LiDAR Self-Testing Method, Its Self-Testing Device, and Computer-Readable Storage Medium

Abstract

The present application provides a method for self-checking a lidar, a self-checking device therefor, and a computer-readable storage medium. The method for self-checking the lidar includes: obtaining a theoretical point cloud for self-checking of the lidar; collecting a real-time self-checking distributed point cloud through the lidar; comparing the distribution of the theoretical point cloud for self-checking with the distribution of the self-checking distributed point cloud to determine whether the distribution difference of the point clouds exceeds a preset threshold; if so, outputting a point cloud self-checking result, where the self-checking result indicates that the lidar is abnormal. Through the above method, the method for self-checking the lidar in the present application can use the theoretical point cloud for self-checking and the self-checking distributed point cloud collected in real time to check whether the lidar is working properly, improving the self-checking efficiency and real-time performance of the lidar.

Description

Technical Field

[0001] The present application relates to the technical field of radar detection, and particularly relates to a method and device for self-checking a lidar and a computer-readable storage medium. Background Art

[0002] A lidar is an electronic device that uses laser beams to detect the distance to a target. The lidar can be used to detect parameters such as the distance and shape of a stationary object, and can also be used to detect parameters such as the shape, speed, and angular velocity of a moving object. It is widely used in traffic surveys, autonomous driving, robotics, mapping, and other scenarios. Because the lidar has a precise structure, is often used outdoors, and needs to run for a long time, it is prone to faults such as abnormal electrical parameters, abnormal motors, and abnormal communications. Because it is outdoors, sometimes minor faults occur. Because they are not discovered or processed in time, the faults are aggravated, and even the lidar device is completely damaged, causing great economic losses.

[0003] Currently, there are some devices or methods for detecting lidars or similar devices:

[0004] For example, one method is to use a UPS power supply as a backup power supply to prevent damage to the lidar caused by the impact current at the moment of power failure. At the same time, by judging whether the laser emits light, it is judged whether the laser fails. This method cannot detect the specific information when the laser fails, and cannot provide useful information for later maintenance and improvement. Moreover, it requires a main control computer to control and record the fault information, which is not practical for lidar devices used outdoors.

[0005] Another method is to detect abnormalities such as the gyro light intensity signal intensity and power supply voltage of the device, mainly for fault collection and reliability assessment. It cannot record fault information and cannot perform adjustment operations on the device to avoid further aggravation of the device fault. Summary of the Invention

[0006] The present application provides a method and device for self-checking a lidar and a computer-readable storage medium.

[0007] The present application provides a method for self-checking a lidar, and the method for self-checking a lidar includes:

[0008] Obtaining the theoretical self-check point cloud of the lidar;

[0009] Collecting the real-time self-check distribution point cloud through the lidar;

[0010] Comparing the distribution of the theoretical self-check point cloud with the distribution of the self-check distribution point cloud, and judging whether the distribution difference of the point cloud exceeds the preset threshold;

[0011] If so, output the self-check result of the point cloud, and the self-check result indicates that there is an abnormality in the lidar.

[0012] Among them, the distribution of the self-check theoretical point cloud is as follows: a first proportion of point cloud data points are distributed within a first distance range, a second proportion of point cloud data points are distributed within a second distance range, and a third proportion of point cloud data points are distributed within a third distance range;

[0013] Among them, the first distance range, the second distance range, and the third distance range are set in the order from near to far.

[0014] Among them, obtaining the self-check theoretical point cloud of the lidar includes:

[0015] Obtain the height information and angle information of the lidar;

[0016] Based on the height information and angle information, search for the self-check theoretical point cloud from a preset theoretical table.

[0017] Among them, determining whether the distribution difference of the point cloud exceeds a preset threshold includes:

[0018] Calculate the difference between the distribution ratio of the self-check distribution point cloud within the first distance range and the first ratio, and determine whether the difference exceeds the preset threshold.

[0019] Among them, determining whether the distribution difference of the point cloud exceeds a preset threshold includes:

[0020] Calculate the difference value between the coordinate value of each self-check theoretical point in the self-check theoretical point cloud and the coordinate value of the corresponding self-check distribution point in the self-check distribution point cloud;

[0021] Accumulate all the difference values to obtain a distance difference value;

[0022] Determine whether the distance difference value is greater than a preset distance difference threshold.

