate the state of the moving vehicle.

[0097] Based on the above exemplary scenarios, please refer to Figure 2 In one embodiment of the autonomous vehicle map matching and localization method of this application, the method includes:

[0098] Step S10: When the high-precision positioning signal is detected to be unavailable, acquire the linear velocity from the IMU data and wheel speed meter data of the autonomous vehicle.

[0099] The high-precision positioning signal can be a GNSS positioning signal. The autonomous vehicle can respond to sensor status monitoring commands to obtain the current number of satellites and the GNSS positioning signal. If the current number of satellites reaches a preset threshold and the GNSS positioning signal is a fixed solution, the signal state of the high-precision positioning signal is determined to be available; otherwise, the high-precision positioning signal is determined to be unavailable. When the high-precision positioning signal is detected to be unavailable, the linear velocity from the IMU data and wheel speedometer data of the autonomous vehicle is acquired.

[0100] In some embodiments, after the step of determining that the signal state of the high-precision positioning signal is available, the method includes:

[0101] Step S1021: Obtain GNSS high-precision positioning data, IMU data, and wheel speed meter data, and calculate and determine the joint adjustment of the GNSS high-precision positioning data, IMU data, and wheel speed meter data;

[0102] Step S1022: Optimize the rotation and translation speed, IMU bias, and local corresponding points in the autonomous vehicle based on the joint adjustment to perform map matching and localization of the autonomous vehicle.

[0103] By jointly adjusting GNSS high-precision positioning data, IMU data, and wheel speed measurement data, the rotational and translational speeds, IMU biases, and local corresponding points in the system state of autonomous vehicles are optimized to achieve high-precision positioning. Furthermore, robust high-precision positioning in complex scenarios is achieved through the fusion and complementarity of multiple types of sensors.

[0104] Step S20: Perform attitude calculation on the autonomous vehicle, convert the angular velocity data and acceleration data in the IMU data into attitude information of the autonomous vehicle in three-dimensional space, so as to determine the Euler angles of the autonomous vehicle. Based on the rotation matrix of pitch angle and the rotation matrix of roll angle in the Euler angle, calculate and determine the rotation matrix to correct the IMU data, so as to determine the coarse positioning information corresponding to the IMU data.

[0105] After acquiring the linear velocity from the IMU data and wheel speedometer data of the autonomous vehicle, attitude calculation is performed on the autonomous vehicle to measure the IMU carrier coordinate system and the ground coordinate system. Since the IMU coordinate system and the ground coordinate system do not coincide, attitude calculation is required. This attitude calculation converts the IMU's angular velocity and acceleration data into attitude information of the object in three-dimensional space, typically represented as Euler angles or quaternions. The Euler angles include pitch, roll, and yaw angles. The angular velocity and acceleration data from the IMU data are then converted into attitude information of the autonomous vehicle in three-dimensional space to determine the Euler angles of the autonomous vehicle. Based on the rotation matrices of the pitch and roll angles within the Euler angles, a rotation matrix is ​​calculated and determined to correct the IMU data, thereby determining the coarse positioning information corresponding to the IMU data.

[0106] Specifically, the step of calculating and determining the rotation matrix for correcting the IMU data based on the rotation matrix of the pitch angle and the rotation matrix of the roll angle in the Euler angles, in order to determine the coarse positioning information corresponding to the IMU data, includes:

[0107] Step S201: Respond to the coarse positioning command based on IMU data, determine the angular velocity data and acceleration data in the IMU data, and calculate and determine the Euler angles of the autonomous vehicle based on the angular velocity data and acceleration data in the IMU data;

[0108] Step S202: Determine the pitch angle and roll angle in the Euler angles of the autonomous vehicle, and calculate and determine the rotation matrix for correcting the IMU data based on the product between the rotation matrix of the pitch angle and the rotation matrix of the roll angle.

[0109] Step S203: Determine the coarse positioning information corresponding to the IMU data based on the rotation matrix of the IMU data.

