Fault location detection method, device, equipment, storage medium and program product

Abstract

The present disclosure provides a positioning fault detection method, device, equipment, storage medium and program product. The method includes: determining the lateral error covariance of the position and posture information of the moving target in a preset time period based on the information measured by the inertial measurement unit and the wheel speed sensor set on the moving target; determining the lateral error amount between the position and posture information of the moving target based on the first posture information and the second posture information of the moving target at the target time, wherein the target time is the end time of the preset time period, the first posture information is measured by the positioning sensor set on the moving target, and the second posture information is determined by the inertial measurement unit and the wheel speed sensor; and performing fault detection on the positioning sensor based on the ratio of the lateral error amount to the lateral error covariance. The present disclosure can correctly detect the fault of the positioning sensor, so that the fault detection is not affected by the turning of the moving target.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the fields of robotics and autonomous driving technology, and in particular to a positioning fault detection method, a positioning fault detection device, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] With the development of robotics and autonomous driving technologies, positioning is an important condition for ensuring the normal operation of mobile robots or autonomous vehicles, whether for mobile robots or autonomous vehicles. Therefore, correctly detecting the faults of sensors used for positioning, such as sudden jumps, and correctly reporting them to the fault system of the mobile robot or autonomous vehicle is a necessary condition for ensuring the safe operation of mobile robots or autonomous vehicles. Summary of the invention

[0003] The embodiments of the present disclosure provide a method for locating fault detection, a device for locating fault detection, an electronic device, a computer-readable storage medium, and a computer program product, which can solve or partially solve the above-mentioned deficiencies in the prior art or other deficiencies in the prior art.

[0004] According to a first aspect of the present disclosure, a positioning fault detection method is provided, comprising: determining a lateral error covariance of the posture information of the moving target in a preset time period based on information measured by an inertial measurement unit and a wheel speed sensor arranged on the moving target; determining a lateral error amount between the posture information of the moving target based on first posture information and second posture information of the moving target at a target moment, wherein the target moment is an end moment of the preset time period, the first posture information is measured by a positioning sensor arranged on the moving target, and the second posture information is determined by the inertial measurement unit and the wheel speed sensor; and performing fault detection on the positioning sensor based on a ratio of the lateral error amount to the lateral error covariance.

[0005] In one embodiment of the present disclosure, determining the lateral error covariance of the position and posture information of the moving target in a preset time period includes: determining the error covariance accumulated by the position and posture information of the moving target in the preset time period through Kalman filtering based on the information measured by the inertial measurement unit and the wheel speed sensor; and determining the lateral error covariance based on the error covariance through the posture information of the moving target at the target moment determined by the inertial measurement unit.

[0006] In one embodiment of the present disclosure, the error covariance accumulated in the preset time period of the posture information of the moving target is determined by Kalman filtering based on the information measured by the inertial measurement unit and the wheel speed sensor, including: determining an error differential equation based on the noise and measured information of the inertial measurement unit and the wheel speed sensor, wherein the error includes position error, heading angle error, wheel speed coefficient error and gyroscope bias error; and discretizing the error differential equation and performing Kalman filtering on the discretized error differential equation to obtain the error covariance accumulated in the preset time period of the posture information of the moving target.

[0007] In one embodiment of the present disclosure, the determining of the error differential equation based on the noise and measured information of the inertial measurement unit and the wheel speed sensor includes: determining the gyroscope angular velocity noise and the gyroscope angular velocity bias noise based on the parameters of the inertial measurement unit; determining the wheel speed noise and the wheel speed coefficient noise based on the parameters of the wheel speed sensor; and determining the error differential equation based on the angular velocity measured by the inertial measurement unit, the speed measured by the wheel speed sensor, the gyroscope angular velocity noise, the gyroscope angular velocity bias noise, the wheel speed noise and the wheel speed coefficient noise.

[0008] In one embodiment of the present disclosure, the error differential equation is determined based on the angular velocity measured by the inertial measurement unit, the speed measured by the wheel speed sensor, the gyroscope angular velocity noise, the gyroscope angular velocity bias noise, the wheel speed noise and the wheel speed coefficient noise, including: determining the wheel speed coefficient error differential equation based on the wheel speed coefficient noise; determining the gyroscope bias error differential equation based on the gyroscope angular velocity bias noise; determining the heading angle error differential equation based on the gyroscope angular velocity noise and the gyroscope bias error; and determining the position error differential equation based on the angular velocity measured by the inertial measurement unit, the speed measured by the wheel speed sensor, the gyroscope angular velocity noise, the wheel speed noise, the wheel speed coefficient error and the heading angle error.

[0009] In one embodiment of the present disclosure, the lateral error covariance is determined based on the error covariance and the posture information of the moving target at the target moment determined by the inertial measurement unit, including: performing singular value decomposition on the position error covariance matrix in the error covariance to obtain two eigenvalues ​​and corresponding eigenvectors; determining a heading vector based on the heading angle of the moving target at the target moment determined by the inertial measurement unit; and determining the lateral error covariance based on the two eigenvalues ​​and the corresponding eigenvectors and the heading vector.

[0010] In one embodiment of the present disclosure, determining the lateral error covariance based on the two eigenvalues ​​and the corresponding eigenvectors and the heading vector includes: constructing an ellipse equation based on the two eigenvalues ​​and the corresponding eigenvectors; determining a straight line equation of the lateral vector perpendicular to the heading vector based on the heading vector and the eigenvector; and determining the lateral error covariance based on the ellipse equation and the straight line equation.

[0011] In one embodiment of the present disclosure, a lateral error amount between the posture information of the moving target is determined based on the first posture information and the second posture information of the moving target at the target moment, including: acquiring the first posture information of the moving target at the target moment determined by the positioning sensor; determining the second posture information of the moving target at the target moment based on information measured by the inertial measurement unit and the wheel speed sensor; and determining the lateral error amount based on the first posture information and the second posture information.

[0012] In one embodiment of the present disclosure, determining the lateral error amount based on the first posture information and the second posture information includes: determining the error amount between the first posture information and the second posture information; and based on the error amount, determining the lateral error amount through the posture information of the moving target at the target moment determined by the inertial measurement unit.

[0013] In one embodiment of the present disclosure, the positioning fault detection method also includes: obtaining the starting posture information of the moving target determined by the positioning sensor at the beginning of the preset time period; determining posture compensation information based on the posture information of the moving target at the first moment in the preset time period determined by the positioning sensor and the posture information of the moving target at the first moment determined by the inertial measurement unit and the wheel speed sensor; compensating the posture information in the starting posture information based on the posture compensation information to obtain the starting posture information of the inertial measurement unit and the wheel speed sensor.

