Positioning accuracy evaluation method, device and computer-readable storage medium

Abstract

The present application discloses a positioning accuracy evaluation method, device, and computer-readable storage medium. The positioning accuracy evaluation method obtains IMU pose data at the current moment by performing a first point cloud matching operation on the point cloud data at the current moment, obtains IMU estimated pose data at the current moment by performing a positioning processing operation on the initial data set and the IMU pose data at the current moment, compares the IMU estimated pose data at the current moment with the IMU reference pose data at the current moment to obtain IMU pose error data, and then obtains a positioning accuracy evaluation result based on the IMU pose error data. The initial data set and the IMU pose data set within a preset time period are subjected to bidirectional filtering to obtain IMU reference pose data, making full use of the point cloud data and improving the accuracy of the IMU reference pose data. The present solution can automatically process data, saving time and effort.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a positioning accuracy evaluation method, device, and computer-readable storage medium. Background Art

[0002] Positioning technology provides accurate estimates of the position, speed, and attitude of autonomous vehicles and is fundamental to autonomous driving. However, during daily driving, autonomous vehicles can experience positioning inaccuracies that can affect their safety. Summary of the Invention

[0003] The present application provides a positioning accuracy evaluation method, device, and computer-readable storage medium.

[0004] The first technical solution adopted in the present application is to provide a positioning accuracy evaluation method, comprising: obtaining point cloud data at the current moment for positioning accuracy evaluation and an initial data set within a preset time period, wherein the preset time period is the time between the initial moment and the current moment;

[0005] Performing a first point cloud matching operation on the point cloud data at the current moment to obtain IMU pose data at the current moment;

[0006] Performing a positioning processing operation on the initial data set and the IMU pose data at the current moment to obtain the IMU estimated pose data at the current moment;

[0007] Comparing the IMU estimated pose data at the current moment with the IMU reference pose data at the current moment to obtain IMU pose error data, wherein the point cloud data within the preset time period is subjected to a second point cloud matching operation to obtain an IMU pose dataset within the preset time period, the IMU reference pose data at the current moment is obtained by performing a bidirectional filtering operation on the initial dataset and the IMU pose dataset within the preset time period, and the first point cloud matching operation is different from the second point cloud matching operation;

[0008] Based on the IMU posture error data, a positioning accuracy evaluation result is obtained.

[0009] wherein performing a first point cloud matching operation on the point cloud data at the current moment to obtain IMU pose data at the current moment includes: obtaining a relative change based on the point cloud data at the previous moment and the point cloud data at the current moment, and obtaining first matching data at the current moment based on the IMU estimated pose data at the previous moment;

[0010] Obtaining second matching data at the current moment based on the point cloud data and the point cloud map at the current moment;

[0011] The IMU pose data at the current moment is obtained based on the first matching data at the current moment and the second matching data at the current moment.

[0012] Wherein, obtaining the IMU pose data at the current moment based on the first matching data at the current moment and the second matching data at the current moment includes:

[0013] fusing the first matching data and the second matching data to obtain the IMU pose data at the current moment; or

[0014] The IMU posture data at the current moment is obtained according to the first matching data.

[0015] In which, the first point cloud matching operation adopts a first point cloud matching algorithm, and the second point cloud matching operation adopts a second point cloud matching algorithm, wherein the matching time of the first point cloud matching algorithm is less than the matching time of the second point cloud matching algorithm, and the accuracy of the data results obtained by the first point cloud matching algorithm is less than the accuracy of the data results obtained by the second point cloud matching algorithm.

[0016] The data in the initial data set is sorted in a first order, which is a time order; and the data is obtained by performing a bidirectional filtering operation on the initial data set and the IMU pose data set within the preset time period, including: performing forward filtering on the initial data set and the IMU pose data set within the preset time period to obtain the IMU forward reference data at the current moment, wherein the forward filtering indicates filtering in the first order;

[0017] Performing reverse filtering on the initial data set and the IMU pose data set within the preset time period to obtain IMU reverse reference data at the current moment, wherein the reverse filtering is performed in a second order, which is opposite to the first order;

[0018] The IMU forward reference data at the current moment and the IMU reverse reference data at the current moment are processed to obtain the IMU reference pose data at the current moment.

[0019] The IMU forward reference data and the IMU reverse reference data at the current moment are processed to obtain the IMU reference pose data at the current moment, including:

[0020] Performing weighted fusion on the IMU forward reference data at the current moment and the IMU reverse reference data at the current moment to obtain the IMU reference pose data at the current moment;

[0021] The formula for weighted fusion is as follows:

[0022]

[0023] Among them, X represents the IMU reference pose data, X f Represents the IMU forward reference data, P f Represents the uncertainty of the IMU forward reference data, X b Represents IMU reverse reference data, P b Represents the uncertainty of the IMU back-reference data.

