CN114252870A - Laser radar self-checking method, laser radar self-checking equipment and computer readable storage medium - Google Patents
Laser radar self-checking method, laser radar self-checking equipment and computer readable storage medium Download PDFInfo
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- CN114252870A CN114252870A CN202111450001.2A CN202111450001A CN114252870A CN 114252870 A CN114252870 A CN 114252870A CN 202111450001 A CN202111450001 A CN 202111450001A CN 114252870 A CN114252870 A CN 114252870A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/497—Means for monitoring or calibrating
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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Abstract
The application provides a laser radar self-checking method, a laser radar self-checking device and a computer readable storage medium. The laser radar self-checking method comprises the following steps: acquiring self-checking theoretical point cloud of the laser radar; collecting real-time self-checking distribution point cloud through a laser radar; comparing the distribution condition of the self-checking theoretical point cloud with the distribution condition of the self-checking distribution point cloud, and judging whether the distribution difference of the point clouds exceeds a preset threshold value; if yes, outputting a point cloud self-checking result, and indicating that the laser radar is abnormal by the self-checking result. By the laser radar self-checking method, whether the laser radar normally works can be checked by utilizing the self-checking theoretical point cloud and the self-checking distribution point cloud collected in real time, and self-checking efficiency and self-checking instantaneity of the laser radar are improved.
Description
Technical Field
The application relates to the technical field of radar detection, in particular to a laser radar self-checking method, self-checking equipment and a computer readable storage medium.
Background
A lidar is an electronic device that uses a laser beam to detect the range of an object. The laser radar can be used for detecting parameters such as distance, shape and the like of a static object, can also be used for detecting parameters such as shape, speed, angular speed and the like of a moving object, and is widely applied to scenes such as traffic investigation, automatic driving, robots, surveying and mapping. Because the laser radar has a precise structure, is often applied outdoors and needs to operate for a long time, faults such as electrical parameter abnormity, motor abnormity, communication abnormity and the like easily occur. Because outdoors, slight faults sometimes occur, because the faults are discovered or processed untimely, the faults are aggravated, even the laser radar device is completely damaged, and great economic loss is caused.
Currently, there are some devices or methods for detecting lidar or similar devices:
for example, one method is to use a UPS power supply as a backup power supply to prevent damage to the laser radar due to impact current at the moment of power failure, and determine whether the laser fails by determining whether the laser emits light. The method can not detect specific information when the laser fails, can not provide useful information for later maintenance and improvement, needs a main control computer for control and recording failure information, and is not practical for a laser radar device used outdoors.
The other method is to detect the abnormality of the gyro light intensity signal intensity, the power supply voltage and the like of the equipment, mainly used for fault collection and reliability evaluation, incapable of recording fault information and regulating the device, and capable of avoiding further aggravation of the device fault.
Disclosure of Invention
The application provides a laser radar self-checking method, a laser radar self-checking device and a computer readable storage medium.
The application provides a laser radar self-checking method, which comprises the following steps:
acquiring self-checking theoretical point cloud of the laser radar;
collecting real-time self-checking distribution point cloud through the laser radar;
comparing the distribution condition of the self-checking theoretical point cloud with the distribution condition of the self-checking distribution point cloud, and judging whether the distribution difference of the point clouds exceeds the preset threshold value;
if yes, outputting a point cloud self-checking result, and indicating that the laser radar is abnormal by the self-checking result.
Wherein, the distribution condition of the self-checking theoretical point cloud is as follows: the method comprises the following steps of distributing point cloud data points of a first proportion in a first distance range, distributing point cloud data points of a second proportion in a second distance range, and distributing point cloud data points of a third proportion in a third distance range;
wherein the first distance range, the second distance range and the third distance range are arranged in order of distance from near to far.
Wherein, the obtaining of the self-checking theoretical point cloud of the laser radar comprises:
acquiring height information and angle information of the laser radar;
and searching the self-checking theoretical point cloud from a preset theoretical table based on the height information and the angle information.
Wherein, the judging whether the distribution difference of the point cloud exceeds a preset threshold value comprises:
and calculating the difference between the distribution proportion of the self-checking distribution point cloud in the first distance range and the first proportion, and judging whether the difference exceeds the preset threshold value.
Wherein, the judging whether the distribution difference of the point cloud exceeds a preset threshold value comprises:
calculating the difference value between the coordinate value of each self-checking theoretical point in the self-checking theoretical point cloud and the coordinate value of the corresponding self-checking distribution point in the self-checking distribution point cloud;
accumulating all the difference values to obtain distance difference values;
and judging whether the distance difference value is larger than a preset distance difference threshold value.