[0110] More specifically, the autonomous vehicle can respond to coarse positioning commands based on IMU data, determine the angular velocity and acceleration data in the IMU data, calculate and determine the Euler angles of the autonomous vehicle based on the angular velocity and acceleration data, determine the pitch and roll angles within the Euler angles, calculate and determine the rotation matrix for correcting the IMU data based on the product of the rotation matrices of the pitch and roll angles, and determine the corresponding coarse positioning information based on the rotation matrix of the IMU data. The Z-axis of the IMU is parallel to the ground coordinate system. Since only the true value of gravitational acceleration g exists, only the Z-axis can be calibrated. The X and Y axes require other calibration methods, such as pre-integration based on IMU data from straight-line travel.

[0111] The rotation matrix for the pitch angle is expressed as:

[0112]

[0113] The rotation matrix for the roll angle is expressed as:

[0114]

[0115] The values ​​of p and r are:

[0116]

[0117] Where g is the true value of gravitational acceleration, a x a y These are the components of the current acceleration in the x and y directions, respectively; based on the product of the rotation matrix of the pitch angle and the rotation matrix of the roll angle, the rotation matrix for correcting the IMU data is calculated and determined. The rotation matrix for correcting the IMU data is:

[0118] R = R pitch R roll .

[0119] The rotation matrix for correcting the IMU data can be calculated using the above formula, and the coarse positioning information corresponding to the IMU data can be determined based on the rotation matrix of the IMU data.

[0120] Step S30: Determine the linear velocity in the wheel speed meter data, obtain the angular velocity in the wheel speed meter data based on the preset dual-wheel differential speed model according to the linear velocity, calculate and determine the pre-integration between two frames of wheel speed meter data based on the linear velocity and angular velocity in the wheel speed meter data, so as to determine the coarse positioning information corresponding to the wheel speed meter data.

[0121] After determining the coarse positioning information corresponding to the IMU data, the linear velocity in the wheel speed meter data is determined. Based on the preset dual-wheel differential speed model, the angular velocity in the wheel speed meter data is obtained according to the linear velocity. The pre-integration between two frames of wheel speed meter data is calculated based on the linear velocity and angular velocity in the wheel speed meter data to determine the coarse positioning information corresponding to the wheel speed meter data.

[0122] Further, the step of determining the linear velocity in the wheel speed gauge data and obtaining the angular velocity in the wheel speed gauge data based on the linear velocity using a preset dual-wheel differential speed model includes:

[0123] Step S301: Determine the linear velocity of the right wheel, the linear velocity of the left wheel, and the track width of the rear wheel in the wheel speed meter;

[0124] Step S302: Call the preset dual-wheel differential speed model to calculate and determine the difference between the linear velocity of the right wheel and the linear velocity of the left wheel;

[0125] Step S303: Determine the angular velocity in the wheel speed meter data based on the ratio between the difference and the rear wheel track.

[0126] Specifically, autonomous vehicles or wheeled robots have two or more wheels, and the angular velocity information, in addition to linear velocity, can be obtained by using a two-wheel differential model of the two drive wheels.

[0127] Please see Figure 3 The wheel speed gauge reading is the linear velocity of the right wheel, v. r The linear velocity of the revolver is v l The rear wheel track width d can be measured in advance, such as Figure 3 As shown, according to the dual-wheel differential speed model, when the carrier undergoes displacement in a short period of time, the displacement of the outer wheel can be obtained by multiplying the difference between the linear velocities of the left and right wheels by time. Since the time is very short and the angular change is very small, the rotation angle θ2 with the low-speed wheel as the center can be approximated, thereby calculating the angular velocity ω' of the yaw angle θ1. That is, based on the ratio of the difference between the linear velocities of the right wheel and the left wheel to the wheelbase of the rear wheel, the angular velocity ω' of the yaw angle θ1 in the wheel speed gauge data is calculated and determined. The calculation formula is expressed as follows:

[0128]

[0129] As can be seen from the calculation formula of the above two-wheel differential model, the angular velocity and yaw angle of the autonomous vehicle's forward direction can be calculated by the speed of the left and right wheels.