[0024] Among them, the positioning accuracy evaluation method further includes: in response to the positioning accuracy evaluation result being failed, adjusting the first point cloud matching operation and the positioning processing operation according to the IMU pose error data, including: in response to the IMU pose error data being greater than a preset value for more than a preset number of moments within a preset time period, modifying the first point cloud matching operation and the positioning processing operation.

[0025] The preset values ​​include Where P represents the uncertainty of the IMU pose error data.

[0026] The uncertainty of the IMU pose error data is calculated by the uncertainty of the IMU forward reference data and the uncertainty of the IMU reverse reference data;

[0027] The calculation formula for the uncertainty of the IMU pose error data is as follows:

[0028]

[0029] Among them, P represents the uncertainty of IMU pose error data, P f Represents the uncertainty of the IMU forward reference data, P b Represents the uncertainty of the IMU back-reference data.

[0030] The second technical solution adopted in this application is: to provide a positioning accuracy evaluation device, including a processor and a memory coupled to the processor, the memory is used to store a computer program, and the processor is used to execute the computer program to implement the positioning accuracy evaluation method.

[0031] The third technical solution adopted in this application is: providing a non-volatile computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, it is used to implement the positioning accuracy evaluation method.

[0032] The above scheme obtains the IMU pose data at the current moment by performing a first point cloud matching operation on the point cloud data at the current moment, and obtains the IMU estimated pose data at the current moment by performing a positioning processing operation on the initial data set and the IMU pose data at the current moment, and compares the IMU estimated pose data at the current moment with the IMU reference pose data at the current moment to obtain the IMU pose error data, and then obtains the positioning accuracy evaluation result based on the IMU pose error data. Among them, the initial data set and the IMU pose data set within the preset time period are subjected to bidirectional filtering to obtain the IMU reference pose data, which makes full use of the point cloud data and improves the accuracy of the IMU reference pose data. This scheme can automatically process data, saving time and effort. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a flow chart of an embodiment of the positioning accuracy evaluation method of the present application;

[0034] Figure 2 This is a flow chart of a logic diagram of an embodiment of the positioning accuracy evaluation method of the present application;

[0035] Figure 3 This is a structural diagram of an embodiment of a positioning accuracy evaluation device of the present application;

[0036] Figure 4 It is a structural diagram of an embodiment of the non-volatile computer-readable storage medium of the present application. DETAILED DESCRIPTION

[0037] The present application will be further described in detail below in conjunction with the accompanying drawings and examples. It is particularly noted that the following examples are only intended to illustrate the present application and are not intended to limit the scope of the present application. Similarly, the following examples are only some examples of the present application and not all examples. All other examples obtained by those of ordinary skill in the art without creative work are intended to fall within the scope of protection of this application.

[0038] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0039] It should be noted that the terms "first", "second" and "third" in this application are only used for descriptive purposes and should not be understood as indicating or suggesting relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first", "second" and "third" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units that are inherent to these processes, methods, products or devices.

[0040] See also Figure 1 and Figure 2 , Figure 1 This is a flow chart of an embodiment of the positioning accuracy evaluation method of the present application. Figure 2 This is a flow chart of the logic of an embodiment of the positioning accuracy evaluation method of the present application. It should be noted that if there are substantially the same results, the method of the present application is not based on Figure 1 The process sequence shown is limited. This method can be applied to electronic devices with computing and other functions. The electronic devices can execute this method by receiving information collected by sensor devices. The sensor devices can be millimeter wave radars, lidars, inertial measurement units (IMUs), global navigation satellite systems (GNSSs), wheel odometers or cameras equipped on autonomous vehicles. The sensor devices perceive the dynamic scenes of the real physical world around the autonomous vehicle during its driving process and locate the vehicle in combination with high-precision maps. Figure 1 As shown, the positioning accuracy evaluation method includes the following steps:

[0041] S1. Obtaining point cloud data at the current moment and an initial data set within a preset time period for positioning accuracy evaluation, wherein the preset time period is the time between the initial moment and the current moment.

[0042] Sensors include radar sensors, IMU sensors, GNSS sensors, and odometer sensors. Radar sensors can be used to collect point cloud data, IMU sensors can be used to collect acceleration data and angular velocity data, GNSS sensors can be used to collect position data or heading data, and odometer sensors can be used to collect velocity data. For example, the radar sensor, IMU sensor, GNSS sensor, and odometer sensor can be installed on a movable device. The movable device can be an autonomous mobile device, such as a robot or an autonomous vehicle.

[0043] In some embodiments, the radar sensor may be a lidar sensor, such as a mechanical lidar, a semi-solid lidar, or a solid-state lidar. In some embodiments, the radar sensor may be a radar device used for autonomous driving and meeting accuracy requirements for providing point cloud perception.