[0023] Among them, the method further includes:

[0024] Calculate the reflectivity difference value between the reflectivity of each self-check distribution point in the self-check distribution point cloud and the theoretical reflectivity, and determine whether the reflectivity difference value is greater than the reflectivity difference threshold.

[0025] Among them, determining whether the reflectivity difference value is greater than the difference threshold includes:

[0026] Calculate the difference value between the reflectivity of each self-check distribution point in the self-check distribution point cloud and the reflectivity threshold;

[0027] Accumulate and average the difference values of all distribution points to obtain a reflectivity difference value;

[0028] Determine whether the reflectivity difference value is greater than a preset reflectivity difference threshold.

[0029] Wherein, after collecting the real-time self-check distribution point cloud through the lidar, the lidar self-check method further includes:

[0030] Identify based on the self-check distribution point cloud and determine whether there is an obstacle;

[0031] If so, confirm that the self-check fails.

[0032] This application also provides a lidar self-check device, which includes a processor and a memory. Program data is stored in the memory, and the processor is used to execute the program data to implement the lidar self-check method as described above.

[0033] This application also provides a computer-readable storage medium, which is used to store program data. When the program data is executed by a processor, it is used to implement the above-mentioned lidar self-check method.

[0034] The beneficial effects of this application are as follows: The lidar self-check device obtains the theoretical self-check point cloud of the lidar; collects the real-time self-check distribution point cloud through the lidar; compares the distribution of the theoretical self-check point cloud with the distribution of the self-check distribution point cloud to determine whether the distribution difference of the point cloud exceeds a preset threshold; if so, outputs the point cloud self-check result, and the self-check result indicates that there is an abnormality in the lidar. Through the above method, the lidar self-check method of this application can use the theoretical self-check point cloud and the real-time collected self-check distribution point cloud to check whether the lidar is working properly, improving the self-check efficiency and real-time performance of the lidar. Description of the Drawings

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them:

[0036] Figure 1 It is a schematic flowchart of an embodiment of the lidar self-check method provided by this application;

[0037] Figure 2 It is a schematic flowchart of another embodiment of the lidar self-check method provided by this application;

[0038] Figure 3 It is a schematic flowchart of yet another embodiment of the lidar self-check method provided by this application;

[0039] Figure 4 It is a schematic structural diagram of an embodiment of the lidar self-checking device provided by this application;

[0040] Figure 5 It is a schematic structural diagram of another embodiment of the lidar self-checking device provided by this application;

[0041] Figure 6 It is a schematic structural diagram of an embodiment of the computer-readable storage medium provided by this application. Specific embodiments

[0042] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0043] Please refer to Figure 1 , Figure 1 It is a schematic flowchart of an embodiment of the lidar self-checking method provided by this application.

[0044] Among them, the lidar self-checking method of this application is applied to a lidar self-checking device. Among them, the lidar self-checking device of this application can be a server or a system in which the server and the terminal device cooperate with each other. Correspondingly, each part included in the lidar self-checking device, such as each unit, sub-unit, module, and sub-module, can be all set in the server, or can be respectively set in the server and the terminal device.

[0045] Furthermore, the above-mentioned server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules, such as software or software modules used to provide a distributed server, or can be implemented as a single software or software module, which is not specifically limited here. In some possible implementation manners, the lidar self-checking method of the embodiments of this application can be implemented by a processor calling computer-readable instructions stored in a memory.

[0046] In the embodiments of this application, the lidar self-checking device, hereinafter simply referred to as the self-checking device, needs to calibrate the position of the lidar. Specifically, since the lidar is a radar system that emits laser beams to detect the position, speed and other characteristic quantities of the target, during the self-check process, the self-checking device needs to check the difference between the pre-stored detection theoretical value and the detection actual value of the lidar, so as to measure the effect of the self-check.

[0047] The detected theoretical value is determined by the device information of the lidar on the one hand and the self-checking site on the other hand. If the self-checking site is relatively cluttered, for example, there are many obstacles set up, or the terrain of the site is uneven, it will lead to low accuracy when the self-checking device calculates the detected theoretical value, and a complex calculation process is required, occupying a large amount of processing resources.

[0048] Therefore, in the embodiments of the present application, it is selected to set the lidar in a normal open and flat detection area, so that the detected theoretical value is convenient to calculate, and the interference information of the site is reduced, so that the detected theoretical value can better reflect the detection characteristics of the lidar.