[0130] Wheel speedometers can only measure the speed of an autonomous vehicle in the forward direction, therefore they only provide measurements for the axis in that direction and lack directional information. Consequently, the measured values ​​exhibit white noise V. n ~N(0,σ 2 ), set the variance of the two axes other than the axis of forward movement to infinity.

[0131] Furthermore, the step of calculating and determining the pre-integral between two frames of wheel speed meter data based on the linear velocity and angular velocity in the wheel speed meter data, in order to determine the coarse positioning information corresponding to the wheel speed meter data, includes:

[0132] Step S3001: Determine the linear velocity and angular velocity in the wheel speed meter data, and calculate and determine the pre-integration between the wheel speed meter data of the i-th frame and the j-th frame based on the linear velocity and angular velocity in the wheel speed meter data according to the preset pre-integration model.

[0133] Step S3002: Based on the pre-integration between the two frames of wheel speed meter data, determine the relative displacement and relative rotation components between the wheel speed meter data of the i-th frame and the j-th frame;

[0134] Step S3003: Determine the coarse positioning information corresponding to the wheel speed meter data based on the relative displacement and relative rotation components between the wheel speed meter data of the i-th frame and the j-th frame.

[0135] Specifically, the pre-integral between two frames of wheel speed meter data, i.e. the relative displacement and relative rotation components between the i-th and j-th frames of wheel speed meter data, can be derived using the linear velocity and angular velocity in the wheel speed meter data.

[0136] The pre-integration model between two frames of wheel speed meter data can be expressed as:

[0137]

[0138] In this embodiment, the same median integration method as the IMU pre-integration is used, integrating from the k-th frame to the (k+1)-th frame. The calculation formula is as follows:

[0139]

[0140] In the above pre-integration model, the relative displacement ΔP and relative rotation ΔR components between two frames of wheel speedometer data are the wheel speedometer pre-integration terms, with the superscript indicating the integration range. ω and v are the angular velocity and acceleration calculated using the median method, respectively, and Δt... k,k+1 The time interval between the kth wheel speedometer measurement and the (k+1)th wheel speedometer measurement.

[0141] Furthermore, to determine the error propagation of the above pre-integration model, and to facilitate the derivation of the covariance propagation formula, the wheel speed gauge pre-integration term needs to be written in the form of a pre-integration error equation, as follows:

[0142]

[0143] Where, n v n ω The following can be calculated using the left and right wheel differential model and the median integral:

[0144]

[0145] Its matrix form is as follows:

[0146]

[0147] In the above calculation formula, [δφ i,k+1 ,δp i,k+1[ ] represents the cumulative error from frame i to frame (k+1), where the error δφ of the rotation term is... i,k+1 It is in the form of Lie algebra.

[0148] Therefore, the covariance propagation (error propagation) of the wheel speed gauge pre-integration can be calculated using the following formula:

[0149]

[0150] Where, Σ i,k+1 and Σ i,k Let Σn be the covariance between the (k+1)th frame and the kth frame. ω n v This represents the covariance of velocity and angular velocity obtained through the dual-wheel difference model.

[0151] Step S40: Based on the preset Kalman filter algorithm, the coarse positioning results corresponding to the IMU data and the coarse positioning results corresponding to the wheel speed meter data are fused together, the position deviation is recalculated, and the constraint calculation of relative displacement is performed to determine the fused positioning information of the autonomous vehicle.

[0152] Based on a preset Kalman filter algorithm, the coarse positioning results corresponding to the IMU data and the coarse positioning results corresponding to the wheel speedometer data are fused together, the position deviation is recalculated, and a constraint calculation of the relative displacement is performed to determine the fused positioning information of the autonomous vehicle.

