A method and device for detecting performance of a laser radar

Abstract

The embodiment of the present disclosure provides a kind of performance detection method and device of laser radar, the method comprises: laser radar and the object to be detected are connected;Laser radar receives detection instruction, starts to identify the object to be detected;The object to be detected moves along the preset motion trajectory, wherein the preset motion trajectory is set to at least one three-dimensional space trajectory;When the object to be detected is identified, record the point cloud data of the object to be detected movement;The point cloud data is preprocessed to obtain the detection motion trajectory of the object to be detected;Compare detection motion trajectory with preset motion trajectory, obtain the distortion degree of detection motion trajectory;Based on the distortion degree, the working performance of the laser radar is determined.The laser radar to be tested of the present disclosure is applied to the field of unmanned driving, the object to be detected moves along the three-dimensional space trajectory, and the working performance of the laser radar is determined by obtaining the distortion degree of detection motion trajectory, the detection method is simple, convenient, practical, and has wide application range.

Description

Technical Field

[0001] This disclosure pertains to the field of lidar detection technology, specifically relating to a lidar performance testing method and apparatus. Background Technology

[0002] In unmanned mining truck scenarios, LiDAR plays a crucial role. LiDAR is responsible for the truck's perception of its surroundings, roads, obstacles, and other vehicles. Due to the harsh working environment in mining areas, LiDAR is prone to frequent malfunctions. Regular inspections of the LiDAR are necessary to promptly detect and repair or replace faulty units. Currently, manual inspection of LiDAR point cloud data is mainly used to determine if there are any gaps in the LiDAR scanning channels or if the beam is continuous. However, this method cannot effectively assess the LiDAR's object detection performance at different distances or other operational aspects.

[0003] Therefore, there is a lack of efficient methods in the existing technology for detecting the working performance of lidar at different distances. Summary of the Invention

[0004] The present disclosure aims to at least solve one of the technical problems existing in the prior art, and to provide a method and apparatus for testing the performance of lidar.

[0005] One aspect of this disclosure provides a method for detecting the performance of a lidar, the method comprising:

[0006] The lidar establishes a connection with the object to be detected;

[0007] The lidar receives the detection command and begins to identify the object to be detected;

[0008] The object to be detected moves along a preset motion trajectory, wherein the preset motion trajectory is set as at least one three-dimensional spatial trajectory;

[0009] Once the object to be detected is identified, the point cloud data of the motion of the object to be detected is recorded;

[0010] The point cloud data is preprocessed to obtain the detection motion trajectory of the object to be detected;

[0011] By comparing the detected motion trajectory with the preset motion trajectory, the degree of distortion of the detected motion trajectory can be determined.

[0012] The operating performance of the lidar is determined based on the degree of distortion.

[0013] Optionally, determining the operating performance of the lidar based on the degree of distortion includes:

[0014] The operating performance of the lidar is determined based on the degree of distortion obtained from multiple tests.

[0015] Optionally, determining the operating performance of the lidar based on the degree of distortion includes:

[0016] If the degree of distortion is greater than the distortion threshold, the lidar is determined to have malfunctioned.

[0017] Optionally, the preprocessing of the point cloud data to obtain the detection motion trajectory of the object to be detected includes:

[0018] The point cloud data is filtered to remove point clouds that do not contain the object to be detected.

[0019] The filtered point cloud data is smoothed to obtain the detection trajectory of the object to be detected.

[0020] Optionally, the operating performance of the lidar includes at least one of the following:

[0021] Detection distance, ranging sampling rate / accuracy, horizontal and vertical field of view, scanning frequency, angular resolution, and measurement resolution / accuracy.

[0022] Optionally, before determining the operating performance of the lidar based on the degree of distortion, the method further includes:

[0023] If the degree of distortion is greater than the detection threshold, the lidar controls the object to be detected to move along the preset motion trajectory again.