[0014] In one embodiment of the present disclosure, the posture compensation information is determined based on the posture information of the moving target at the first moment in the preset time period determined by the positioning sensor and the posture information of the moving target at the first moment determined by the inertial measurement unit and the wheel speed sensor, including: determining a first vector based on the posture information of the moving target at the first moment in the preset time period determined by the positioning sensor and the starting posture information; determining a second vector based on the posture information of the moving target at the first moment determined by the inertial measurement unit and the wheel speed sensor and the starting posture information; and determining an angle between the first vector and the second vector; compensating the posture information in the starting posture information based on the posture compensation information to obtain the starting posture information of the inertial measurement unit and the wheel speed sensor, including: compensating the heading angle in the starting posture information by the angle to obtain the starting posture information of the inertial measurement unit and the wheel speed sensor.

[0015] In one embodiment of the present disclosure, the fault detection of the positioning sensor based on the ratio of the lateral error amount to the lateral error covariance includes: determining whether the ratio of the lateral error amount to the lateral error covariance is greater than a preset threshold; in response to the ratio of the lateral error amount to the lateral error covariance being greater than the preset threshold, determining that a lateral jump fault has occurred in the positioning sensor.

[0016] According to the second aspect of the present disclosure, a positioning fault detection device is provided, including: a lateral error covariance determination module, configured to determine the lateral error covariance of the posture information of the moving target in a preset time period based on information measured by an inertial measurement unit and a wheel speed sensor arranged on the moving target; a lateral error amount determination module, configured to determine the lateral error amount between the posture information of the moving target based on first posture information and second posture information of the moving target at a target moment, wherein the target moment is the end moment of the preset time period, the first posture information is measured by the positioning sensor arranged on the moving target, and the second posture information is determined by the inertial measurement unit and the wheel speed sensor; and a fault detection module, configured to perform fault detection on the positioning sensor based on the ratio of the lateral error amount to the lateral error covariance.

[0017] The electronic device provided according to the third aspect of the present disclosure may include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the positioning fault detection method described in the first aspect of the present disclosure.

[0018] The computer-readable storage medium provided according to the fourth aspect of the present disclosure includes a computer program, and when the computer program is executed by a processor, the positioning fault detection method described in the first aspect of the present disclosure is implemented.

[0019] The computer program product provided according to the fifth aspect of the present disclosure stores a computer program, and when the computer program is executed by a processor, the positioning fault detection method described in the first aspect of the present disclosure is implemented.

[0020] According to the positioning fault detection method, positioning fault detection device, electronic device, computer-readable storage medium and computer program product provided in the embodiments of the present disclosure, fault detection of the positioning sensor is performed through two relatively commonly used and highly stable sensors, namely an inertial measurement unit and a wheel speed sensor. The fault of the positioning sensor can be correctly detected, and the fault detection is not affected by the turning of the moving target.

[0021] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Other features, objects and advantages of the present disclosure will become more apparent by reading the detailed description of the non-limiting embodiments made with reference to the following drawings. The drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. Among them:

[0023] Figure 1 is a flow chart of a positioning fault detection method according to an embodiment of the present disclosure;

[0024] Figure 2 is a flow chart for determining lateral error covariance according to some embodiments of the present disclosure;

[0025] Figure 3 is a flow chart for determining error covariance according to some embodiments of the present disclosure;

[0026] Figure 4 is a flow chart for determining lateral error covariance through error covariance according to some embodiments of the present disclosure;

[0027] Figure 5 is a schematic diagram of a trajectory of a vehicle in a global coordinate system according to an embodiment of the present disclosure;

[0028] Figure 6 is a schematic diagram of determining a lateral error covariance according to an embodiment of the present disclosure;

[0029] Figure 7is a flow chart for determining a lateral error amount according to some embodiments of the present disclosure;

[0030] Figure 8 is a flowchart for aligning the starting pose according to some embodiments of the present disclosure;

[0031] Fig. 9 is a flow chart of a positioning fault detection method according to an embodiment of the present disclosure;

[0032] Figures 10 to 13 is a schematic diagram of experimental comparison results of a positioning fault detection method according to an embodiment of the present disclosure;

[0033] Fig.14 is a block diagram of a positioning fault detection device according to an embodiment of the present disclosure;

[0034] Fig.15 is a block diagram of an example electronic device that can be used to implement embodiments of the present disclosure. DETAILED DESCRIPTION

[0035] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0036] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0037] An embodiment of the present disclosure provides a positioning fault detection method 1000 .

[0038] Figure 1 FIG. 1 is a flow chart of a method 1000 for detecting a positioning fault according to an embodiment of the present disclosure. Figure 1 As shown, the positioning fault detection method 1000 may include the following steps:

[0039] S100, determining a lateral error covariance of the position and posture information of the moving target in a preset time period based on information measured by an inertial measurement unit and a wheel speed sensor arranged on the moving target.

[0040] S200. Determine the lateral error amount between the posture information of the moving target based on the first posture information and the second posture information of the moving target at the target time, wherein the target time is the end time of a preset time period, the first posture information is determined by a positioning sensor arranged on the moving target, and the second posture information is determined by an inertial measurement unit and a wheel speed sensor.

[0041] S300 , performing fault detection on the positioning sensor based on the ratio of the lateral error amount to the lateral error covariance.

[0042] The positioning fault detection method 1000 of the embodiment of the present disclosure can be executed by a positioning fault detection device. The positioning fault detection device can be hardware or software, or can include both hardware and software. The embodiment of the present disclosure does not limit this. The positioning fault detection device is set on the moving target. The embodiment of the present disclosure does not limit the type of the moving target. For example, the moving target can be an autonomous driving vehicle or a mobile robot. The inertial measurement unit (IMU), wheel speed sensor and positioning sensor of the embodiment of the present disclosure are set on the moving target, and can be existing sensors on the moving target. Among them, the IMU is used to measure the acceleration and angular velocity of the moving target, and the wheel speed sensor is used to measure the speed of the moving target. According to the acceleration, angular velocity and speed of the moving target, the position and posture information of the moving target can be determined. The positioning sensor can be, for example, a laser radar and / or a camera, etc., and the embodiment of the present disclosure does not limit this. The laser radar is used to collect point cloud information of the surrounding environment of the moving target, and the position and posture information of the moving target can be determined according to the point cloud information of the surrounding environment of the moving target. The camera is used to collect image information of the surrounding environment of the moving target, and the position and posture information of the moving target can be determined according to the image information of the surrounding environment of the moving target.