[0153] The fine positioning calculation based on the Kalman filter algorithm (EKF) applies wheel speed sensor data and IMU data. It is implemented in a tightly coupled manner, using the IMU kinematic model to predict the trajectory, and the wheel speed sensor data to observe the vehicle's velocity in the XY two-dimensional plane.

[0154] The quantities to be calibrated are determined, including the zero bias and scale deviation of the gyroscope in the X, Y, and Z directions, and the zero bias and scale deviation of the accelerometer in the Z direction. This part has been calibrated previously.

[0155] This method integrates information from all wheel speedometer and IMU data, and when the calibration is accurate, it can obtain a trajectory that is close to the true value.

[0156] The steps of fusing the coarse positioning results corresponding to the IMU data and the wheel speedometer data based on a preset Kalman filter algorithm, recalculating the position deviation, and performing constraint calculations on relative displacement to determine the fused positioning information of the autonomous vehicle include:

[0157] Based on a preset Kalman filter algorithm, the coarse positioning results corresponding to the IMU data and the coarse positioning results corresponding to the wheel speed meter data are fused together, including:

[0158] State variables:

[0159] X K =[v x ,v y ,θ] T ,

[0160] Among them, v x ,、v y Let θ be the vehicle's velocity in the X and Y directions, and θ be the angle between the vehicle's direction of motion and the due east direction. The input values ​​are:

[0161] U k =[a′ x ,a′ y ,ω yaw ] T ,

[0162] Among them, at a′ x ,a′ y Let ω be the acceleration of the vehicle relative to the inertial navigation coordinate system. yaw yaw is the angular acceleration;

[0163] Please see Figure 4 Transform the inertial navigation coordinate system to the geographic coordinate system, where O is the origin of the geographic coordinate system, O' is the origin of the IMU coordinate system, v is the vehicle speed, and the angle between the inertial navigation coordinate system and the geographic coordinate system is θ.

[0164] Equations of state:

[0165]

[0166] Observation equation:

[0167] Z = CX k +R,

[0168]

[0169] Among them, X k+1 : The state vector of the system at time k+1;

[0170] The components of velocity v in the x and y directions at time k;

[0171] f(X k U k+1 ): Nonlinear state transition function;

[0172] U k+1 : Control input vector at time k+1;

[0173] Q: State covariance matrix;

[0174] Since there are no observations in the direction of motion, the Z matrix only contains observations in the velocity direction. Q and R are the state covariance matrix and the observation covariance matrix, respectively. The state equation f is nonlinear, therefore a Kalman filter algorithm is needed to approximate the state equation nonlinearly. The state equation f at point U... k-1 Taylor expansion yields:

[0175] f(X k U k )≈J(U k-1 U k (X) k-1 -U k-1 )+f(U k-1 U k ),

[0176] Where matrix J(U) k-1 U k Let f be the Jacobian matrix of f;

[0177] Predicting covariance estimation:

[0178] P k =J T P k-1 J+Q,

[0179] Kalman Gain Update:

[0180] K = PC T / (C T PC+R),

[0181] Status Update:

[0182]

[0183] Status Update:

[0184] P = (I - KC)P.

[0185] Based on the aforementioned Kalman gain update, state update, and state update, the coarse positioning results corresponding to the IMU data and the coarse positioning results corresponding to the wheel speedometer data are fused together, the position deviation is recalculated, and a constraint calculation of the relative displacement is performed to determine the fused positioning information of the autonomous vehicle.

[0186] Step S50: Determine the vehicle movement trajectory of the autonomous vehicle based on the fused positioning information, and perform map matching or visual matching based on the vehicle movement trajectory to complete the map matching and positioning of the autonomous vehicle.

[0187] After determining the fused positioning information of the autonomous vehicle, the vehicle movement trajectory of the autonomous vehicle is determined based on the fused positioning information, and map matching or visual matching is performed based on the vehicle movement trajectory to complete the map matching and positioning of the autonomous vehicle.