[0024] Optionally, the preset motion trajectory is set as multiple three-dimensional spatial trajectories; before determining the working performance of the lidar based on the degree of distortion, the method further includes:

[0025] If the degree of distortion is greater than the adjustment threshold, the lidar will reselect a three-dimensional spatial trajectory from the multiple three-dimensional spatial trajectories and control the object to be detected to move along the reselected three-dimensional spatial trajectory.

[0026] Optionally, the preset motion trajectory is a conical spiral.

[0027] Optionally, before the object to be detected moves along a preset motion trajectory, the method further includes: a step of generating the preset motion trajectory, specifically:

[0028] Select the preset motion trajectory of the object to be detected according to the model of the lidar; or,

[0029] The optimal motion trajectory is generated based on the historical detection results of the lidar, and this optimal motion trajectory is used as the preset motion trajectory.

[0030] Another aspect of this disclosure provides a performance testing device for a lidar, the device comprising:

[0031] The connection module is used to establish a connection between the lidar and the object to be detected;

[0032] The receiving and identification module is used to enable the lidar to receive detection commands and begin identifying the object to be detected.

[0033] The control module is used to make the object to be detected move along a preset motion trajectory.

[0034] The preset motion trajectory is set as at least one three-dimensional spatial trajectory;

[0035] The recording module is used to record the point cloud data of the motion of the object to be detected after the object is identified.

[0036] The preprocessing module is used to preprocess the point cloud data to obtain the detection motion trajectory of the object to be detected;

[0037] The comparison module is used to compare the detected motion trajectory with the preset motion trajectory to determine the degree of distortion of the detected motion trajectory;

[0038] The judgment module is used to determine the working performance of the lidar based on the degree of distortion.

[0039] In the performance testing method and apparatus for lidar of the present disclosure, the preset motion trajectory of the object to be tested is at least one three-dimensional spatial trajectory, and the detected motion trajectory is compared with the preset motion trajectory to obtain the degree of distortion of the detected motion trajectory, thereby determining the working performance of the lidar. This testing method effectively detects the working performance of the lidar, and the testing method is simple, comprehensive, efficient, and has a wide range of applications. Attached Figure Description

[0040] Figure 1 This is a schematic flowchart of a performance testing method for a lidar according to an embodiment of the present disclosure;

[0041] Figure 2 This is a schematic diagram of the preprocessing process for point cloud data in a lidar performance testing method according to an embodiment of the present disclosure.

[0042] Figure 3 This is a schematic diagram of the orthographic projection of a preset motion trajectory on a horizontal plane for a performance testing method of a lidar according to an embodiment of the present disclosure.

[0043] Figure 4 This is a schematic diagram of the planar projection of the preset motion trajectory of a lidar performance detection method according to an embodiment of the present disclosure on the field of view.

[0044] Figure 5 This is a schematic diagram of the structure of a lidar performance testing device according to another embodiment of the present disclosure. Detailed Implementation

[0045] To enable those skilled in the art to better understand the technical solutions of the embodiments of this disclosure, the embodiments of this disclosure will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0046] like Figure 1 As shown, a performance testing method S100 for lidar includes the following steps:

[0047] S110: The lidar establishes a connection with the object to be detected.

[0048] Specifically, the object to be detected serves as the target of the lidar, and the visibility of the object in the lidar point cloud, among other indicators, reflects the lidar's performance. In step S110, the lidar is first activated, and a wireless connection is established between the lidar and the object to be detected. The lidar then controls the movement of the object. This embodiment of the present disclosure reduces the need for personnel or servers by establishing a connection between the lidar and the object to be detected, thus simplifying lidar performance testing.

[0049] S120: The lidar receives the detection command and begins to identify the object to be detected.

[0050] Specifically, in step S120, the detection command can be sent to the lidar by the object to be detected, input manually, or sent to the lidar by the server. After receiving the detection command, the lidar enters the detection process and begins to capture and identify the object to be detected.

[0051] S130, The object to be detected moves along a preset motion trajectory, wherein the preset motion trajectory is set as at least one three-dimensional spatial trajectory.