[0043] In the present disclosure, the direction in which the moving target moves is taken as the longitudinal direction, and the direction perpendicular to the moving target's direction in the plane is taken as the lateral direction. In the process of the moving target moving forward, the error covariance of the position and posture information of the moving target determined by the positioning sensor will expand in the lateral direction as the moving target moves forward, so that the lateral error covariance is greater than the longitudinal error covariance. For example, when the vehicle moves along the y-axis direction of the global coordinate system, the y-axis direction is the longitudinal direction, and the x-axis direction is the lateral direction. The error covariance of the position and posture information of the vehicle determined by the positioning sensor will expand along the x-axis direction of the global coordinate system as the vehicle moves forward. When the vehicle turns and moves along the x-axis direction of the global coordinate system, the x-axis direction is the longitudinal direction, and the y-axis direction is the lateral direction. The error covariance of the position and posture of the vehicle determined by the positioning sensor will expand along the y-axis direction of the global coordinate system as the vehicle moves forward.

[0044] Since the vehicle turns, the lateral and longitudinal directions change. For example, in the above example, the y-axis direction changes from longitudinal to lateral, and the x-axis direction changes from lateral to longitudinal. The lateral error covariance and longitudinal error covariance will also change accordingly. For example, in the above example, the error covariance in the x-axis direction changes from lateral error covariance to longitudinal error covariance, and the error covariance in the y-axis direction changes from longitudinal error covariance to lateral error covariance. In the above example, since the lateral error covariance in the x-axis direction is greater than the longitudinal error covariance in the y-axis direction before turning, the longitudinal error covariance in the x-axis direction is greater than the lateral error covariance in the y-axis direction after turning. That is to say, before turning, the lateral error covariance is greater than the longitudinal error covariance, and after turning, the longitudinal error covariance is greater than the lateral error covariance. Turning causes the lateral error covariance and the longitudinal error covariance to be exchanged, so that the longitudinal error covariance changes from being less than the lateral error covariance to being greater than the lateral error covariance. The longitudinal error covariance jumps as the vehicle turns. This jump in error covariance is caused by the change in vehicle posture, not by the failure of the positioning sensor. When fault detection of the positioning sensor is performed, this jump in error covariance caused by the change in vehicle posture needs to be excluded from the failure of the positioning sensor.

[0045] The positioning fault detection method 1000 of the embodiment of the present disclosure determines the lateral error covariance of the position and posture information of the moving target in a preset time period according to the information measured by the IMU and the wheel speed sensor, determines the lateral error amount between the position and posture information of the moving target according to the first position information of the moving target measured by the positioning sensor at the target time and the second position information of the moving target determined by the IMU and the wheel speed sensor at the target time, and performs fault detection on the positioning sensor according to the ratio of the lateral error amount to the lateral error covariance. By using the lateral error amount between the position and posture information of the moving target measured by the positioning sensor and the position and posture information of the moving target determined by the IMU and the wheel speed sensor, and the lateral error covariance of the position and posture information of the moving target determined by the IMU and the wheel speed sensor, the ratio of the two will remain within a range as the vehicle posture changes, to perform fault detection on the positioning sensor, the jump of the error covariance caused by the change of the vehicle posture can be excluded from the fault of the positioning sensor, and the jump of the error covariance caused by the fault of the positioning sensor, such as the lateral jump fault, can be detected.

[0046] From the above, it can be seen that the embodiment of the present disclosure uses IMU and wheel speed sensor, two relatively commonly used and highly stable sensors, to perform fault detection on the positioning sensor, which can correctly detect the fault of the positioning sensor, so that the fault detection is not affected by the turning of the moving target, and the calculation is simple, the cost is low, and it is not easily affected by weather, environmental and other factors.

[0047] It should be understood that the steps shown in method 1000 are not exclusive, and other steps may be performed before, after, or between any of the steps shown. In addition, some of the steps shown may be performed simultaneously, or may be performed in different steps. Figure 1 Executed in the order shown.

[0048] The following is a detailed description of each step in the positioning fault detection method 1000 according to an embodiment of the present disclosure.

[0049] Figure 2 FIG. 4 shows a flow chart for determining the lateral error covariance according to some embodiments of the present disclosure. Figure 2 As shown, the lateral error covariance of the position and posture information of the moving target in the preset time period determined in step S100 may include the following steps:

[0050] S110, based on the information measured by the inertial measurement unit and the wheel speed sensor, determine the error covariance accumulated in the preset time period of the position and posture information of the moving target through Kalman filtering.

[0051] S120 , based on the error covariance, determine the lateral error covariance by using the posture information of the moving target at the target time determined by the inertial measurement unit.

[0052] In some optional embodiments of the present disclosure, the execution subject may first determine the accumulated error covariance of the position and posture information of the moving target determined by the inertial measurement unit and the wheel speed sensor in a preset time period through Kalman filtering according to the information measured by the inertial measurement unit and the wheel speed sensor, and then obtain the lateral error covariance through the attitude information of the moving target determined by the inertial measurement unit at the target time according to the error covariance determined by the Kalman filtering. Among them, the Kalman filter performs state estimation by fusing the dynamic model and measurement data of the system. It adopts a recursive method to continuously update the estimated value while taking into account past estimates and new measurements. Optionally, the position error, heading angle error, wheel speed coefficient error and gyroscope bias error can be used as state quantities, and the state differential equation can be determined according to the noise and measured information of the IMU and the wheel speed sensor. By discretizing the state differential equation and calculating the discretized state differential equation through Kalman filtering, the accumulated error covariance of the position and posture information of the moving target determined by the IMU and the wheel speed sensor in the preset time period is obtained.

[0053] Therefore, if Figure 3As shown, step S110 determines the error covariance accumulated in the preset time period of the posture information of the moving target through Kalman filtering based on the information measured by the inertial measurement unit and the wheel speed sensor, and can also include: S111, determining the error differential equation based on the noise and measured information of the inertial measurement unit and the wheel speed sensor, wherein the error includes position error, heading angle error, wheel speed coefficient error and gyroscope bias error; S112, discretizing the error differential equation, and performing Kalman filtering on the discretized error differential equation to obtain the error covariance accumulated in the preset time period of the posture information of the moving target.

[0054] In some optional examples, the noise of the IMU and the wheel speed sensor may include: gyroscope angular velocity noise, gyroscope angular velocity bias noise, wheel speed noise and wheel speed coefficient noise. The inherent noise of these IMUs and wheel speed sensors can be determined according to the parameters of the IMU and the wheel speed sensor, for example, by querying the noise parameters in the data sheet of the IMU and the wheel speed sensor, and the embodiments of the present disclosure are not limited to this. Therefore, S111 determines the error differential equation based on the noise and measured information of the inertial measurement unit and the wheel speed sensor, which may include: determining the gyroscope angular velocity noise and the gyroscope angular velocity bias noise based on the parameters of the inertial measurement unit; determining the wheel speed noise and the wheel speed coefficient noise based on the parameters of the wheel speed sensor; and determining the error differential equation based on the angular velocity measured by the inertial measurement unit, the speed measured by the wheel speed sensor, the gyroscope angular velocity noise, the gyroscope angular velocity bias noise, the wheel speed noise and the wheel speed coefficient noise.