Wherein the method further comprises:
and calculating a reflectivity difference value between the reflectivity of each self-checking distribution point in the self-checking distribution point cloud and the theoretical reflectivity, and judging whether the reflectivity difference value is greater than a reflectivity difference threshold value.
Wherein the determining whether the reflectivity difference value is greater than a difference threshold value comprises:
calculating the difference value between the reflectivity of each self-detection distribution point in the self-detection distribution point cloud and the reflectivity threshold;
accumulating the difference values of all the distribution points and carrying out average operation to obtain a reflectivity difference value;
and judging whether the reflectivity difference value is larger than a preset reflectivity difference threshold value.
After the real-time self-checking distribution point cloud is collected through the laser radar, the radar self-checking method further comprises the following steps:
identifying based on the self-checking distribution point cloud, and judging whether an obstacle exists;
if yes, confirming that the self-checking fails.
The application also provides laser radar self-checking equipment which comprises a processor and a memory, wherein program data are stored in the memory, and the processor is used for executing the program data to realize the laser radar self-checking method.
The application also provides a computer readable storage medium for storing program data, which when executed by a processor, is used for implementing the laser radar self-test method.
The beneficial effect of this application is: the laser radar self-checking equipment acquires self-checking theoretical point cloud of the laser radar; collecting real-time self-checking distribution point cloud through a laser radar; comparing the distribution condition of the self-checking theoretical point cloud with the distribution condition of the self-checking distribution point cloud, and judging whether the distribution difference of the point clouds exceeds a preset threshold value; if yes, outputting a point cloud self-checking result, and indicating that the laser radar is abnormal by the self-checking result. By the laser radar self-checking method, whether the laser radar normally works can be checked by utilizing the self-checking theoretical point cloud and the self-checking distribution point cloud collected in real time, and self-checking efficiency and self-checking instantaneity of the laser radar are improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. Wherein:
fig. 1 is a schematic flowchart of an embodiment of a laser radar self-inspection method provided in the present application;
FIG. 2 is a schematic flow chart diagram illustrating another embodiment of a lidar self-test method provided by the present application;
FIG. 3 is a schematic flow chart diagram illustrating a lidar self-test method according to yet another embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an embodiment of the lidar self-inspection apparatus provided in the present application;
fig. 5 is a schematic structural diagram of another embodiment of the lidar self-inspection apparatus provided in the present application;
FIG. 6 is a schematic structural diagram of an embodiment of a computer-readable storage medium provided in the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a laser radar self-inspection method according to an embodiment of the present disclosure.
The laser radar self-checking method is applied to laser radar self-checking equipment, wherein the laser radar self-checking equipment can be a server, and can also be a system formed by mutually matching the server and terminal equipment. Correspondingly, each part, such as each unit, sub-unit, module, and sub-module, included in the lidar self-test device may be all disposed in the server, or may be disposed in the server and the terminal device, respectively.
Further, the server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server. When the server is software, it may be implemented as a plurality of software or software modules, for example, software or software modules for providing distributed servers, or as a single software or software module, and is not limited herein. In some possible implementations, the laser radar self-test method according to the embodiment of the present application may be implemented by a processor calling a computer readable instruction stored in a memory.
In the embodiment of the present application, the laser radar self-inspection device, hereinafter referred to as self-inspection device, needs to calibrate the position of the laser radar. Specifically, since the laser radar is a radar system that emits a laser beam to detect a characteristic quantity such as a position and a speed of a target, in a self-checking process, a self-checking device needs to check a difference between a detection theoretical value and a detection actual value that are stored in advance by the laser radar, so that a self-checking effect is measured.
The detection theoretical value is determined by the equipment information of the laser radar on one hand and the self-checking place on the other hand. If the self-checking place is relatively messy, for example, a plurality of obstacles are arranged, or the place is uneven, the precision of the self-checking equipment in calculating the detection theoretical value is not high, a complex calculation process is needed, and a large amount of processing resources are occupied.
Therefore, in the embodiment of the application, the laser radar is selectively arranged in a normally wide and flat detection area, so that the detection theoretical value is convenient to calculate, the interference information of a place is reduced, and the detection characteristic of the laser radar can be better embodied by the detection theoretical value.