[0049] Specifically, in one way, the staff can calibrate the detection point clouds of the lidar at different heights and different angles in a normal open and flat area, so as to record the self-checking theoretical point clouds at different heights and different angles and their distribution in a preset theoretical table. In another way, the staff can calculate the detection point clouds of the lidar at different heights and different angles according to the device parameters when the lidar leaves the factory, so as to record the self-checking theoretical point clouds at different heights and different angles and their distribution in a preset theoretical table.

[0050] For example, for some lidars, the point clouds detected by the lidar generally show a sparse-dense-sparse trend distribution pattern. It is assumed that in the embodiments of the present application, theoretically, 30% of the radar point clouds detected by the lidar in a normal open and flat area should have data points within 50 meters, 50% of the data points are between 50 meters and 100 meters, and 20% of the data points are outside 100 meters, showing a sparse-dense-sparse trend distribution pattern.

[0051] It should be noted that the lidar self-checking method of the present application is applicable to different types of lidars, such as 64-line lidars, 32-line lidars, 16-line lidars, etc.

[0052] Specifically, as Figure 1 shown, the lidar self-checking method of the embodiments of the present application specifically includes the following steps:

[0053] Step S11: Obtain the self-checking theoretical point cloud of the lidar.

[0054] In the embodiments of the present application, since the staff has made a preset theoretical table in advance, the preset theoretical table records the self-checking theoretical point clouds and their distribution corresponding to the combinations of different height values and different angle values.

[0055] Therefore, when the lidar self-check method according to the embodiments of the present application is started, the self-check device calibrates the real-time pose of the lidar to obtain the height information and angle information of the lidar. Then, the self-check device looks up the corresponding self-check theoretical point cloud and its distribution in the preset theoretical table based on the real-time height information and angle information of the lidar.

[0056] Step S12: Collect the real-time self-check distribution point cloud through the lidar.

[0057] In the embodiments of the present application, the self-check device collects the real-time self-check distribution point cloud through the lidar.

[0058] Furthermore, since the self-check theoretical point cloud in the embodiments of the present application is detected or calculated based on a normal open and flat area, the self-check device needs to ensure that the area where the lidar is located during self-check also belongs to a normal open and flat area. Therefore, after the self-check device collects the real-time self-check distribution point cloud through the lidar, it can judge whether obstacles can be recognized based on the self-check distribution point cloud. If there are data points of obstacles in the self-check distribution point cloud, it means that there are obstacles in the area where the lidar is located, which may affect the self-check result. At this time, the self-check device can directly confirm that the self-check fails. Until there are no data points of obstacles in the self-check distribution point cloud detected by the lidar, the self-check process is restarted. If there are no data points of obstacles in the self-check distribution point cloud, it means that the lidar is in a normal open and flat area, and step S13 is entered.

[0059] Step S13: Compare the distribution of the self-check theoretical point cloud with the distribution of the self-check distribution point cloud, and judge whether the distribution difference of the point cloud exceeds a preset threshold.

[0060] In the embodiments of the present application, the self-check device compares the distribution of the self-check theoretical point cloud with the distribution of the self-check distribution point cloud, and judges whether the distribution difference of the point cloud exceeds a preset threshold. For example, the distribution of the self-check theoretical point cloud is: 30% of the data points are within 50 meters, 50% of the data points are between 50 meters and 100 meters, and 20% of the data points are outside 100 meters. The difference value between the data point distribution ratio of the self-check distribution point cloud and the data point distribution ratio of the self-check theoretical point cloud should be less than the preset threshold, then the self-check device confirms that the distribution of the self-check theoretical point cloud is consistent with the distribution of the self-check distribution point cloud, and confirms that the self-check is successful. Otherwise, step S14 is entered.

[0061] Specifically, the distribution of the self-checking theoretical point cloud is as follows: within the first distance range, the first proportion of point cloud data points is distributed; within the second distance range, the second proportion of point cloud data points is distributed; within the third distance range, the third proportion of point cloud data points is distributed. Among them, the first distance range, the second distance range, and the third distance range are set in the order from near to far. The self-checking device needs to obtain the distribution ratios of the self-checking distributed point cloud within the first distance range, the second distance range, and the third distance range respectively. Then, compare the distribution ratio of the first distance range with the first proportion, compare the distribution ratio of the second distance range with the second proportion, and compare the distribution ratio of the third distance range with the third proportion. When all the difference values corresponding to the distribution ratios are lower than the preset threshold, it is confirmed that the self-check is successful. When the difference value corresponding to any one of the distribution ratios exceeds the preset threshold, go to step S14.