[0055] Among them, determining the error differential equation based on the angular velocity measured by the inertial measurement unit, the speed measured by the wheel speed sensor, the gyroscope angular velocity noise, the gyroscope angular velocity bias noise, the wheel speed noise and the wheel speed coefficient noise can include: determining the wheel speed coefficient error differential equation based on the wheel speed coefficient noise; determining the gyroscope bias error differential equation based on the gyroscope angular velocity bias noise; determining the heading angle error differential equation based on the gyroscope angular velocity noise and the gyroscope bias error; and determining the position error differential equation based on the angular velocity measured by the inertial measurement unit, the speed measured by the wheel speed sensor, the gyroscope angular velocity noise, the wheel speed noise, the wheel speed coefficient error and the heading angle error.

[0056] For example, the vehicle’s position information determined by the IMU and wheel speed sensor is The error covariance at time ,exist arrive The cumulative error covariance for the time period is , The calculation process is as follows:

[0057] State quantity: Position error , heading angle error , wheel speed coefficient error , gyroscope bias error .

[0058] Noise: Wheel speed noise , wheel speed coefficient noise , gyroscope angular velocity noise , gyroscope angular velocity bias noise .

[0059] The speed measured by the wheel speed sensor at the center of the vehicle's rear axle is The proportional coefficient of wheel speed is k , the angular velocity measured by the IMU is w, then the velocity in the IMU coordinate system is .in, It is the external parameter of the wheel speed sensor and the horizontal arm of the IMU. The wheel speed sensor is set at the center of the rear axle of the vehicle and outputs the longitudinal speed of the center of the rear axle of the vehicle.

[0060] Then the differential equation is as follows:

[0061] (Formula 1)

[0062] (Formula 2)

[0063] (Formula 3)

[0064] (Formula 4)

[0065] Among them, b is the coordinate system of IMU, n is the global coordinate system, is the heading angle, To transform the IMU coordinate system into the rotation matrix of the global coordinate system, , , x, y, z represent the directions horizontally to the right, horizontally forward, and vertically upward, respectively, and the coordinate system is represented by superscripts in the present disclosure.

[0066] By discretizing the above differential equation and using Kalman filtering, we can calculate arrive The cumulative error covariance for the time period .

[0067] Figure 4 FIG. 4 is a flow chart showing a method for determining a lateral error covariance by using an error covariance according to some embodiments of the present disclosure. Figure 4 As shown, step S120 determines the lateral error covariance based on the error covariance by determining the attitude information of the moving target at the target time by the inertial measurement unit, and may include the following steps:

[0068] S121. Perform singular value decomposition on the position error covariance matrix in the error covariance to obtain two eigenvalues ​​and corresponding eigenvectors.

[0069] S122. Determine a heading vector based on the heading angle of the moving target at the target time determined by the inertial measurement unit.

[0070] S123. Determine the lateral error covariance based on the two eigenvalues ​​and the corresponding eigenvectors and the heading vector.

[0071] Figure 5 A schematic diagram showing the trajectory of a vehicle in a global coordinate system according to an embodiment of the present disclosure is shown, Figure 5 As shown, the orange solid line 501 is the vehicle determined by the laser radar Lidar Time has come The global trajectory at time t, the blue solid line 502 is the vehicle from the IMU and wheel speed sensor Time has come The global trajectory at time t, the blue dotted line 503 is The driving direction of the vehicle at the moment, the purple dotted line 504 is The lateral direction of the vehicle's driving direction at time tk is perpendicular to the vehicle's driving direction. The yellow solid line 505 is the lateral error between the global position of the vehicle determined by the Lidar and the global position of the vehicle determined by the IMU and the wheel speed sensor. The green ellipse 506 is the error covariance of the vehicle's position determined by the IMU and the wheel speed sensor in the global coordinate system at time tk. Figure 5 It can be seen that the lateral error covariance that needs to be determined in step S120 is Figure 5 The length of the line segment between the intersection of the purple dashed line 504 and the green ellipse 506 and the center of the green ellipse 504 .

[0072] Figure 6 FIG. 4 shows a schematic diagram of determining the lateral error covariance according to an embodiment of the present disclosure, as shown in FIG. Figure 6 As shown, the green oval 601 corresponds to Figure 5 The green ellipse 506 in the figure is the error covariance of the position in the global coordinate system, and the blue dotted line 602 corresponds to Figure 5 The blue dotted line 503 in the figure is the direction of the vehicle, and the purple dotted line 603 corresponds to Figure 5 The purple dotted line 504 in the figure is the lateral direction of the vehicle, and the length of the line segment between the intersection point A of the purple dotted line 603 and the green ellipse 601 and the center O of the green ellipse 601 is the lateral error covariance to be determined in step S120. The error covariance at the time is obtained to obtain the equation corresponding to the green ellipse 601, and then the vehicle is determined by the inertial measurement unit at Heading angle at the moment , the angle between the blue dotted line 602 and the x-axis of the green ellipse 601 is obtained, and then according to the perpendicular relationship between the purple dotted line 603 and the blue dotted line 602, the equation corresponding to the purple dotted line 603 is obtained. The lateral error covariance can be obtained by jointly solving the equation corresponding to the green ellipse 601 and the equation corresponding to the purple dotted line 603.

[0073] Therefore, two eigenvalues ​​and corresponding eigenvectors can be obtained by performing singular value decomposition on the position error covariance matrix in the error covariance; then the heading vector is determined according to the heading angle of the moving target at the target time determined by the IMU; and then the lateral error covariance is determined according to the two eigenvalues ​​and the corresponding eigenvectors and the heading vector. Among them, determining the lateral error covariance according to the two eigenvalues ​​and the corresponding eigenvectors and the heading vector may include: constructing an ellipse equation based on the two eigenvalues ​​and the corresponding eigenvectors; determining the straight line equation of the lateral vector perpendicular to the heading vector based on the heading vector and the eigenvector; and determining the lateral error covariance based on the ellipse equation and the straight line equation.