[0044] In one application scenario, an autonomous vehicle is traveling on a road. A radar sensor installed on the autonomous vehicle senses point cloud data for positioning accuracy evaluation, obtaining point cloud data within a preset time period, including point cloud data at the current moment. An IMU sensor is used to collect acceleration data and angular velocity data, a GNSS sensor is used to collect position data or heading data, and an odometer sensor is used to collect speed data, obtaining an initial data set within a preset time period. The data collection frequencies of the radar sensor, IMU sensor, GNSS sensor, and odometer sensor can be different and can be set according to actual usage requirements. For example, the collection frequency of the IMU sensor is greater than the collection frequency of the odometer sensor, and the collection frequency of the radar sensor is greater than the collection frequency of the GNSS sensor.

[0045] The preset time period is the time between the initial time and the current time. The initial time can be understood as the time when the sensor device is powered on. For example, if the sensor device is powered on at time t0, the initial time is t0 and the current time is t 10 , then the preset time period is from t0 to t 10 The moments between, i.e., t0, t1, t2, t3, t4, t5, t6, t7, t8, t9, t 10 .

[0046] S2. Perform a first point cloud matching operation on the point cloud data at the current moment to obtain the IMU pose data at the current moment.

[0047] Perform a first point cloud matching operation on the point cloud data at the current moment to obtain the IMU pose data at the current moment. The first point cloud matching operation is a real-time point cloud matching operation, and the matching operation algorithm can adopt the Iterative Closest Point (ICP) algorithm, the Normal Distributions Transform (NDT) algorithm, the LOAM algorithm, or other achievable algorithms, without specific limitation. It can be understood that the radar sensor collects point cloud data in real time, and performs a real-time point cloud matching operation on the point cloud data at the current moment through the matching algorithm, so that the IMU pose data at the current moment can be calculated in real time.

[0048] S3. Perform positioning processing operations on the initial data set and the IMU pose data at the current moment to obtain the IMU estimated pose data at the current moment.

[0049] Performing a positioning processing operation on the initial data set and the IMU pose data at the current moment to obtain the IMU estimated pose data at the current moment. Inputting the data in the initial data set and the IMU pose data at the current moment calculated in real time into the positioning algorithm, performing a positioning processing operation to obtain the IMU estimated pose data at the current moment. The positioning processing algorithm can adopt a Kalman filter algorithm, that is, inputting the initial data set and the IMU pose data at the current moment into the Kalman filter algorithm for calculation to obtain the IMU estimated pose data at the current moment.

[0050] For example, if the initial time is t0 and the current time is t3, the Kalman filter algorithm is used to filter the data from t0 to t3 in the initial data set and the IMU pose data at t3 in the time sequence of t0-t3 to obtain the IMU estimated pose data at t3; if the current time is t 10 , the Kalman filter algorithm is used to filter the initial data from t0 to t 10 The data at time t 10 The IMU pose data at the moment is calculated as t0-t 10 Filter in time order to get t 10 The IMU estimated pose data at each moment is calculated in real time. In other embodiments, the positioning processing operation algorithm can also adopt other achievable algorithms without specific limitation.

[0051] S4. Compare the IMU estimated pose data at the current moment with the IMU reference pose data at the current moment to obtain IMU pose error data, wherein the point cloud data within the preset time period is subjected to a second point cloud matching operation to obtain the IMU pose data set within the preset time period, and the IMU reference pose data at the current moment is obtained by performing a bidirectional filtering operation on the initial data set and the IMU pose data set within the preset time period. The first point cloud matching operation is different from the second point cloud matching operation.

[0052] The IMU estimated pose data at the current moment is compared with the IMU reference pose data at the current moment to obtain IMU pose error data. It can be understood that the IMU estimated pose data can be obtained through the positioning processing operation, and the IMU estimated pose data can be compared with the IMU reference pose data to obtain IMU pose error data.

[0053] The point cloud data within the preset time period is subjected to a second point cloud matching operation to obtain an IMU pose dataset within the preset time period, and the IMU pose dataset includes IMU pose data at each moment within the preset time period. A bidirectional filtering operation is performed on the initial dataset within the preset time period and the IMU pose dataset within the preset time period to obtain IMU reference pose data at each moment within the preset time period, including the IMU reference pose data at the current moment.

[0054] The first point cloud matching operation is different from the second point cloud matching operation. The second point cloud matching operation is a non-real-time point cloud matching operation. The matching algorithm can adopt the Iterative Closest Point (ICP) algorithm, the Normal Distributions Transform (NDT) algorithm, the LOAM algorithm, or other achievable algorithms, without specific limitation. The non-real-time point cloud matching operation is performed on the point cloud data at each moment in a preset time period through the matching algorithm. That is, the point cloud data at each moment in the preset time period is input into the matching algorithm, and an IMU pose data set within the preset time period can be obtained for the subsequent calculation of the IMU reference pose data.