[0062] The self-checking device can also compare the single-point features of the self-checking theoretical point set with the single-point features of the self-checking distributed point set, or compare the local features of the self-checking theoretical point set with the local features of the self-checking distributed point set, or compare the global features of the self-checking theoretical point set with the global features of the self-checking distributed point set.

[0063] Taking the single-point feature as an example, the self-checking device compares the data points of the self-checking theoretical point set and the data points of the self-checking distributed point set in sequence according to the acquisition order or distance, so as to obtain the difference value between the self-checking theoretical point set and the self-checking distributed point set. Among them, the type of the difference value is determined by the single-point feature types of the self-checking theoretical point set and the self-checking distributed point set. The single-point feature types of the point cloud include but are not limited to the following types: three-dimensional coordinates, normal vectors, principal curvatures, eigenvalues, echo intensities, etc.

[0064] For example, the self-checking device can generate a self-checking theoretical point distribution curve based on the self-checking theoretical point set, generate a self-checking distributed point distribution curve based on the self-checking distributed point set, and then compare the curve difference value between the self-checking theoretical point distribution curve and the self-checking distributed point distribution curve.

[0065] When the difference value between the self-checking theoretical point set and the self-checking distributed point set is greater than the preset threshold, it is confirmed that the self-check fails, and it is necessary to recalibrate or check the lidar, and go to step S14; when the difference value between the self-checking theoretical point set and the self-checking distributed point set is less than or equal to the preset threshold, it is confirmed that the self-check is successful. Among them, the preset threshold in the embodiments of the present application is an empirical threshold for those skilled in the art, and specific values are not limited herein.

[0066] Step S14: Output the point cloud self-check result, and the self-check result indicates that there is an abnormality in the lidar.

[0067] In the embodiment of the present application, the lidar self-checking device obtains the theoretical point cloud of the lidar self-check; collects the real-time self-check distribution point cloud through the lidar; compares the distribution of the theoretical point cloud of the self-check with the distribution of the self-check distribution point cloud to determine whether the distribution difference of the point cloud exceeds a preset threshold; if so, outputs the point cloud self-check result, and the self-check result indicates that there is an abnormality in the lidar. Through the above method, the lidar self-checking method of the present application can use the theoretical point cloud of the self-check and the self-check distribution point cloud collected in real time to check whether the lidar is working properly, improving the self-check efficiency and real-time performance of the lidar.

[0068] Please continue to refer to Figure 2 , Figure 2 which is a schematic flowchart of another embodiment of the lidar self-checking method provided by the present application.

[0069] Specifically, as Figure 2 shown, the lidar self-checking method of the embodiment of the present application specifically includes the following steps:

[0070] Step S21: Obtain the theoretical point cloud of the lidar self-check.

[0071] Step S22: Collect the real-time self-check distribution point cloud through the lidar.

[0072] In the embodiment of the present application, steps S21 to S22 are the same as steps S11 to S12 in the Figure 1 embodiment shown, and will not be elaborated here.

[0073] Step S23: Calculate the difference value between the coordinate value of each self-check theoretical point in the self-check theoretical point cloud and the coordinate value of the corresponding self-check distribution point in the self-check distribution point cloud.

[0074] In the embodiment of the present application, since a normal, open, flat detection area is selected, the height information of the point cloud can be considered the same or negligible. Therefore, the self-checking device projects the point cloud detected by the lidar in real time onto the plane where the ground of the detection area is located to obtain the self-check distribution point set of the lidar. At this time, the data points in the self-check distribution point set record their projected two-dimensional coordinate values.

[0075] Correspondingly, the data points in the self-check theoretical point set in step S21 also record their two-dimensional coordinate values, that is, the height coordinate values in the three-dimensional coordinate system are deleted.

[0076] In the embodiment of the present application, the self-checking device calculates the difference value between the two-dimensional coordinate value of each self-check theoretical point in the self-check theoretical point set and the two-dimensional coordinate value of the corresponding self-check distribution point in the self-check distribution point set. Among them, the calculation order can be determined by the distance arrangement order of the data points in the self-check theoretical point set and the self-check distribution point set.