[0188] Compared to existing technologies, this application addresses the limitations of relying on a single sensor for positioning, which is often subject to various constraints, such as unstable or missing GPS signals, laser point cloud drift in scenarios like long straight corridors or tunnels, instantaneous wheel speed measurement anomalies caused by tire slippage, and the fact that wheel speed measurement data is often treated as time-series data, with motion state derivation and calculation performed frame by frame. This calculation model fails to fully consider the differences in motion models of various systems. This application offers advantages including, but not limited to, the following:

[0189] Firstly, when the high-precision positioning signal is detected to be unavailable, this application acquires the linear velocity from the IMU data and wheel speedometer data of the autonomous vehicle; performs attitude calculation on the autonomous vehicle, converting the angular velocity and acceleration data from the IMU data into attitude information of the autonomous vehicle in three-dimensional space to determine the Euler angles of the autonomous vehicle; based on the rotation matrix of the pitch angle and the rotation matrix of the roll angle in the Euler angles, calculates and determines the rotation matrix of the IMU data to determine the coarse positioning information corresponding to the IMU data; determines the linear velocity from the wheel speedometer data, and based on the preset dual-wheel differential speed model, obtains the angular velocity from the wheel speedometer data according to the linear velocity, and then calculates and determines the attitude information of the autonomous vehicle in three-dimensional space to determine the Euler angles of the autonomous vehicle; based on the rotation matrix of the pitch angle and the rotation matrix of the roll angle in the Euler angles, calculates and determines the rotation matrix of the IMU data to correct the IMU data to determine the coarse positioning information corresponding to the IMU data; determines the linear velocity from the wheel speedometer data, and based on the linear velocity from the wheel speedometer data, obtains the angular velocity from the wheel speedometer data to determine the attitude information of the autonomous vehicle in three-dimensional space ... Linear velocity and angular velocity calculations determine the pre-integration between two frames of wheel speed meter data to determine the coarse positioning information corresponding to the wheel speed meter data. Based on a preset Kalman filter algorithm, the coarse positioning results corresponding to the IMU data and the coarse positioning results corresponding to the wheel speed meter data are fused together, the position deviation is recalculated, and the relative displacement is constrained to determine the fused positioning information of the autonomous vehicle. The vehicle movement trajectory of the autonomous vehicle is determined according to the fused positioning information, and map matching or visual matching is performed based on the vehicle movement trajectory. This application can achieve high-precision map matching and positioning of autonomous vehicles without the need for high-cost, high-precision inertial navigation equipment when GNSS signals are unavailable.

[0190] Secondly, by combining GNSS data, IMU data, and wheel speed measurement, high-precision and high-reliability positioning results are provided for autonomous driving and vehicle-to-everything (V2X) communication, significantly improving the data quality of the fused positioning results. This provides a high-quality data foundation for autonomous vehicles to perform environmental perception calculations, such as map matching and visual matching.

[0191] Third, by combining dual wheel speedometer data with the vehicle motion model, a wheel speedometer pre-integration method based on the differential speed of the dual wheel speedometers was derived and calculated, which integrates multiple wheel speedometer measurements between two frames into a single observation of the vehicle's relative motion state.

[0192] Fourth, by combining GNSS, IMU, and wheel speed meter adjustment, the system state, including rotation and translation speed, IMU bias, and local corresponding points, is optimized to achieve high-precision positioning. Furthermore, robust high-precision positioning in complex scenarios is achieved through multi-sensor fusion and complementarity.