[0052] Specifically, in step S130, the preset motion trajectory can be pre-saved in the object to be detected or the server, or it can be stored in the lidar. The object to be detected can be controlled by the lidar to move according to the retrieved preset motion trajectory. The preset motion trajectory is set as at least one three-dimensional spatial trajectory, which can cover the effective working range of the lidar in three-dimensional space. The object to be detected can be selected according to the actual scenario. For example, in a scenario such as an unmanned mining truck, the object to be detected can be a drone. The user inputs the preset motion trajectory into the drone's controller, which can control the drone to move along the preset motion trajectory. In other application scenarios, the object to be detected can also be set on a device that moves in the horizontal, vertical, and vertical directions, and the device is used to drive the object to be detected to move along the preset motion trajectory. Of course, in addition to this, those skilled in the art can also select other objects to be detected according to different application scenarios, and this embodiment does not limit this. The effective working range of the lidar in three-dimensional space includes the effective working distance and the field of view. As the effective working distance increases, the detection surface of all beams will also gradually increase.

[0053] In step S130, there are certain requirements for the preset motion trajectory. The preset motion trajectory should be within the effective working range of the lidar in three-dimensional space so that the lidar can detect the object at any position it moves to. This is used to test the lidar's performance at different distances.

[0054] As a specific example of a preset motion trajectory, the preset motion trajectory is a conical spiral shape. For example... Figure 3 As shown, the orthographic projection of the preset motion trajectory onto the horizontal plane is a spiral line covering the effective working range of the lidar. The preset motion trajectory exists at different distances relative to the lidar, allowing for the detection of the lidar's performance at different distances. In addition to... Figure 3 Besides the spiral shown, the orthographic projection of the preset motion trajectory on the horizontal plane can also be other regular curves, such as S-curves, sine curves, etc. This embodiment does not limit this, and the spiral is used as an example for specific explanation below.

[0055] like Figure 4 As shown, when the spiral is unfolded, the trajectory on the vertical plane is a sinusoidal trajectory with continuously increasing amplitude. Because the vertical field of view of the lidar has a fixed angle between its upper and lower edges, the detection area formed by all the beams also increases as the radius expands. The upper and lower tangents of this sinusoidal trajectory correspond to the vertical field of view of the lidar, ensuring that the object to be detected covers all detection channels of the lidar.

[0056] It should be noted that no limitation is made on the type of motion used to achieve the preset motion trajectory defined in this embodiment. For example, a single motion, such as spiral upward motion, can be used; or a reciprocating motion, such as spiral upward followed by spiral downward motion, can be used; of course, other complex motions can also be used to achieve the desired preset motion trajectory. The complex motion is achieved through a combination of multiple motion trajectories, such as circumferential spiral motion around the lidar and axial vibration around the lidar. The spiral motion ensures that the preset motion trajectory covers the effective working distance of the lidar. The vibration motion ensures that the preset motion trajectory simultaneously covers all detection channels of the lidar. The vibration amplitude of the vibration motion at least covers the field of view of the lidar where the object to be detected is located. Those skilled in the art will understand that other curvilinear motions that can cover the effective working radius and field of view of the lidar are all within the scope of protection of this disclosure. This embodiment does not limit this.

[0057] It should be further noted that, in order to locate the position information of the object to be detected in a timely manner, the object to be detected may also be equipped with positioning devices such as GPS and inertial measurement sensors to ensure that the object to be detected does not deviate from the preset motion trajectory.

[0058] S140. After the object to be detected is identified, the point cloud data of the motion of the object to be detected is recorded.