[0077] Step S24: Accumulate all the difference values to obtain a distance difference value.

[0078] Step S25: Determine whether the distance difference value is greater than a preset threshold.

[0079] In the embodiment of the present application, when the distance difference value between the self-checking theoretical point set and the self-checking distribution point set is greater than the preset threshold, go to Step S26, and it is necessary to recalibrate or check the lidar; when the distance difference value between the self-checking theoretical point set and the self-checking distribution point set is less than or equal to the preset threshold, confirm that the self-check is successful. Among them, the preset threshold in the embodiment of the present application is an empirical threshold of those skilled in the art, and no specific value is limited here.

[0080] Step S26: Output the point cloud self-check result, and the self-check result indicates that there is an abnormality in the lidar.

[0081] Please continue to refer to Figure 3 , Figure 3 which is a schematic flowchart of another embodiment of the lidar self-checking method provided by the present application.

[0082] Specifically, as Figure 3 shown, the lidar self-checking method in the embodiment of the present application specifically includes the following steps:

[0083] Step S31: Search for the reflectivity threshold of the self-checking theoretical point cloud from a preset theoretical table based on the height information and the angle information.

[0084] Step S32: Collect the real-time self-checking distribution point cloud through the lidar, and obtain the reflectivity of each self-checking distribution point in the self-checking distribution point cloud.

[0085] In the embodiment of the present application, Steps S31 to S32 are basically the same as Figure 1 Steps S11 to S12 in the embodiment shown, and will not be elaborated here.

[0086] Step S33: Calculate the difference value between the reflectivity of each self-checking distribution point in the self-checking distribution point cloud and the reflectivity threshold.

[0087] Step S34: Accumulate the difference values of all the distribution points and perform an averaging operation to obtain a reflectivity difference value.

[0088] In the embodiment of the present application, the self-checking device compares the reflectivity of each self-checking distribution point in the self-checking distribution point set with the reflectivity threshold to obtain the difference value between the reflectivity of each self-checking distribution point and the reflectivity threshold, and then accumulates all the difference values and performs an averaging operation to obtain a reflectivity difference value.

[0089] Step S35: Determine whether the reflectivity difference value is greater than a preset threshold.

[0090] In an embodiment of the present application, when the reflectivity difference value between the self-checking theoretical point set and the self-checking distribution point set is greater than a preset threshold, step S38 is entered, and recalibration or inspection of the lidar needs to be performed; when the reflectivity difference value between the self-checking theoretical point set and the self-checking distribution point set is less than or equal to the preset threshold, it is confirmed that the self-check is successful. Among them, the preset threshold in the embodiment of the present application is an empirical threshold for those skilled in the art, and no specific value is limited here.

[0091] Step S36: Output the point cloud self-check result, and the self-check result indicates that there is an abnormality in the lidar.

[0092] Those skilled in the art can understand that in the above method of the specific embodiment, the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined according to its function and possible internal logic.

[0093] To implement the lidar self-check method of the above embodiment, the present application also proposes a lidar self-check device. For details, please refer to Figure 4 , Figure 4 is a schematic structural diagram of an embodiment of the lidar self-check device provided by the present application.

[0094] As Figure 4 shown, the lidar self-check device 400 provided by the present application includes a theoretical acquisition module 41, a real-time acquisition module 42, and a distribution self-check module 43.

[0095] Among them, the theoretical acquisition module 41 is used to acquire the self-checking theoretical point cloud of the lidar.

[0096] The real-time acquisition module 42 is used to collect the real-time self-checking distribution point cloud through the lidar.

[0097] The distribution self-check module 43 is used to compare the distribution of the self-checking theoretical point cloud with the distribution of the self-checking distribution point cloud, and determine whether the distribution difference of the point cloud exceeds the preset threshold; if so, output the point cloud self-check result, and the self-check result indicates that there is an abnormality in the lidar.

[0098] To implement the lidar self-check method of the above embodiment, the present application also proposes another lidar self-check device. For details, please refer to Figure 5 , Figure 5 is a schematic structural diagram of another embodiment of the lidar self-check device provided by the present application.

[0099] The lidar self-check device 500 in the embodiment of the present application includes a memory 51 and a processor 52, wherein the memory 51 and the processor 52 are coupled.