[0074] For example, Figure 6 As shown, the lateral error covariance The calculation process is as follows:

[0075] The vehicle’s position information determined by the IMU and wheel speed sensor is The cumulative error covariance at each moment is is a 5*5 matrix, from which we can take the error covariance representing the position (x, y) , It is a 2*2 matrix. Singular Value Decomposition (SVD) can be performed on this matrix to obtain two eigenvalues ​​and corresponding eigenvectors, where the larger eigenvalue is a and the smaller eigenvalue is b. It can be expressed as an ellipse with a as the major axis and b as the minor axis. The equation of the ellipse can be:

[0076] (Formula 5)

[0077] The heading angle of the vehicle at the time determined by the IMU , according to the heading angle The corresponding heading vector v1(sin( ),cos( )), the eigenvector corresponding to the eigenvalue a is v2. According to v1 and v2, the angle between v1 and v2 can be calculated as α. Then the slope of the heading vector v1 in the coordinate system corresponding to the ellipse equation is , then the slope of the lateral vector perpendicular to the heading vector in the coordinate system corresponding to the ellipse equation is , then we can solve the following two equations to get :

[0078]

[0079] Among them, solving the above equations simultaneously can obtain the coordinates of point A in the coordinate system corresponding to the ellipse equation , the coordinates of point A Substituting into the following formula, we can get the lateral error covariance .

[0080] (Formula 8)

[0081] The embodiments of the present disclosure obtain the error covariance by establishing a Kalman filter equation fused by the IMU and the wheel speed sensor, determine the lateral error covariance by using the error covariance updated by the measurementless method, and judge whether the positioning sensor fails by the ratio between the lateral error covariance and the lateral error amount. The transformation of the lateral error covariance in the global coordinate system along the x and y directions when the moving target turns is taken into consideration, so that the fault detection of the positioning sensor is not affected by the turning of the moving target.

[0082] Figure 7 A flow chart for determining the amount of lateral error according to some embodiments of the present disclosure is shown. Figure 7 As shown, step S200 determines the lateral error amount between the posture information of the moving target based on the first posture information and the second posture information of the moving target at the target time, and may include the following steps:

[0083] S210: Acquire the first position information of the moving target at the target time determined by the positioning sensor.

[0084] S220: Determine second position information of the moving target at a target time based on information measured by the inertial measurement unit and the wheel speed sensor.

[0085] S230 , determining a lateral error amount based on the first posture information and the second posture information.

[0086] In some optional embodiments of the present disclosure, the execution subject may first obtain the first pose information of the moving target at the target time determined by the positioning sensor, and then determine the second pose information of the moving target at the target time according to the information measured by the inertial measurement unit and the wheel speed sensor, and then determine the lateral error amount according to the first pose information and the second pose information. Figure 5 As shown, the green solid line 507 is the vehicle determined by the Lidar The global pose at the moment The vehicle is in the The global pose at the moment The error between The error of the vehicle's global position can be determined by the Lidar, IMU and wheel speed sensor. and the vehicle determined by the IMU The posture information of the vehicle at the moment The horizontal error at the moment ,Right now Figure 5 Medium yellow solid line 505.

[0087] Therefore, step S230 determines the lateral error amount based on the first posture information and the second posture information, which may include: determining the error amount between the first posture information and the second posture information; and determining the lateral error amount based on the error amount and the posture information of the moving target at the target time determined by the inertial measurement unit. Figure 5 As shown, the direction of the yellow solid line 505 is consistent with the direction of the purple dotted line 504, which is the error amount of the green solid line 507 The projection in the direction of the purple dotted line 504 is the lateral component of the error between the two in the IMU coordinate system, which can be calculated according to the following formula:

[0088] (Formula 9)

[0089] in, Here for The x-value of the inner vector.

[0090] In some optional embodiments of the present disclosure, the position and posture information of the moving target at the target time is determined based on the IMU and the wheel speed sensor. It is necessary to determine the position and posture information of the moving target at the beginning of the preset time period. The initial position and posture information of the moving target at the beginning of the preset time period determined by the positioning sensor can be obtained as the position and posture information of the IMU and the wheel speed sensor at the beginning of the preset time period. In order to ensure the accuracy of the position and posture information of the moving target at the target time determined by the IMU and the wheel speed sensor, it is necessary to perform posture compensation on the initial position and posture information of the moving target at the beginning of the preset time period determined by the positioning sensor. Therefore, Figure 8 As shown, the positioning fault detection method 1000 may further include the following steps:

[0091] S410: Acquire the initial position information of the moving target determined by the positioning sensor at the beginning time of the preset time period.

[0092] S420, determining posture compensation information based on the posture information of the moving target at the first moment in the preset time period determined by the positioning sensor and the posture information of the moving target at the first moment determined by the inertial measurement unit and the wheel speed sensor.

[0093] S420: Compensate the posture information in the initial posture information based on the posture compensation information to obtain the initial posture information of the inertial measurement unit and the wheel speed sensor.

[0094] The first moment may be a moment close to the start moment in the preset time period, and the distance between the start moment and the first moment needs to be a straight line distance. For example, the distance between the start moment and the first moment may be a distance of 3 to 5 meters. The horizontal position of the vehicle in the global coordinate system determined by the Lidar can be obtained and heading angle The horizontal position and heading angle are combined with the external parameters of the IMU and the Lidar Switch to the IMU coordinate system, as the IMU and wheel speed sensor at the start time Starting position .in Can include rotation and translation matrices.

[0095] In order to obtain the IMU and wheel speed sensor at the beginning The actual starting position in the preset time period The global position of the Lidar can be obtained at any time , calculated by IMU and wheel speed sensor, the vehicle is The global pose at the moment is , according to the laser radar Lidar obtained at the start time Determine the starting position and Lidar The global pose determined at time and the IMU and wheel speed sensor calculated at time The global pose can be used to obtain the vector of the global position transformation of the Lidar during the time period , and the vectors of the global position transformation of the IMU and wheel speed sensors , according to the vector and The angle between two vectors can be obtained , you can use To compensate for the laser radar Lidar obtained at the beginning The heading angle in the starting position , get the IMU and wheel speed sensor at the start time The real starting position , thus obtaining the true starting position of the IMU and wheel speed sensor at the beginning After that, the pose information of the IMU and wheel speed sensor is calculated from the real starting pose of the IMU and wheel speed sensor.

[0096] Therefore, step S420 determines the posture compensation information based on the posture information of the moving target at the first moment in the preset time period determined by the positioning sensor and the posture information of the moving target at the first moment determined by the inertial measurement unit and the wheel speed sensor, which may include: determining a first vector based on the posture information of the moving target at the first moment in the preset time period determined by the positioning sensor and the initial posture information; determining a second vector based on the posture information of the moving target at the first moment determined by the inertial measurement unit and the wheel speed sensor and the initial posture information; and determining the angle between the first vector and the second vector. Step S430 compensates the posture information in the initial posture information based on the posture compensation information to obtain the initial posture information of the inertial measurement unit and the wheel speed sensor, which may include: compensating the heading angle in the initial posture information by the determined angle to obtain the initial posture information of the inertial measurement unit and the wheel speed sensor.