[0059] Specifically, in step S140, the lidar can be a multi-line lidar such as a 16-line, 32-line, or 64-line lidar. The lidar can be a mechanical rotating lidar or a solid-state lidar, etc., and is not limited here. The movement speed of the object to be detected can be set according to actual conditions. When the object to be detected moves according to a preset trajectory within the effective detection range of the lidar, the object to be detected will simultaneously appear in the point cloud data of the lidar and perform corresponding movements, thereby recording the point cloud data of the object to be detected. Each point in the point cloud data generally contains three-dimensional coordinate information, which are the X, Y, and Z elements commonly referred to. Sometimes it also contains color information, reflection intensity information, echo count information, etc.

[0060] S150. Preprocess the point cloud data to obtain the detection motion trajectory of the object to be detected.

[0061] Specifically, in step S150, static background is filtered out from the point cloud data. When there is little static background data, the point cloud data can also be directly filtered to remove isolated noise information and retain the point cloud data of the object to be detected.

[0062] As a specific example for determining the operating performance of the lidar, such as Figure 2As shown, the preprocessing of the point cloud data to obtain the detection motion trajectory of the object to be detected includes:

[0063] S151. Filter the point cloud data to remove point clouds that do not contain the object to be detected.

[0064] The point cloud data is filtered using the Kalman filter algorithm to remove point cloud data that is not the object to be detected. Here, only the point cloud data within the detection trajectory range and the range within 3m around the object to be detected are selected to reduce the amount of point cloud data while maintaining the shape characteristics of the point cloud.

[0065] S152. Smooth the filtered point cloud data to obtain the detection motion trajectory of the object to be detected.

[0066] Specifically, the filtered point cloud data is smoothed to further reduce the amount of point cloud data and improve the calculation speed. If the lidar is a rotating lidar, such as a traditional 360° mechanical lidar, each frame is obtained by scanning around the lidar center for one revolution, for example, every 100ms. Since the object to be detected is moving, after one revolution, the point cloud data of the current object to be detected is obtained. The three-dimensional spatial coordinates of the current object to be detected can be extracted from each frame of point cloud data. Arranging the three-dimensional coordinates in chronological order yields the detection trajectory of the object to be detected, i.e., the spatial discrete trajectory points.

[0067] S160. Compare the detected motion trajectory with the preset motion trajectory to determine the degree of distortion of the detected motion trajectory.

[0068] Specifically, in step S160, due to factors such as measurement accuracy and deviations between the actual and preset motion trajectories, the detected motion trajectory exhibits an error, or distortion, compared to the preset trajectory. Based on point cloud data, the lidar obtains the distance and three-dimensional coordinates of the object to be detected relative to the lidar in each frame. The detected and preset motion trajectories are then transformed to the same coordinate system. The three-dimensional coordinates corresponding to the same distance between the detected and preset motion trajectories and the lidar are determined, and the Euclidean distance between the two coordinates is calculated to obtain the degree of distortion for each three-dimensional coordinate of the detected motion trajectory. Alternatively, the average distance can be calculated and used as the average degree of distortion.

[0069] S170. Based on the degree of distortion, determine the operating performance of the lidar.

[0070] Specifically, in step S170, the operating performance of the lidar can be determined based on the degree of distortion. The smaller the distortion, the better the lidar's operating performance; the larger the distortion, the worse the lidar's operating performance; and distortion exceeding a certain level indicates a lidar malfunction.

[0071] When the preset motion trajectory is a conical spiral, this embodiment of the present disclosure can also determine the specific fault of the lidar by comparing the detected motion trajectory with the preset motion trajectory. If there is a significant missing part in the detected motion trajectory, it can be determined that the lidar detection channel or effective working distance corresponding to the missing part of the detected motion trajectory is faulty.

[0072] As a specific example of determining the operating performance of the lidar, the determination of the lidar operating performance based on the degree of distortion includes:

[0073] The operating performance of the lidar is determined based on the degree of distortion obtained from multiple tests.

[0074] Specifically, this disclosure allows for multiple tests on the lidar, repeating steps S110 to S170. This results in more accurate test results and better determination of the lidar's performance.

[0075] As a specific example of determining the operating performance of the lidar, the determination of the lidar operating performance based on the degree of distortion includes:

[0076] If the degree of distortion is greater than the distortion threshold, the lidar is determined to have malfunctioned.