[0055] In some embodiments, the calculation step for the IMU estimated pose data can be performed by the vehicle-mounted device, and the calculation step for the IMU reference pose data can be performed by the cloud. The cloud obtains point cloud data within a preset time period and performs a second point cloud matching operation on the point cloud data to obtain an IMU pose data set within the preset time period. A bidirectional filtering operation is performed on the initial data set within the preset time period and the IMU pose data set within the preset time period to obtain IMU reference pose data. The cloud obtains the IMU estimated pose data and compares it with the IMU reference pose data to obtain IMU pose error data.

[0056] S5. Obtain positioning accuracy evaluation results based on IMU posture error data.

[0057] Based on the IMU pose error data, a positioning accuracy evaluation result is obtained. The positioning accuracy evaluation result can include a pass or fail. For example, an error threshold can be preset, and the positioning accuracy evaluation result can be obtained by comparing the IMU pose error data with the error threshold. The positioning accuracy can be determined based on the distribution of the IMU pose error data at each moment within a preset time period, thereby obtaining a positioning accuracy evaluation result.

[0058] The initial time is t0 and the current time is t 10 For example, t 10 The point cloud data at the moment is input into the algorithm of the first point cloud matching operation in real time, and t is calculated. 10 The IMU pose data at the moment t 10 The IMU pose data at time t and the data in the initial data set are input into the positioning algorithm to obtain 10 The IMU estimated pose data at time t0 to t 10 The point cloud data at each moment in the time period is input into the algorithm of the second non-real-time point cloud matching operation, and the time interval from t0 to t 10 IMU pose dataset within the time period. 10 IMU pose dataset and time period t0 to t 10 The data in the initial data set within the time period t0 to t 10 The IMU reference pose data at each moment in the time period. 10 The IMU estimated pose data at time t 10 Compare the IMU reference pose data at time t to get 10 IMU pose error data at the moment. Based on t0 to t 10 The distribution of IMU pose error data within the time period is used to obtain the positioning accuracy evaluation results.

[0059] The above scheme obtains the IMU pose data at the current moment by performing a first point cloud matching operation on the point cloud data at the current moment, and obtains the IMU estimated pose data at the current moment by performing a positioning processing operation on the initial data set and the IMU pose data at the current moment, and compares the IMU estimated pose data at the current moment with the IMU reference pose data at the current moment to obtain the IMU pose error data, and then obtains the positioning accuracy evaluation result based on the IMU pose error data. Among them, the initial data set and the IMU pose data set within the preset time period are subjected to bidirectional filtering to obtain the IMU reference pose data, which makes full use of the point cloud data and improves the accuracy of the IMU reference pose data. This scheme can automatically process data, saving time and effort.

[0060] In one embodiment of the present application, a first point cloud matching operation is performed on the point cloud data at the current moment to obtain the IMU pose data at the current moment, including: obtaining a relative change based on the point cloud data at the previous moment and the point cloud data at the current moment, and obtaining the first matching data at the current moment based on the IMU estimated pose data at the previous moment; obtaining the second matching data at the current moment based on the point cloud data and the point cloud map at the current moment; and obtaining the IMU pose data at the current moment based on the first matching data at the current moment and the second matching data at the current moment.

[0061] The relative change is obtained based on the point cloud data at the previous moment and the point cloud data at the current moment, and the first matching data at the current moment is obtained based on the IMU estimated pose data at the previous moment. It can be understood that the point cloud data at the previous moment is known. After obtaining the point cloud data at the current moment, the point cloud data at the current moment is compared with the point cloud data at the previous moment to obtain the relative change of the IMU at the current moment equivalent to the previous moment. Then, based on the IMU estimated pose data obtained by the positioning processing at the previous moment and the relative change of the IMU, the position data and posture data of the IMU at the current moment, that is, the first matching data, are obtained.

[0062] Based on the current point cloud data and the point cloud map, the second matching data at the current moment is obtained. It can be understood that the current point cloud data is matched and aligned with the point cloud in the point cloud map. If the current point cloud data coincides with the point cloud in the point cloud map, it means that the matching alignment is successful. Then, based on the current point cloud data, the current IMU position data and IMU posture data, that is, the second matching data, are obtained.

[0063] In some embodiments, if the point cloud data at the current moment does not overlap with the point cloud in the point cloud map, the point cloud data at the current moment can be evaluated based on the point cloud map to obtain the second matching data at the current moment. For example, the distance between the points in the point cloud data at the current moment and the points in the point cloud map can be calculated to perform a comprehensive evaluation of the point cloud data at the current moment, and the second matching data at the current moment can be obtained based on the calculation result.

[0064] The IMU pose data at the current moment is obtained based on the first matching data and the second matching data at the current moment. That is, the first matching data is obtained by matching the point cloud data at adjacent moments, and the second matching data is obtained by matching the point cloud data with the point cloud map. The first matching data at the current moment and the second matching data at the current moment are processed to obtain the IMU pose data at the current moment.