[0097] In some optional embodiments of the present disclosure, step S300 performs fault detection on the positioning sensor based on the ratio of the lateral error amount to the lateral error covariance, which may include: determining whether the ratio of the lateral error amount to the lateral error covariance is greater than a preset threshold; and in response to the ratio of the lateral error amount to the lateral error covariance being greater than the preset threshold, determining that a lateral jump fault occurs in the positioning sensor. For example, is the lateral error, The lateral error

[0098] Covariance, the ratio of the lateral error amount to the lateral error covariance is .

[0099] Fig. 9 FIG. 1 is a flow chart showing a method for detecting a positioning fault according to an embodiment of the present disclosure. Fig. 9As shown, the Lidar is used as a positioning sensor on the vehicle to provide global position. For example, the Lidar can be a 32-line 3D mechanical Lidar installed on the roof, and the frequency of updating the pose information can be 10hz. The IMU and wheel speed sensor are used to detect the fault of the Lidar. For example, the frequency of updating the pose information of the IMU and wheel speed sensor can be 100hz. The specific process is as follows:

[0100] Step 701, determine the starting position: obtain the starting position of the vehicle at the time determined by the laser radar Lidar.

[0101] Step 702, align the position vector to obtain the true starting position: based on The starting position at time t is consistent with the vehicle position determined by Lidar The position of the Lidar at the moment Time has come The position transformation vector at the moment. Based on The starting position at time t is consistent with the vehicle position determined by IMU and wheel speed sensor. The pose at the moment determines the position of the IMU and wheel speed sensor Time has come The position transformation vector at the moment. The compensation angle is obtained by determining the angle between the two position transformation vectors, compensating the heading angle in the starting posture, and obtaining the real starting posture of the IMU and wheel speed sensor.

[0102] Step 703, fault detection prediction: Based on the noise and measured information of the IMU and wheel speed sensor, determine the error differential equation, discretize the error differential equation, and perform Kalman filtering on the discretized error differential equation to obtain the vehicle's position and posture. arrive The error covariance accumulated over the time period. The errors include position error, heading angle error, wheel speed coefficient error and gyroscope bias error. The noise includes gyroscope angular velocity noise, gyroscope angular velocity bias noise, wheel speed noise and wheel speed coefficient noise.

[0103] Step 704, calculate the lateral error amount: based on the vehicle's position determined by the laser radar Lidar The position and posture of the vehicle at the moment are determined by the IMU and wheel speed sensor. Based on the error, the vehicle position determined by IMU is The attitude at the moment is used to obtain the lateral error.

[0104] Step 705, calculate the lateral error covariance: Based on the error covariance, the vehicle is determined by the IMU The attitude at the moment is used to determine the lateral error covariance.

[0105] Step 706, fault detection judgment: based on the ratio of the lateral error amount to the lateral error covariance Perform fault detection on the Lidar. If the preset threshold is exceeded, it is determined that the Lidar fails and the failure can be checked in step 707.

[0106] like If the preset threshold is not exceeded, it can be determined whether the preset mileage threshold is exceeded in step 708. If the preset mileage threshold is not exceeded, the process returns to step 703 to start the fault detection prediction. If the preset mileage threshold is exceeded, the process returns to step 701 to determine the starting position and start the execution.

[0107] In order to avoid the increase of IMU cumulative error affecting the accuracy of Lidar fault detection, the method disclosed in this disclosure usually needs to reset the entire detection process after a period of time or distance, for example, the preset mileage threshold is 100m. The method disclosed in this disclosure can accurately detect whether the Lidar has a lateral jump fault. If a lateral jump fault occurs, If the value exceeds the preset threshold, the Lidar is judged to be faulty.

[0108] Figures 10 to 13 FIG. 1 is a schematic diagram showing experimental comparison results of a positioning fault detection method according to an embodiment of the present disclosure. Fig.10 As shown in the figure, when the trajectory of Lidar positioning is normal throughout the whole process without jumps, such as Fig.11 As shown, The data changes within a range of less than 6. Fig.12 As shown, when Fig.10 When a 0.5m jump occurs on the same trajectory, Fig.13 As shown, for the jump part of Lidar positioning, The data will surge to more than 20.

[0109] The embodiment of the present disclosure further provides a positioning fault detection device 2000, Fig.14 FIG. 2 shows a block diagram of a positioning fault detection device 2000 according to an embodiment of the present disclosure. The positioning fault detection device 2000 according to an embodiment of the present disclosure can execute the positioning fault detection method 1000 described above. Fig.14 As shown, the positioning fault detection device 2000 may include:

[0110] The lateral error covariance determination module 810 is configured to determine the lateral error covariance of the position information of the moving target determined by the inertial measurement unit and the wheel speed sensor in a preset time period based on the information measured by the inertial measurement unit and the wheel speed sensor.

[0111] The lateral error amount determination module 820 is configured to determine the lateral error amount between the posture information of the moving target based on the first posture information and the second posture information of the moving target at the target time, wherein the target time is the end time of the preset time period, the first posture information is measured by the positioning sensor arranged on the moving target, and the second posture information is determined by the inertial measurement unit and the wheel speed sensor.

[0112] The fault detection module 830 is configured to perform fault detection on the positioning sensor based on the ratio of the lateral error amount to the lateral error covariance.

[0113] In some optional implementations, the lateral error covariance determination module 810 includes:

[0114] The error covariance determination unit is configured to determine the error covariance accumulated by the position and posture information of the moving target in the preset time period through Kalman filtering based on the information measured by the inertial measurement unit and the wheel speed sensor.

[0115] The lateral error covariance determining unit is configured to determine the lateral error covariance based on the error covariance and the posture information of the moving target at the target time determined by the inertial measurement unit.

[0116] In some optional implementations, the error covariance determination unit is further configured to:

[0117] determining an error differential equation based on noise and measured information of the inertial measurement unit and the wheel speed sensor, wherein the error includes position error, heading angle error, wheel speed coefficient error, and gyroscope bias error; and

[0118] The error differential equation is discretized, and Kalman filtering is performed on the discretized error differential equation to obtain the error covariance accumulated in the preset time period of the position and posture information of the moving target.

[0119] In some optional implementations, the error covariance determination unit is further configured to:

[0120] Determining gyroscope angular velocity noise and gyroscope angular velocity bias noise based on the parameters of the inertial measurement unit;

[0121] determining wheel speed noise and wheel speed coefficient noise based on parameters of the wheel speed sensor; and

[0122] The error differential equation is determined based on the angular velocity measured by the inertial measurement unit, the velocity measured by the wheel speed sensor, the gyroscope angular velocity noise, the gyroscope angular velocity bias noise, the wheel speed noise, and the wheel speed coefficient noise.