[0077] The operating performance of the lidar includes at least one of the following: detection range, ranging sampling rate / accuracy, horizontal and vertical field of view, scanning frequency, angular resolution, and measurement resolution / accuracy.

[0078] Specifically, the performance of the LiDARs is interconnected. For example, angular resolution is crucial for long-distance recognition. The LiDAR's sampling rate is fixed; a higher frame rate results in lower angular resolution, and vice versa. If the distortion of the detected trajectory exceeds the distortion threshold, at least one of the following performance parameters fails to meet requirements: detection distance, ranging sampling rate / accuracy, horizontal and vertical field of view, scanning frequency, angular resolution, and measurement resolution / accuracy, leading to distortion exceeding the threshold. More specifically, if distortion exceeds the threshold in a certain area of ​​the detected motion trajectory, it is determined that the detection distance or horizontal and vertical field of view of the LiDAR corresponding to that area is faulty; if distortion exceeds the threshold at all points of the detected motion trajectory, it is determined that the ranging sampling rate / accuracy, angular resolution, measurement resolution / accuracy, or scanning frequency of the LiDAR is faulty.

[0079] As a specific example of determining the operating performance of the lidar, before determining the operating performance of the lidar based on the degree of distortion, the method further includes:

[0080] If the degree of distortion is greater than the detection threshold, the lidar controls the object to be detected to move along the preset motion trajectory again.

[0081] Specifically, if the distortion degree exceeds the detection threshold, it indicates a large distortion, which may be due to external environmental factors causing the actual motion trajectory of the object to be detected to deviate significantly from the preset motion trajectory. In this case, if the preset motion trajectory has a three-dimensional spatial trajectory, the lidar controls the object to be detected to move along the preset motion trajectory again, repeating steps S130 to S160. Here, if atmospheric vortices or windy weather are encountered, the external environment has a significant impact on the actual motion trajectory of the object to be detected, causing the distortion degree to exceed the detection threshold. All three-dimensional coordinates of the detected motion trajectory may exhibit irregular distortion. In this case, the detection location or detection time can be changed to make the object to be detected move along the preset motion trajectory again.

[0082] As a specific example of determining the operating performance of the lidar, the preset motion trajectory is set as multiple three-dimensional spatial trajectories; before determining the operating performance of the lidar based on the degree of distortion, the method further includes:

[0083] If the degree of distortion is greater than the adjustment threshold, the lidar will reselect a three-dimensional spatial trajectory from the multiple three-dimensional spatial trajectories and control the object to be detected to move along the reselected three-dimensional spatial trajectory.

[0084] Specifically, when the preset motion trajectory is set as multiple three-dimensional spatial trajectories, the object to be detected moves along the initial preset motion trajectory. However, if the distortion degree of the detected motion trajectory is greater than the adjustment threshold, it can be determined, based on the distortion location, that the preset motion trajectory does not cover the effective detection range of the lidar. In this case, the lidar reselects a three-dimensional spatial trajectory and controls the object to be detected to move along this trajectory. Steps S130 to S160 are repeated for detection, and the cause of distortion is investigated based on the detection results. If the distortion degree is still greater than the adjustment threshold when detecting based on the reselected three-dimensional spatial trajectory, another three-dimensional spatial trajectory can be selected and detected again. Then, the working performance of the lidar is investigated based on the distortion degree, distortion location, and point cloud data. The reselected preset motion trajectory has a different route than the original preset motion trajectory and covers the effective working range of the lidar. Here, if all distortion degrees are greater than the adjustment threshold, a new three-dimensional spatial trajectory is selected from the multiple three-dimensional spatial trajectories, and the object to be detected moves along this trajectory.