[0065] As described above, first matching data is obtained by matching point cloud data at adjacent moments, second matching data is obtained by matching the point cloud data with the point cloud map, and the first matching data and the second matching data are then processed to obtain IMU pose data. In one embodiment of the present application, the IMU pose data at the current moment is obtained based on the first matching data at the current moment and the second matching data at the current moment, including: fusing the first matching data and the second matching data to obtain the IMU pose data at the current moment; or obtaining the IMU pose data at the current moment based on the first matching data.

[0066] The first matching data at the current moment and the second matching data at the current moment are fused to obtain the IMU pose data at the current moment. If the current moment is t3, the first matching data at t3 and the second matching data at t3 are fused to obtain the IMU pose data at t3; if the current moment is t5, the first matching data at t5 and the second matching data at t5 are fused to obtain the IMU pose data at t5. The specific fusion algorithm can be selected according to actual computing requirements and is not specifically limited.

[0067] Alternatively, the IMU posture data at the current moment is obtained based on the first matching data, wherein the first matching data is obtained by performing a first point cloud matching operation on the point cloud data at the current moment, and the point cloud data is collected in real time by the radar sensor and has higher reliability.

[0068] In one embodiment of the present application, the first point cloud matching operation adopts a first point cloud matching algorithm, and the second point cloud matching operation adopts a second point cloud matching algorithm, wherein the matching time of the first point cloud matching algorithm is less than the matching time of the second point cloud matching algorithm, and the accuracy of the data results obtained by the first point cloud matching algorithm is less than the accuracy of the data results obtained by the second point cloud matching algorithm.

[0069] The first point cloud matching operation uses a first point cloud matching algorithm to perform a first point cloud matching operation on the point cloud data at the current moment in real time through the first point cloud matching algorithm. The second point cloud matching operation uses a second point cloud matching algorithm to perform a second point cloud matching operation on the point cloud data within a preset time period in non-real time through the second point cloud matching algorithm. It is understandable that because the first point cloud matching algorithm calculates the point cloud data at the current moment, while the second point cloud matching algorithm calculates the point cloud data within a preset time period, the matching time of the first point cloud matching algorithm is less than the matching time of the second point cloud matching algorithm. And because the amount of data calculated by the first point cloud matching algorithm is less than the amount of data calculated by the second point cloud matching algorithm, the accuracy of the data results obtained by the first point cloud matching algorithm is less than the accuracy of the data results obtained by the second point cloud matching algorithm.

[0070] As mentioned above, a bidirectional filtering operation is performed on the initial data set and the IMU pose data set within a preset time period to obtain the IMU reference pose data at the current moment. In one embodiment of the present application, the initial data set includes GNSS data, IMU data, and odometer data, and the data in the initial data set are sorted in a first order, which is a time order. It can be understood that the sensor device collects data in real time within a preset time period, and the initial data set includes GNSS data collected in real time by the GNSS sensor, IMU data collected in real time by the IMU sensor, and odometer data collected in real time by the odometer sensor, wherein the GNSS data includes GNSS position data and GNSS heading data, the IMU data includes IMU acceleration data and IMU angular velocity data, and the odometer data includes odometer speed data. The data in the initial data set are sorted in a first order corresponding to each moment, wherein the first order is a time order, that is, the data corresponding to each moment in the initial data set are sorted in time order.

[0071] As described above, the point cloud data at each moment within the preset time period is input into the second point cloud matching algorithm to perform non-real-time point cloud matching processing, thereby obtaining an IMU pose dataset for the preset time period. In one embodiment, a bidirectional filtering operation is performed on the initial dataset and the IMU pose dataset within the preset time period, including: forward filtering the initial dataset and the IMU pose dataset within the preset time period to obtain IMU forward reference data for each moment within the preset time period, including the IMU forward reference data at the current moment, wherein forward filtering means filtering in a first order; reverse filtering the initial dataset and the IMU pose dataset within the preset time period to obtain IMU reverse reference data for each moment within the preset time period, including the IMU reverse reference data at the current moment, wherein reverse filtering is filtering in a second order, the second order being opposite to the first order; and processing the IMU forward reference data at the current moment and the IMU reverse reference data at the current moment to obtain the IMU reference pose data at the current moment.

[0072] The bidirectional filtering operation can adopt the Kalman filter algorithm or other achievable algorithms, which can be selected according to actual computing requirements without specific limitation. It can be understood that the data in the initial data set and the data in the IMU posture data set within the preset time period are sorted in a first order, and the data in the initial data set and the data in the IMU posture data set within the preset time period are subjected to a bidirectional filtering operation, including: performing forward Kalman filtering on the data in the initial data set and the data in the IMU posture data set within the preset time period, and performing reverse Kalman filtering on the data in the initial data set and the data in the IMU posture data set within the preset time period. Among them, forward Kalman filtering means filtering in the first order, that is, performing Kalman filtering on the data in the initial data set and the data in the IMU posture data set within the preset time period in time order, and reverse filtering means filtering in the second order, the second order is opposite to the first order, that is, the second order is in reverse time order, and reverse filtering means performing Kalman filtering on the data in the initial data set and the data in the IMU posture data set within the preset time period in reverse time order.