Specifically, in one mode, a worker can mark the detection point clouds of the laser radars with different heights and different angles in advance in a normally wide and flat area so as to record the self-checking theoretical point clouds with different heights and different angles and the distribution conditions of the self-checking theoretical point clouds with different heights and different angles in a preset theoretical table. In another mode, the staff can calculate the detection point clouds of the laser radars at different heights and different angles according to the equipment parameters of the laser radars when leaving the factory so as to record the self-checking theoretical point clouds at different heights and different angles and the distribution conditions thereof in a preset theoretical table.
For example, for a part of laser radars, the point cloud obtained by the laser radar detection generally shows a sparse-dense-sparse trend distribution pattern, and it is assumed that the laser radar detected in the embodiment of the present application theoretically detects that 30% of the data points of the radar point cloud in a normal open flat area 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, so that the sparse-dense-sparse trend distribution pattern is shown.
It should be noted that the lidar self-checking method of the present application is applicable to different types of lidar, such as 64-line lidar, 32-line lidar, 16-line lidar, and the like.
Specifically, as shown in fig. 1, the laser radar self-inspection method in the embodiment of the present application specifically includes the following steps:
step S11: and acquiring self-checking theoretical point cloud of the laser radar.
In the embodiment of the application, the staff creates the preset theoretical table in advance, and the preset theoretical table records the self-checking theoretical point clouds and the distribution conditions thereof corresponding to different height values and different angle value combinations.
Therefore, when the laser radar self-checking method is started, the self-checking equipment calibrates the real-time pose of the laser radar to acquire the height information and the angle information of the laser radar. Then, the self-checking equipment searches corresponding self-checking theoretical point cloud and distribution conditions thereof in a preset theoretical table based on the real-time height information and angle information of the laser radar.
Step S12: and collecting real-time self-checking distribution point cloud through a laser radar.
In the embodiment of the application, the self-checking equipment collects real-time self-checking distribution point clouds through a laser radar.
Further, since the self-checking theoretical point cloud of the embodiment of the application is detected or calculated based on a normally open and flat area, the self-checking device needs to ensure that the area where the laser radar is located during self-checking also belongs to the normally open and flat area. Therefore, after the self-checking equipment collects the real-time self-checking distribution point cloud through the laser radar, whether the obstacle can be identified can be judged based on the self-checking distribution point cloud. If the data points of the obstacles exist in the self-checking distribution point cloud, the fact that the obstacles exist in the area where the laser radar is located is indicated, the self-checking result is possibly influenced, at the moment, the self-checking equipment can directly confirm that the self-checking fails, and the self-checking process is executed again until the obstacles do not exist in the self-checking distribution point cloud detected by the laser radar. If no data point of the obstacle exists in the self-checking distribution point cloud, it indicates that the laser radar is in a normal wide and flat area, and the process proceeds to step S13.
Step S13: and comparing the distribution condition of the self-checking theoretical point cloud with the distribution condition of the self-checking distribution point cloud, and judging whether the distribution difference of the point clouds exceeds a preset threshold value.
In the embodiment of the application, the self-checking equipment compares the distribution condition of the self-checking theoretical point cloud with the distribution condition of the self-checking distribution point cloud, and judges whether the distribution difference of the point cloud exceeds a preset threshold value. For example, the distribution of the self-checking 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. And if the difference value between the data point distribution ratio of the self-checking distribution point cloud and the data point distribution ratio of the self-checking theoretical point cloud is smaller than a preset threshold value, the self-checking equipment confirms that the distribution condition of the self-checking theoretical point cloud is consistent with that of the self-checking distribution point cloud, and the self-checking is successful. Otherwise, the process proceeds to step S14.
Specifically, the distribution of the self-checking theoretical point cloud is as follows: the first distance range is distributed with point cloud data points of a first proportion, the second distance range is distributed with point cloud data points of a second proportion, and the third distance range is distributed with point cloud data points of a third proportion, wherein the first distance range, the second distance range and the third distance range are arranged according to the order of the distance from the near to the far. The self-checking equipment needs to respectively obtain distribution proportions of the self-checking distribution point cloud in a first distance range, a second distance range and a third distance range. Then, the distribution ratio of the first distance range is compared with the first ratio, the distribution ratio of the second distance range is compared with the second ratio, the distribution ratio of the third distance range is compared with the third ratio, when the difference values corresponding to all the distribution ratios are lower than the preset threshold value, the self-checking is confirmed to be successful, and when the difference value corresponding to any one distribution ratio exceeds the preset threshold value, the step S14 is performed.