[0100] The memory 51 is used to store program data, and the processor 52 is used to execute the program data to implement the lidar self-check method described in the above embodiments.

[0101] In this embodiment, the processor 52 can also be referred to as a CPU (Central Processing Unit). The processor 52 may be an integrated circuit chip with signal processing capabilities. The processor 52 can also be a general-purpose processor, a digital signal processor (DSP, Digital Signal Process), an application-specific integrated circuit (ASIC, ApplicationSpecific Integrated Circuit), a field-programmable gate array (FPGA, Field Programmable GateArray), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor 52 can also be any conventional processor, etc.

[0102] To implement the lidar self-check method of the above embodiments, the present application also provides a computer-readable storage medium, such as Figure 6 As shown, the computer-readable storage medium 600 is used to store program data 61, and when the program data 61 is executed by the processor, it is used to implement the lidar self-check method described in the above embodiments.

[0103] The present application also provides a computer program product, wherein the computer program product includes a computer program, and the computer program is operable to cause a computer to execute the lidar self-check method described in the embodiments of the present application. The computer program product can be a software installation package.

[0104] When the lidar self-check method described in the above embodiments of the present application exists in the form of a software functional unit and is sold or used as an independent product, it can be stored in a device, such as a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical disks, etc., which can store program codes.

[0105] The above are only the embodiments of the present application, and do not limit the patent scope of the present application accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present application.

Claims

1. A lidar self-checking method, characterized in that, The lidar self-check method includes: Obtaining the self-check theoretical point cloud of the lidar; Collecting the real-time self-check distributed point cloud through the lidar; Comparing the distribution of the self-check theoretical point cloud with the distribution of the self-check distributed point cloud, and determining whether the distribution difference of the point cloud exceeds a preset threshold; If so, outputting the point cloud self-check result, and the self-check result indicates that the lidar is abnormal; The distribution of the self-check theoretical point cloud is: a first proportion of point cloud data points are distributed within a first distance range, a second proportion of point cloud data points are distributed within a second distance range, and a third proportion of point cloud data points are distributed within a third distance range; Among them, the first distance range, the second distance range, and the third distance range are set in the order from near to far.

2. The lidar self-check method according to claim 1, wherein The obtaining of the self-check theoretical point cloud of the lidar includes: Obtaining the height information and angle information of the lidar; Searching for the self-check theoretical point cloud from a preset theoretical table based on the height information and angle information.

3. The lidar self-checking method according to claim 1, wherein The determining whether the distribution difference of the point cloud exceeds a preset threshold includes: Calculating the difference between the distribution ratio of the self-check distributed point cloud within the first distance range and the first ratio, and determining whether the difference exceeds the preset threshold.

4. The lidar self-check method according to claim 1, wherein The determining whether the distribution difference of the point cloud exceeds a preset threshold includes: Calculating the difference value between the coordinate value of each self-check theoretical point in the self-check theoretical point cloud and the coordinate value of the corresponding self-check distributed point in the self-check distributed point cloud; Accumulating all the difference values to obtain a distance difference value; Determining whether the distance difference value is greater than a preset distance difference threshold.

5. The lidar self-check method according to claim 4, wherein The method further includes: Calculating the reflectivity difference value between the reflectivity of each self-check distributed point in the self-check distributed point cloud and the theoretical reflectivity, and determining whether the reflectivity difference value is greater than a reflectivity difference threshold.

6. The lidar self-check method according to claim 5, wherein The determining whether the reflectivity difference value is greater than a difference threshold includes: Calculating the difference value between the reflectivity of each self-check distributed point in the self-check distributed point cloud and the reflectivity threshold; Accumulating and averaging the difference values of all distributed points to obtain a reflectivity difference value; Determining whether the reflectivity difference value is greater than a preset reflectivity difference threshold.

7. The lidar self-check method according to claim 1, wherein After collecting the real-time self-check distributed point cloud through the lidar, the lidar self-check method further includes: Identifying based on the self-check distributed point cloud and determining whether there is an obstacle; If so, confirming that the self-check fails.

8. A lidar self-checking device, characterized in that, The lidar self-check device includes a processor and a memory, and program data is stored in the memory. The processor is configured to execute the program data to implement the lidar self-check method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used for storing program data, and when the program data is executed by a processor, it is used to implement the lidar self-checking method according to any one of claims 1-7.

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