[0073] In the following, the initial time is t0 and the current time is t 10 As an example, the initial data set includes time t0 to t 10 The GNSS data, IMU data and odometer data corresponding to each moment in the time period, and the IMU pose data set includes the time from t0 to t 10 The IMU pose data obtained by the second point cloud matching operation at each moment in the time period. The forward filtering is from time t0 to t 10 The order of time is the data in the initial data set and the time from t0 to t10 The data in the IMU pose data set within the time period are processed by Kalman filtering to obtain the 10 The IMU forward reference data at each moment in the time period and the corresponding uncertainty of the IMU forward reference data, where the IMU forward reference data includes IMU velocity, IMU position and IMU attitude, and the uncertainty of the IMU forward reference data includes the uncertainty of IMU velocity, IMU position and IMU attitude. The reverse filtering is performed by t 10 The order from time to time t0 affects the data in the initial data set and t 10 The data in the IMU pose data set from time t to time t0 are processed by Kalman filtering to obtain t 10 The IMU reverse reference data at each moment in the time period from time t to time t0 and the corresponding uncertainty of the IMU reverse reference data, where the IMU reverse reference data includes IMU velocity, IMU position and IMU attitude, and the uncertainty of the IMU reverse reference data includes the uncertainty of IMU velocity, IMU position and IMU attitude.

[0074] The IMU forward reference data and IMU reverse reference data corresponding to each moment are processed to obtain the IMU reference pose data at that moment, thereby obtaining the IMU reference pose data for each moment within a preset time period, including the IMU reference pose data at the current moment. For example, the IMU forward reference data and the IMU reverse reference data at time t0 are processed to obtain the IMU reference pose data at time t0; the IMU forward reference data and the IMU reverse reference data at time t1 are processed to obtain the IMU reference pose data at time t1.

[0075] As described above, by performing forward Kalman filtering on the data in the initial data set and the data in the IMU pose data set within the preset time period, the IMU forward reference data and the uncertainty of the IMU forward reference data can be obtained. By performing reverse Kalman filtering on the data in the initial data set and the data in the IMU pose data set within the preset time period, the IMU reverse reference data and the uncertainty of the IMU reverse reference data can be obtained. In one embodiment of the present application, the IMU forward reference data at the current moment and the IMU reverse reference data at the current moment are processed to obtain the IMU reference pose data at the current moment, including: performing weighted fusion on the IMU forward reference data at the current moment and the IMU reverse reference data at the current moment to obtain the IMU reference pose data at the current moment.

[0076] The IMU forward reference data at the current moment and the IMU reverse reference data at the current moment are weightedly fused to obtain the IMU reference pose data at the current moment. It can be understood that by weightedly fusion of the IMU forward reference data and the IMU reverse reference data corresponding to each moment in the preset time period, the IMU reference pose data at each moment in the preset time period can be obtained. For example, the IMU forward reference data at time t0 and the IMU reverse reference data at time t0 are processed to obtain the IMU reference pose data at time t0; the IMU forward reference data at time t1 and the IMU reverse reference data at time t1 are processed to obtain the IMU reference pose data at time t1, thereby obtaining the IMU reference pose data from the initial time t0 to the current time t 10 IMU reference pose data at each moment in the time period.

[0077] Among them, the formula of weighted fusion is as follows:

[0078]

[0079] Among them, X represents the IMU reference pose data, X f Represents the IMU forward reference data, P f Represents the uncertainty of the IMU forward reference data, X b Represents IMU reverse reference data, P b Represents the uncertainty of the IMU back-reference data.

[0080] It can be understood that by performing forward Kalman filtering on the data in the initial data set and the data in the IMU pose data set within a preset time period, the IMU forward reference data X is obtained. f and the uncertainty P of the IMU forward reference data f , perform reverse Kalman filtering on the data in the initial data set and the data in the IMU pose data set within the preset time period to obtain the IMU reverse reference data X b and the uncertainty P of the IMU back reference data b , and then the IMU forward reference data X f and IMU reverse reference data X b Perform weighted fusion processing to obtain the IMU reference pose data X. Among them, P f Represents the uncertainty of the IMU forward reference data, P f The smaller the value, the more the IMU forward reference data X f The more stable; P b represents the uncertainty of the IMU reverse reference data, P b The smaller the value, the better the IMU reverse reference data X b The more stable.