[0193] Please see Figure 5 This application provides an autonomous vehicle map matching and positioning device, comprising a data acquisition module 1100, an IMU coarse positioning module 1200, a wheel speed meter coarse positioning module 1300, a fusion positioning module 1400, and a map matching and positioning module 1500, for one of the purposes of this application. The data acquisition module 1100 is configured to acquire the linear velocity from the IMU data and wheel speed meter data of the autonomous vehicle when a high-precision positioning signal is detected as unavailable. The IMU coarse positioning module 1200 is configured to perform attitude calculation on the autonomous vehicle, converting the angular velocity and acceleration data from the IMU data into attitude information of the autonomous vehicle in three-dimensional space to determine the Euler angles of the autonomous vehicle. Based on the rotation matrix of the pitch angle and the rotation matrix of the roll angle in the Euler angles, a rotation matrix for correcting the IMU data is calculated to determine the coarse positioning information corresponding to the IMU data. The wheel speed meter coarse positioning module 1300 is configured to determine the linear velocity from the wheel speed meter data based on a preset dual-wheel differential speed model. The linear velocity is obtained from the angular velocity in the wheel speed meter data. Based on the linear velocity and angular velocity in the wheel speed meter data, the pre-integral between two frames of wheel speed meter data is calculated to determine the coarse positioning information corresponding to the wheel speed meter data. The fusion positioning module 1400 is configured to fuse the coarse positioning results corresponding to the IMU data and the coarse positioning results corresponding to the wheel speed meter data based on a preset Kalman filter algorithm, recalculate the position deviation, and perform a constraint calculation of relative displacement to determine the fusion positioning information of the autonomous vehicle. The map matching positioning module 1500 is configured to determine the vehicle movement trajectory of the autonomous vehicle based on the fusion positioning information, and perform map matching or visual matching based on the vehicle movement trajectory to complete the map matching positioning of the autonomous vehicle.

[0194] Based on any embodiment of this application, please refer to Figure 6 Another embodiment of this application also provides an electronic device, which can be implemented by a computer device, such as... Figure 6The diagram shows the internal structure of a computer device. The computer device includes a processor, a computer-readable storage medium, a memory, and a network interface connected via a system bus. The computer-readable storage medium stores an operating system, a database, and computer-readable instructions. The database may store a sequence of control information. When the computer-readable instructions are executed by the processor, the processor can implement a map matching and positioning method for autonomous vehicles. The processor of the computer device provides computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can execute the map matching and positioning method for autonomous vehicles of this application. The network interface of the computer device is used for communication with a terminal. Those skilled in the art will understand that… Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0195] In this embodiment, the processor is used to execute... Figure 5 The system contains the specific functions of each module and its sub-modules, and the memory stores the program code and various data required to execute these modules or sub-modules. A network interface is used for data transmission between the user terminal and the server. In this embodiment, the memory stores the program code and data required to execute all modules / sub-modules in the autonomous vehicle map matching and positioning device of this application, and the server can call the server's program code and data to execute the functions of all sub-modules.

[0196] This application also provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the autonomous vehicle map matching and positioning method described in any embodiment of this application.

[0197] This application also provides a computer program product, including a computer program / instructions that, when executed by one or more processors, implement the steps of the autonomous vehicle map matching and positioning method described in any embodiment of this application.

[0198] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0199] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

[0200] In summary, this application enables high-precision, real-time sensor fusion positioning for autonomous driving vehicles without the need for expensive, high-precision inertial navigation equipment, even when GNSS signals are unavailable.

Claims

1. An automatic driving vehicle sensor fusion positioning method, characterized in that, The method comprises the following steps: When it is detected that the high-precision positioning signal is in an unusable state, acquiring IMU data and linear velocity in wheel speed data of the autonomous vehicle; Performing attitude calculation on the autonomous vehicle, converting angular velocity data and acceleration data in the IMU data into attitude information of the autonomous vehicle in a three-dimensional space to determine Euler angles of the autonomous vehicle, calculating a rotation matrix for correcting the IMU data based on a rotation matrix of a pitch angle and a rotation matrix of a roll angle in the Euler angles, and determining coarse positioning information corresponding to the IMU data; Determining linear velocity in the wheel speed data, acquiring angular velocity in the wheel speed data based on a preset double-wheel differential model according to the linear velocity, and calculating and determining pre-integration between two frames of wheel speed data according to the linear velocity and the angular velocity in the wheel speed data to determine coarse positioning information corresponding to the wheel speed data; Fusing the coarse positioning result corresponding to the IMU data and the coarse positioning result corresponding to the wheel speed data based on a preset Kalman filtering algorithm, recalculating position deviation, and performing constraint calculation of relative displacement to determine fusion positioning information of the autonomous vehicle; Determining a vehicle movement trajectory of the autonomous vehicle according to the fusion positioning information, and performing map matching or visual matching based on the vehicle movement trajectory to complete map matching positioning of the autonomous vehicle.