[0085] As a specific example of determining the operating performance of the lidar, before the object to be detected moves along a preset motion trajectory, the method further includes: a step of generating the preset motion trajectory, specifically:

[0086] Select the preset motion trajectory of the object to be detected according to the model of the lidar; or,

[0087] The optimal motion trajectory is generated based on the historical detection results of the lidar, and this optimal motion trajectory is used as the preset motion trajectory.

[0088] Specifically, the preset motion trajectory can be pre-designed according to the model of the lidar and stored in the corresponding location for direct retrieval during lidar detection; or it can be generated by summarizing and comparing historical detection results of the lidar to obtain the optimal motion trajectory, and the optimal motion trajectory is used as the preset motion trajectory. Here, the optimal motion trajectory is a trajectory that can comprehensively detect the working performance of the lidar. For example, the effective working distance and field of view of the lidar can be detected by the optimal motion trajectory.

[0089] The lidar performance testing method of this disclosure involves moving the object to be tested along a preset motion trajectory and quantitatively comparing and analyzing the three-dimensional coordinate data of the preset motion trajectory with the three-dimensional coordinate information of the detected motion trajectory to obtain the working performance of the lidar. Since the preset motion trajectory is a three-dimensional spatial trajectory and covers the effective working range of the lidar under test, it can effectively detect the working performance of the lidar.

[0090] This embodiment of the invention compares the detected motion trajectory acquired by the lidar with the preset motion trajectory by moving the object to be detected along a preset three-dimensional spatial trajectory to obtain the degree of distortion of the detected motion trajectory. Based on the degree of distortion, the working performance of the lidar is determined. This detection method is simple, efficient, easy to operate, and has a wide range of applications.

[0091] Another aspect of this disclosure provides a performance testing device for lidar, such as... Figure 5 As shown, the device 200 includes:

[0092] Connection module 210 is used to establish a connection between the lidar and the object to be detected.

[0093] The receiving and identification module 220 is used to enable the lidar to receive detection commands and begin identifying the object to be detected.

[0094] The control module 230 is used to make the object to be detected move along a preset motion trajectory, wherein the preset motion trajectory is set as at least one three-dimensional spatial trajectory.

[0095] The recording module 240 is used to record the point cloud data of the motion of the object to be detected after the object to be detected is identified.

[0096] The preprocessing module 250 is used to preprocess the point cloud data to obtain the detection motion trajectory of the object to be detected.

[0097] The comparison module 260 is used to compare the detected motion trajectory with the preset motion trajectory to determine the degree of distortion of the detected motion trajectory.

[0098] The judgment module 270 is used to determine the working performance of the lidar based on the degree of distortion.

[0099] This disclosure allows for the testing of LiDAR performance using a LiDAR performance testing device. The device connects the object to be tested to the LiDAR via a connection module 210 and a receiving and identification module 220, enabling subsequent testing and simplifying the equipment. When the object to be tested is placed on a moving device, a control module 230 controls the device to move along a preset trajectory. When the object to be tested is a drone, the control module 230 can directly control the drone to move along a preset trajectory. The control module 230 can be integrated into the LiDAR. A recording module 240 is electrically connected to a preprocessing module 250. The preprocessing module 250 preprocesses the point cloud data recorded by the recording module 240 to obtain the detection trajectory of the object to be tested. A comparison module 260 calculates the distortion degree of the detection trajectory according to a pre-set method. A judgment module 270 determines the working performance of the LiDAR based on the detection trajectory of the object to be tested and the distortion degree of the preset trajectory. For example, if the distortion degree exceeds a distortion threshold, the detection range and field of view detection performance of the LiDAR malfunction. This disclosure utilizes a preset three-dimensional spatial trajectory of the object to be detected to effectively detect the working performance of the lidar, thereby improving the detection efficiency of the lidar.

[0100] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the embodiments of this disclosure, and the embodiments of this disclosure are not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the embodiments of this disclosure, and these modifications and improvements are also considered to be within the protection scope of the embodiments of this disclosure.