[0081] In one embodiment of the present application, the positioning accuracy evaluation method further includes: in response to the positioning accuracy evaluation result being failed, adjusting the first point cloud matching operation and the positioning processing operation based on the IMU pose error data, including: in response to the IMU pose error data at more than a preset number of moments within a preset time period being greater than a preset value, modifying the first point cloud matching operation and the positioning processing operation.

[0082] By comparing the IMU estimated pose data at the current moment with the IMU reference pose data at the corresponding moment, the IMU pose error data at the current moment is obtained, thereby obtaining the IMU pose error data at each moment within the preset time period. Based on the distribution of the IMU pose error data within the preset time period, the positioning accuracy is judged to obtain a positioning accuracy evaluation result. The positioning accuracy evaluation result includes pass and fail. In response to the positioning accuracy evaluation result being fail, the positioning processing operation of the first point cloud matching operation is adjusted based on the IMU pose error data. In some embodiments, the steps of adjusting the first point cloud matching operation and the positioning operation can be performed by the cloud.

[0083] In response to the IMU pose error data being greater than a preset value for more than a preset number of moments within a preset time period, it indicates that the positioning accuracy is poor and cannot pass the test evaluation of the positioning accuracy. Therefore, it is necessary to modify the first point cloud matching algorithm of the first point cloud matching operation to adjust the first point cloud matching operation, and modify the algorithm of the positioning processing operation to adjust the positioning processing operation, thereby improving the positioning accuracy.

[0084] For example, the preset number is 3. It can be understood that the total number of moments in the preset time period is greater than 3. Taking the total number of moments in the preset time period as 10 as an example, the IMU pose error data of 10 moments in the preset time period are calculated. If the IMU pose error data corresponding to 5 of the moments are greater than the preset value, then the number of moments greater than the preset value exceeds the preset number, indicating that the positioning accuracy in the preset time period is poor, and the first point cloud matching algorithm of the first point cloud matching operation and the algorithm of the positioning processing operation need to be modified to improve the positioning accuracy.

[0085] As described above, in response to the IMU pose error data being greater than a preset value, the first point cloud matching operation and the positioning processing operation are modified. In one embodiment of the present application, the preset value includes Where P represents the uncertainty of the IMU pose error data.

[0086] The default value can be set to Where P represents the uncertainty of the IMU error data. It can be understood that the uncertainty of the IMU error data is a numerical value that can characterize the stability of the IMU posture error data. For example, if the uncertainty of the IMU posture error data is 1 meter, the preset value is If the IMU pose error data is 5, which is significantly greater than the preset value 3, the first point cloud matching algorithm of the first point cloud matching operation and the positioning processing operation algorithm need to be modified to improve the positioning accuracy.

[0087] In other embodiments, the preset value may also be set to other values ​​according to actual accuracy requirements, without specific limitation.

[0088] As mentioned above, the uncertainty of the IMU pose error data is used to characterize the stability of the IMU pose error data. In one embodiment of the present application, the uncertainty of the IMU pose error data is calculated by the uncertainty of the IMU forward reference data and the uncertainty of the IMU reverse reference data; the calculation formula for the uncertainty of the IMU pose error data is as follows:

[0089]

[0090] Among them, P represents the uncertainty of IMU pose error data, P f Represents the uncertainty of the IMU forward reference data, P b Represents the uncertainty of the IMU back-reference data.

[0091] Using the above formula, the uncertainty P of the IMU forward reference data can be calculated f and the uncertainty P of the IMU back reference data b The uncertainty P of the IMU pose error data is calculated and used to characterize the stability of the IMU pose error data.

[0092] See also Figure 3 , Figure 3 It is a structural diagram of an embodiment of the positioning accuracy evaluation device of the present application. The device 100 includes a processor 101 and a memory 102 coupled to the processor 101. The memory 102 is used to store computer programs, and the processor 101 is used to execute computer programs to implement the positioning accuracy evaluation method in the above embodiment.

[0093] See also Figure 4 , Figure 4 It is a structural diagram of an embodiment of the non-volatile computer-readable storage medium of the present application. The computer-readable storage medium 200 is used to store a computer program 201. When the computer program 201 is executed by the processor 101, it is used to implement the positioning accuracy evaluation method in the above embodiment.

[0094] The computer-readable storage medium 200 can be a server, a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., which can store program codes.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules or units is merely a logical functional division. In actual implementation, other division methods may be used. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not implemented.

[0096] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0097] In addition, each functional unit in each embodiment of the present application may be integrated into a processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The above-mentioned integrated units may be implemented in the form of hardware or software functional units.