[0123] In some optional implementations, the error covariance determination unit is further configured to:

[0124] determining a wheel speed coefficient error differential equation based on the wheel speed coefficient noise;

[0125] determining a gyroscope bias error differential equation based on the gyroscope angular velocity bias noise;

[0126] determining a heading angle error differential equation based on the gyroscope angular velocity noise and the gyroscope bias error; and

[0127] A position error differential equation is determined based on the angular velocity measured by the inertial measurement unit, the velocity measured by the wheel speed sensor, the gyroscope angular velocity noise, the wheel speed noise, the wheel speed coefficient error, and the heading angle error.

[0128] In some optional implementations, the lateral error covariance determination unit is further configured to:

[0129] Performing singular value decomposition on the position error covariance matrix in the error covariance to obtain two eigenvalues ​​and corresponding eigenvectors;

[0130] Determining a heading vector based on the heading angle of the moving target at the target time determined by the inertial measurement unit; and

[0131] The lateral error covariance is determined based on the two eigenvalues ​​and the corresponding eigenvectors and the heading vector.

[0132] In some optional implementations, the lateral error covariance determination unit is further configured to:

[0133] constructing an elliptic equation based on the two eigenvalues ​​and the corresponding eigenvectors;

[0134] Determine a straight line equation of a lateral vector perpendicular to the heading vector based on the heading vector and the eigenvector; and

[0135] The lateral error covariance is determined based on the ellipse equation and the line equation.

[0136] In some optional implementations, the lateral error amount determination module 820 is further configured to:

[0137] Acquire the first position information of the moving target at the target time determined by the positioning sensor;

[0138] Determining second position information of the moving target at the target time based on information measured by the inertial measurement unit and the wheel speed sensor; and

[0139] The lateral error amount is determined based on the first posture information and the second posture information.

[0140] In some optional implementations, the lateral error amount determination module 820 is further configured to:

[0141] Determining an error amount between the first posture information and the second posture information; and

[0142] Based on the error amount, the lateral error amount is determined by the posture information of the moving target at the target time determined by the inertial measurement unit.

[0143] In some optional implementations, the positioning fault detection device 2000 further includes: a starting posture determination module, the starting posture determination module is configured to:

[0144] Acquire the starting position information of the moving target at the beginning of the preset time period determined by the positioning sensor;

[0145] Determine attitude compensation information based on the attitude information of the moving target at a first moment in the preset time period determined by the positioning sensor and the attitude information of the moving target at the first moment determined by the inertial measurement unit and the wheel speed sensor;

[0146] The posture information in the initial posture information is compensated based on the posture compensation information to obtain the initial posture information of the inertial measurement unit and the wheel speed sensor.

[0147] In some optional implementations, the starting posture determination module is further configured to:

[0148] Determine a first vector based on the position information of the moving target at a first moment in the preset time period determined by the positioning sensor and the starting position information;

[0149] Determine a second vector based on the position information of the moving target at the first moment determined by the inertial measurement unit and the wheel speed sensor and the initial position information; and

[0150] determining an angle between the first vector and the second vector;

[0151] The heading angle in the initial posture information is compensated by the included angle to obtain the initial posture information of the inertial measurement unit and the wheel speed sensor.

[0152] In some optional implementations, the fault detection module 830 is further configured to:

[0153] Determining whether a ratio of the lateral error amount to the lateral error covariance is greater than a preset threshold;

[0154] In response to a ratio of the lateral error amount to the lateral error covariance being greater than the preset threshold, it is determined that a lateral jump fault occurs in the positioning sensor.

[0155] In addition, an embodiment of the present disclosure also provides an electronic device, which includes at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned positioning fault detection method 1000.

[0156] The embodiment of the present disclosure further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above-mentioned positioning fault detection method 1000 is implemented.

[0157] The embodiments of the present disclosure further provide a computer program product, including a computer program, which implements the above-mentioned positioning fault detection method 1000 when executed by a processor.

[0158] Fig.15 A block diagram of an electronic device 3000 that can be used to implement an example of an embodiment of the present disclosure is shown. The electronic device 3000 is intended to represent various forms of digital computers. The electronic device 3000 can also represent various forms of mobile devices that can run computing programs. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0159] like Fig.15 As shown, the electronic device 3000 includes a computing unit 910, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 920 or a computer program loaded from a storage unit 980 to a random access memory (RAM) 930. In the RAM 930, various programs and data required for the operation of the autonomous driving vehicle or mobile robot 3000 can also be stored. The computing unit 910, the ROM 920, and the RAM 930 are connected to each other via a bus 940. An input / output (I / O) interface 950 is also connected to the bus 940.

[0160] A number of components in the electronic device 3000 are connected to the I / O interface 950, including: an input unit 960, such as a touch screen, etc.; an output unit 970, such as various types of displays, speakers, etc.; a storage unit 980, such as a disk, etc.; and a communication unit 990, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 990 allows the electronic device 3000 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0161] The computing unit 910 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 910 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 910 performs the various methods and processes described above, such as the positioning fault detection method 1000. For example, in some embodiments, the positioning fault detection method 1000 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 980. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 3000 via the ROM 920 and / or the communication unit 990. When the computer program is loaded into the RAM 930 and executed by the computing unit 910, one or more steps of the positioning fault detection method 1000 described above may be performed. Alternatively, in other implementations, the computing unit 910 may be configured to execute the positioning fault detection method 1000 in any other appropriate manner (eg, by means of firmware).

[0162] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0163] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0164] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0165] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0166] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for locating fault detection, characterized in that: include: Determine the lateral error covariance of the position information of the moving target in a preset time period based on information measured by an inertial measurement unit and a wheel speed sensor disposed on the moving target; Determine a lateral error amount between the posture information of the moving target based on first posture information and second posture information of the moving target at a target time, wherein the target time is an end time of the preset time period, the first posture information is determined by a positioning sensor disposed on the moving target, and the second posture information is determined by the inertial measurement unit and the wheel speed sensor; as well as performing fault detection on the positioning sensor based on a ratio of the lateral error amount to the lateral error covariance; Wherein, determining the lateral error covariance of the position and posture information of the moving target in a preset time period includes: Based on the information measured by the inertial measurement unit and the wheel speed sensor, determining the error covariance accumulated in the preset time period of the posture information of the moving target by Kalman filtering; and Based on the error covariance, determining the lateral error covariance by using the posture information of the moving target at the target time determined by the inertial measurement unit; Determining a lateral error amount between the posture information of the moving target based on the first posture information and the second posture information of the moving target at the target time includes: Acquire the first position information of the moving target at the target time determined by the positioning sensor; Determining second position information of the moving target at the target time based on information measured by the inertial measurement unit and the wheel speed sensor; and The lateral error amount is determined based on the first posture information and the second posture information.