2. The method of claim 1, wherein, The step of calculating and determining a rotation matrix for correcting the IMU data based on a rotation matrix of a pitch angle and a rotation matrix of a roll angle in the Euler angles to determine coarse positioning information corresponding to the IMU data comprises: In response to a coarse positioning instruction based on the IMU data, determining angular velocity data and acceleration data in the IMU data, and calculating and determining Euler angles of the autonomous vehicle according to the angular velocity data and the acceleration data in the IMU data; Determining a pitch angle and a roll angle in the Euler angles of the autonomous vehicle, and calculating and determining a rotation matrix for correcting the IMU data based on a product between a rotation matrix of the pitch angle and a rotation matrix of the roll angle; Determining coarse positioning information corresponding to the IMU data according to the rotation matrix of the IMU data.

3. The method of claim 1, wherein, The step of determining linear velocity in the wheel speed data and acquiring angular velocity in the wheel speed data based on a preset double-wheel differential model according to the linear velocity comprises: Determining right wheel linear velocity, left wheel linear velocity, and rear wheel track in the wheel speed data; Calling a preset double-wheel differential model to calculate and determine a difference value between the right wheel linear velocity and the left wheel linear velocity; Determining angular velocity in the wheel speed data based on a ratio between the difference value and the rear wheel track.

4. The method of claim 3, wherein, The step of calculating and determining pre-integration between two frames of wheel speed data according to linear velocity and angular velocity in the wheel speed data to determine coarse positioning information corresponding to the wheel speed data comprises: Determining linear velocity and angular velocity in the wheel speed data, and calculating and determining pre-integration between the i-th frame and the j-th frame of two frames of wheel speed data based on a preset pre-integration model according to the linear velocity and the angular velocity in the wheel speed data; determining a relative displacement and a relative rotation component between the i-th frame and the j-th frame of wheel speed meter data based on pre-integration between the two frames of wheel speed meter data; determining coarse positioning information corresponding to the wheel speed meter data based on the relative displacement and the relative rotation component between the i-th frame and the j-th frame of wheel speed meter data.

5. The method of claim 1, wherein, Before the step of acquiring IMU data and linear velocity in wheel speed meter data of the autonomous vehicle when it is detected that the high-precision positioning signal is in an unusable state, the method comprises: in response to a sensor state monitoring instruction, acquiring a number of satellites and a GNSS positioning signal of the autonomous vehicle at a current time; in a case where the number of satellites at the current time reaches a preset number threshold and the GNSS positioning signal is a fixed solution, determining that a signal state of the high-precision positioning signal is a usable state; otherwise, determining that the high-precision positioning signal is in an unusable state.

6. The method of claim 5, wherein, After the step of determining that the signal state of the high-precision positioning signal is in a usable state, the method comprises: acquiring GNSS high-precision positioning data, IMU data and wheel speed meter data, and calculating joint adjustment of the GNSS high-precision positioning data, the IMU data and the wheel speed meter data; based on the joint adjustment, optimizing rotation and translation speed, IMU bias and local homonym in the autonomous vehicle to perform map matching positioning of the autonomous vehicle.