[0098] The above description is only an implementation method of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the description and drawings of this application, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A positioning accuracy evaluation method, characterized in that: include: Acquire point cloud data at the current moment and an initial data set within a preset time period for positioning accuracy evaluation, wherein the preset time period is the time between the initial moment and the current moment; Performing a first point cloud matching operation on the point cloud data at the current moment to obtain IMU pose data at the current moment; Performing a positioning processing operation on the initial data set and the IMU pose data at the current moment to obtain the IMU estimated pose data at the current moment; Comparing the IMU estimated pose data at the current moment with the IMU reference pose data at the current moment to obtain IMU pose error data, wherein the point cloud data within the preset time period is subjected to a second point cloud matching operation to obtain an IMU pose dataset within the preset time period, the IMU reference pose data at the current moment is obtained by performing a bidirectional filtering operation on the initial dataset and the IMU pose dataset within the preset time period, and the first point cloud matching operation is different from the second point cloud matching operation; Obtaining a positioning accuracy evaluation result based on the IMU posture error data; The first point cloud matching operation is a real-time point cloud matching operation, and the second point cloud matching operation is a non-real-time point cloud matching operation. The positioning accuracy evaluation method further includes: In response to the positioning accuracy evaluation result being a failure, adjusting the first point cloud matching operation and the positioning processing operation according to the IMU pose error data, including: In response to the IMU pose error data being greater than a preset value for more than a preset number of moments within a preset time period, the first point cloud matching operation and the positioning processing operation are modified.

2. The positioning accuracy evaluation method according to claim 1, characterized in that: Performing a first point cloud matching operation on the point cloud data at the current moment to obtain IMU pose data at the current moment, including: Obtaining a relative change based on the point cloud data at the previous moment and the point cloud data at the current moment, and obtaining first matching data at the current moment based on the IMU estimated pose data at the previous moment; Obtaining second matching data at the current moment based on the point cloud data and the point cloud map at the current moment; The IMU pose data at the current moment is obtained based on the first matching data at the current moment and the second matching data at the current moment.

3. The positioning accuracy evaluation method according to claim 2, characterized in that: Obtaining the IMU pose data at the current moment based on the first matching data at the current moment and the second matching data at the current moment, including: fusing the first matching data and the second matching data to obtain the IMU pose data at the current moment; or The IMU posture data at the current moment is obtained according to the first matching data.

4. The positioning accuracy evaluation method according to claim 1, characterized in that: The first point cloud matching operation adopts a first point cloud matching algorithm, and the second point cloud matching operation adopts a second point cloud matching algorithm, wherein the matching time of the first point cloud matching algorithm is less than the matching time of the second point cloud matching algorithm, and the accuracy of the data results obtained by the first point cloud matching algorithm is less than the accuracy of the data results obtained by the second point cloud matching algorithm.

5. The positioning accuracy evaluation method according to claim 1, characterized in that: The data in the initial data set are sorted in a first order, where the first order is a time order; Performing a bidirectional filtering operation on the initial data set and the IMU pose data set within the preset time period to obtain the IMU reference pose data at the current moment, including: Performing forward filtering on the initial data set and the IMU pose data set within the preset time period to obtain IMU forward reference data at the current moment, wherein the forward filtering indicates filtering in the first order; Performing reverse filtering on the initial data set and the IMU pose data set within the preset time period to obtain IMU reverse reference data at the current moment, wherein the reverse filtering is performed in a second order, which is opposite to the first order; The IMU forward reference data at the current moment and the IMU reverse reference data at the current moment are processed to obtain the IMU reference pose data at the current moment.

6. The positioning accuracy evaluation method according to claim 5, characterized in that: Processing the IMU forward reference data and the IMU reverse reference data at the current moment to obtain the IMU reference pose data at the current moment, including: Performing weighted fusion on the IMU forward reference data at the current moment and the IMU reverse reference data at the current moment to obtain the IMU reference pose data at the current moment; The formula for weighted fusion is as follows: Among them, X represents the IMU reference pose data, X f Represents the IMU forward reference data, P f Represents the uncertainty of the IMU forward reference data, X b Represents IMU reverse reference data, P b Represents the uncertainty of the IMU back-reference data.

7. The positioning accuracy evaluation method according to claim 1, characterized in that: The preset values ​​include Where P represents the uncertainty of the IMU pose error data.

8. The positioning accuracy evaluation method according to claim 7, characterized in that: The uncertainty of the IMU pose error data is calculated by the uncertainty of the IMU forward reference data and the uncertainty of the IMU reverse reference data; The calculation formula for the uncertainty of the IMU pose error data is as follows: Among them, P represents the uncertainty of IMU pose error data, P f Represents the uncertainty of the IMU forward reference data, P b Represents the uncertainty of the IMU back-reference data.

9. A positioning accuracy evaluation device, characterized in that: It includes a processor and a memory coupled to the processor, the memory is used to store a computer program, and the processor is used to execute the computer program to implement the positioning accuracy evaluation method according to any one of claims 1 to 8.

10. A non-volatile computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, it is used to implement the positioning accuracy evaluation method according to any one of claims 1 to 8.

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