Claims

1. A method for testing the performance of a lidar, characterized in that, The method includes: The lidar establishes a connection with the object to be detected; The lidar receives the detection command and begins to identify the object to be detected; The object to be detected moves along a preset motion trajectory, wherein the preset motion trajectory is set as at least one three-dimensional spatial trajectory; the three-dimensional spatial trajectory covers the effective working range of the lidar in three-dimensional space; Once the object to be detected is identified, the point cloud data of the motion of the object to be detected is recorded; The point cloud data is preprocessed to obtain the detection motion trajectory of the object to be detected; By comparing the detected motion trajectory with the preset motion trajectory, the degree of distortion of the detected motion trajectory can be determined. Based on the degree of distortion, the operating performance of the lidar at different distances is determined.

2. The method according to claim 1, characterized in that, Determining the operating performance of the lidar based on the degree of distortion includes: The operating performance of the lidar is determined based on the degree of distortion obtained from multiple tests.

3. The method according to claim 1, characterized in that, Determining the operating performance of the lidar based on the degree of distortion includes: If the degree of distortion is greater than the distortion threshold, the lidar is determined to have malfunctioned.

4. The method according to any one of claims 1 to 3, characterized in that, The preprocessing of the point cloud data to obtain the detection motion trajectory of the object to be detected includes: The point cloud data is filtered to remove point clouds that do not contain the object to be detected. The filtered point cloud data is smoothed to obtain the detection trajectory of the object to be detected.

5. The method according to any one of claims 1 to 3, characterized in that, The operating performance of the lidar includes at least one of the following: Detection distance, ranging sampling rate / accuracy, horizontal and vertical field of view, scanning frequency, angular resolution, and measurement resolution / accuracy.

6. The method according to any one of claims 1 to 3, characterized in that, Before determining the operating performance of the lidar based on the degree of distortion, the method further includes: If the degree of distortion is greater than the detection threshold, the lidar controls the object to be detected to move along the preset motion trajectory again.

7. The method according to any one of claims 1 to 3, characterized in that, The preset motion trajectory is set as multiple three-dimensional spatial trajectories; before determining the working performance of the lidar based on the degree of distortion, the method further includes: If the degree of distortion is greater than the adjustment threshold, the lidar will reselect a three-dimensional spatial trajectory from the multiple three-dimensional spatial trajectories and control the object to be detected to move along the reselected three-dimensional spatial trajectory.

8. The method according to any one of claims 1 to 3, characterized in that, The preset motion trajectory is a conical spiral.

9. The method according to claim 6, characterized in that, Before the object to be detected moves along a preset motion trajectory, the method further includes: a step of generating the preset motion trajectory, specifically: Select the preset motion trajectory of the object to be detected according to the model of the lidar; or, The optimal motion trajectory is generated based on the historical detection results of the lidar, and this optimal motion trajectory is used as the preset motion trajectory.

10. The method according to claim 7, characterized in that, Before the object to be detected moves along a preset motion trajectory, the method further includes: a step of generating the preset motion trajectory, specifically: Select the preset motion trajectory of the object to be detected according to the model of the lidar; or, The optimal motion trajectory is generated based on the historical detection results of the lidar, and this optimal motion trajectory is used as the preset motion trajectory.

11. A performance testing device for lidar, characterized in that, The connection module is used to establish a connection between the lidar and the object to be detected; The receiving and identification module is used to enable the lidar to receive detection commands and begin identifying the object to be detected. The control module is used to make the object to be detected move along a preset motion trajectory, wherein the preset motion trajectory is set as at least one three-dimensional spatial trajectory; the three-dimensional spatial trajectory covers the effective working range of the lidar in three-dimensional space. The recording module is used to record the point cloud data of the motion of the object to be detected after the object is identified. The preprocessing module is used to preprocess the point cloud data to obtain the detection motion trajectory of the object to be detected; The comparison module is used to compare the detected motion trajectory with the preset motion trajectory to determine the degree of distortion of the detected motion trajectory; The judgment module is used to determine the working performance of the lidar at different distances based on the degree of distortion.

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