2. The method according to claim 1, characterized in that The method of determining the error covariance accumulated in the preset time period of the posture information of the moving target by Kalman filtering based on the information measured by the inertial measurement unit and the wheel speed sensor includes: determining an error differential equation based on noise and measured information of the inertial measurement unit and the wheel speed sensor, wherein the error includes position error, heading angle error, wheel speed coefficient error, and gyroscope bias error; and The error differential equation is discretized, and Kalman filtering is performed on the discretized error differential equation to obtain the error covariance accumulated in the preset time period of the position and posture information of the moving target.

3. The method according to claim 2, characterized in that The determining of the error differential equation based on the noise and measured information of the inertial measurement unit and the wheel speed sensor comprises: Determining gyroscope angular velocity noise and gyroscope angular velocity bias noise based on the parameters of the inertial measurement unit; determining wheel speed noise and wheel speed coefficient noise based on parameters of the wheel speed sensor; and The error differential equation is determined based on the angular velocity measured by the inertial measurement unit, the velocity measured by the wheel speed sensor, the gyroscope angular velocity noise, the gyroscope angular velocity bias noise, the wheel speed noise, and the wheel speed coefficient noise.

4. The method according to claim 3, characterized in that: The determining the error differential equation based on the angular velocity measured by the inertial measurement unit, the speed measured by the wheel speed sensor, the gyroscope angular velocity noise, the gyroscope angular velocity bias noise, the wheel speed noise and the wheel speed coefficient noise comprises: determining a wheel speed coefficient error differential equation based on the wheel speed coefficient noise; determining a gyroscope bias error differential equation based on the gyroscope angular velocity bias noise; determining a heading angle error differential equation based on the gyroscope angular velocity noise and the gyroscope bias error; and A position error differential equation is determined based on the angular velocity measured by the inertial measurement unit, the velocity measured by the wheel speed sensor, the gyroscope angular velocity noise, the wheel speed noise, the wheel speed coefficient error, and the heading angle error.

5. The method according to claim 1, characterized in that The determining the lateral error covariance based on the error covariance and the posture information of the moving target at the target time determined by the inertial measurement unit includes: Performing singular value decomposition on the position error covariance matrix in the error covariance to obtain two eigenvalues ​​and corresponding eigenvectors; Determining a heading vector based on the heading angle of the moving target at the target time determined by the inertial measurement unit; and The lateral error covariance is determined based on the two eigenvalues ​​and the corresponding eigenvectors and the heading vector.

6. The method according to claim 5, characterized in that The determining the lateral error covariance based on the two eigenvalues ​​and the corresponding eigenvectors and the heading vector comprises: constructing an elliptic equation based on the two eigenvalues ​​and the corresponding eigenvectors; Determine a straight line equation of a lateral vector perpendicular to the heading vector based on the heading vector and the eigenvector; and The lateral error covariance is determined based on the ellipse equation and the line equation.

7. The method according to claim 1, characterized in that The determining the lateral error amount based on the first posture information and the second posture information includes: Determining an error amount between the first posture information and the second posture information; and Based on the error amount, the lateral error amount is determined by the posture information of the moving target at the target time determined by the inertial measurement unit.

8. The method according to claim 1, characterized in that Also includes: Acquire the starting position information of the moving target at the beginning of the preset time period determined by the positioning sensor; Determine attitude compensation information based on the attitude information of the moving target at a first moment in the preset time period determined by the positioning sensor and the attitude information of the moving target at the first moment determined by the inertial measurement unit and the wheel speed sensor; The posture information in the initial posture information is compensated based on the posture compensation information to obtain the initial posture information of the inertial measurement unit and the wheel speed sensor.

9. The method according to claim 8, characterized in that The determining of attitude compensation information based on the attitude information of the moving target at the first moment in the preset time period determined by the positioning sensor and the attitude information of the moving target at the first moment determined by the inertial measurement unit and the wheel speed sensor includes: Determine a first vector based on the position information of the moving target at a first moment in the preset time period determined by the positioning sensor and the starting position information; Determine a second vector based on the position information of the moving target at the first moment determined by the inertial measurement unit and the wheel speed sensor and the initial position information; and determining an angle between the first vector and the second vector; The step of compensating the posture information in the initial posture information based on the posture compensation information to obtain the initial posture information of the inertial measurement unit and the wheel speed sensor includes: The heading angle in the initial posture information is compensated by the included angle to obtain the initial posture information of the inertial measurement unit and the wheel speed sensor.

10. The method according to claim 1, characterized in that The performing fault detection on the positioning sensor based on the ratio of the lateral error amount to the lateral error covariance includes: Determining whether a ratio of the lateral error amount to the lateral error covariance is greater than a preset threshold; In response to a ratio of the lateral error amount to the lateral error covariance being greater than the preset threshold, it is determined that a lateral jump fault occurs in the positioning sensor.

11. A positioning fault detection device, characterized in that: include: A lateral error covariance determination module is configured to determine the lateral error covariance of the position and posture information of the moving target in a preset time period based on information measured by an inertial measurement unit and a wheel speed sensor provided on the moving target; a lateral error amount determination module, configured to determine a lateral error amount between the posture information of the moving target based on first posture information and second posture information of the moving target at a target time, wherein the target time is an end time of the preset time period, the first posture information is determined by a positioning sensor provided on the moving target, and the second posture information is determined by the inertial measurement unit and the wheel speed sensor; as well as a fault detection module, configured to perform fault detection on the positioning sensor based on a ratio of the lateral error amount to the lateral error covariance; Wherein, the lateral error covariance determination module includes: an error covariance determining unit, configured to determine, based on information measured by the inertial measurement unit and the wheel speed sensor, an error covariance accumulated by the position and posture information of the moving target in the preset time period through a Kalman filter; and a lateral error covariance determining unit, configured to determine the lateral error covariance based on the error covariance and through the posture information of the moving target at the target time determined by the inertial measurement unit; The lateral error amount determination module is further configured to: obtain the first posture information of the moving target at the target time determined by the positioning sensor; determine the second posture information of the moving target at the target time based on the information measured by the inertial measurement unit and the wheel speed sensor; and determine the lateral error amount based on the first posture information and the second posture information.

12. An electronic device, characterized in that: include: at least one processor; as well as, A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can execute the positioning fault detection method described in any one of claims 1 to 10.

13. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the positioning fault detection method as claimed in any one of claims 1 to 10 is implemented.

14. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the positioning fault detection method as claimed in any one of claims 1 to 10 is implemented.

Images