7. The automatic vehicle map-matching positioning method according to any one of claims 1 to 6, characterized in that, The step of fusing the coarse positioning result corresponding to the IMU data and the coarse positioning result corresponding to the wheel speed meter data based on a preset Kalman filtering algorithm, recalculating a position deviation, and performing constraint calculation of relative displacement to determine fusion positioning information of the autonomous vehicle, comprises: fusing the coarse positioning result corresponding to the IMU data and the coarse positioning result corresponding to the wheel speed meter data based on a preset Kalman filtering algorithm, comprising: state quantity: X K = [v x ,v y ,θ] T , where v x , v y are the velocities of the vehicle in the X, Y directions, θ is the angle between the direction of motion of the vehicle and the positive East direction, and the inputs are: U k = [a ′ x ,a ′ y ,ω yaw ] T , wherein a ′ x a ′ y is the acceleration of the vehicle with respect to the inertial coordinate system, ω yaw is the yaw angular acceleration; converting the inertial navigation coordinate system to the geographic coordinate system, O is the origin of the geographic coordinate system, O' is the origin of the IMU coordinate system, v is the vehicle speed, and the angle between the inertial navigation coordinate system and the geographic coordinate system is θ; state equation: where X k+1 : system state vector at time k + 1 v(k) velocity at time k in x, y directions; f(X k ,U k+1 ): nonlinear state transition function; U k+1 : control input vector at time instant k+1; Q: state covariance matrix; observation equation: Z = CX k + R, Q, R are state covariance matrix and observation covariance matrix, the state equation f is nonlinear, the Kalman filtering algorithm is used for nonlinear approximation of the state equation, the state equation f is Taylor expanded at point U k-1 to obtain: f(X k ,U k )≈J(U k-1 ,U k )(X k-1 -U k-1 )+f(U k-1 ,U k ), where the matrix J(U k-1 ,U k ) is the Jacobian matrix of f; prediction covariance estimation: P k = J T P k-1 J + Q, Kalman gain update: K = PC T / (C T PC+R), state update: X k = X k - + K(Z - CX k - ), prediction covariance update: P = (I-KC)P.

8. A map matching and positioning device for autonomous vehicles, characterized in that, comprising: a data acquisition module configured to acquire IMU data and linear velocity in wheel speed meter data of the autonomous vehicle when it is detected that the high-precision positioning signal is in an unusable state; an IMU coarse positioning module configured to perform attitude solution on the autonomous vehicle, convert angular velocity data and acceleration data in the IMU data into attitude information of the autonomous vehicle in a three-dimensional space, determine Euler angles of the autonomous vehicle, calculate a rotation matrix for correcting the IMU data based on a rotation matrix of a pitch angle and a rotation matrix of a roll angle in the Euler angles, and determine coarse positioning information corresponding to the IMU data. The wheel speed meter coarse positioning module is configured to determine the linear velocity in the wheel speed meter data, obtain the angular velocity in the wheel speed meter data based on a preset double-wheel differential model according to the linear velocity, calculate the pre-integral between two frames of wheel speed meter data according to the linear velocity and the angular velocity in the wheel speed meter data, and determine the coarse positioning information corresponding to the wheel speed meter data. The fusion positioning module is configured to fuse the coarse positioning result corresponding to the IMU data and the coarse positioning result corresponding to the wheel speed meter data based on a preset Kalman filtering algorithm, recalculate the position deviation, and perform constraint calculation on the relative displacement, so as to determine the fusion positioning information of the autonomous vehicle. The map matching positioning module is configured to determine the vehicle movement trajectory of the autonomous vehicle according to the fusion positioning information, perform map matching or visual matching based on the vehicle movement trajectory, and complete the map matching positioning of the autonomous vehicle.

9. An electronic device comprising a central processing unit and a memory, characterized in that The central processing unit is configured to call and run a computer program stored in the memory to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable instructions store a computer program implemented according to the method of any one of claims 1 to 7, and when the computer program is called and run by a computer, the steps included in the corresponding method are performed.

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