The self-checking equipment can also compare the single-point characteristics of the self-checking theoretical point set with the single-point characteristics of the self-checking distribution point set, or compare the local characteristics of the self-checking theoretical point set with the local characteristics of the self-checking distribution point set, or compare the global characteristics of the self-checking theoretical point set with the global characteristics of the self-checking distribution point set.
Taking the single-point characteristic 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 distribution point set in sequence according to the acquisition sequence or the distance, so as to obtain the difference value between the self-checking theoretical point set and the self-checking distribution point set. The type of the difference value is determined by the single-point characteristic type of the self-checking theory point set and the self-checking distribution point set. The types of single point features of the point cloud include, but are not limited to, the following types: three-dimensional coordinates, normal, principal curvature, eigenvalues, echo intensity, etc.
For example, the self-inspection apparatus may generate a self-inspection theoretical point distribution curve based on the self-inspection theoretical point set, generate a self-inspection distribution point distribution curve based on the self-inspection distribution point set, and then compare curve difference values of the self-inspection theoretical point distribution curve and the self-inspection distribution point distribution curve.
When the difference value between the self-checking theoretical point set and the self-checking distribution point set is greater than a preset threshold value, confirming that the self-checking fails, and performing recalibration or checking on the laser radar, and entering step S14; and when the difference value between the self-checking theory point set and the self-checking distribution point set is less than or equal to a preset threshold value, the self-checking is confirmed to be successful. The preset threshold in the embodiment of the present application is an empirical threshold of a person skilled in the art, and the specific value is not limited herein.
Step S14: and outputting a point cloud self-checking result, wherein the self-checking result indicates that the laser radar is abnormal.
In the embodiment of the application, the laser radar self-checking equipment acquires the self-checking theoretical point cloud of the laser radar; collecting real-time self-checking distribution point cloud through a laser radar; comparing the distribution condition of the self-checking theoretical point cloud with the distribution condition of the self-checking distribution point cloud, and judging whether the distribution difference of the point clouds exceeds a preset threshold value; if yes, outputting a point cloud self-checking result, and indicating that the laser radar is abnormal by the self-checking result. By the laser radar self-checking method, whether the laser radar normally works can be checked by utilizing the self-checking theoretical point cloud and the self-checking distribution point cloud collected in real time, and self-checking efficiency and self-checking instantaneity of the laser radar are improved.
Referring to fig. 2, fig. 2 is a schematic flowchart illustrating a laser radar self-inspection method according to another embodiment of the present disclosure.
Specifically, as shown in fig. 2, the laser radar self-inspection method in the embodiment of the present application specifically includes the following steps:
step S21: and acquiring self-checking theoretical point cloud of the laser radar.
Step S22: and collecting real-time self-checking distribution point cloud through a laser radar.
In the embodiment of the present application, steps S21 to S22 are the same as steps S11 to S12 in the embodiment shown in fig. 1, and are not repeated herein.
Step S23: and calculating the difference value between the coordinate value of each self-checking theoretical point in the self-checking theoretical point cloud and the coordinate value of the corresponding self-checking distribution point in the self-checking distribution point cloud.
In the embodiment of the present application, since a normally wide and flat detection area is selected, the height information of the point cloud may be regarded as the same or ignored. Therefore, the self-checking device projects the point cloud detected by the laser radar in real time to the plane of the ground of the detection area to obtain the self-checking distribution point set of the laser radar, and at the moment, the data points in the self-checking distribution point set record the projected two-dimensional coordinate values.
Correspondingly, the data points in the self-checking theoretical point set in step S21 are also recorded with their two-dimensional coordinate values, i.e. the height coordinate values in the three-dimensional coordinate system are deleted.
In the embodiment of the application, the self-checking equipment calculates the difference value between the two-dimensional coordinate value of each self-checking theoretical point in the self-checking theoretical point set and the two-dimensional coordinate value of the corresponding self-checking distribution point in the self-checking distribution point set. The calculation order can be determined by the distance arrangement order of the data points in the self-checking theory point set and the self-checking distribution point set.
Step S24: and accumulating all the difference values to obtain distance difference values.
Step S25: and judging whether the distance difference value is larger than a preset threshold value.
In the embodiment of the application, when the distance difference between the self-checking theoretical point set and the self-checking distribution point set is greater than the preset threshold, the step S26 is executed, and the laser radar needs to be calibrated again or checked; and when the distance difference value between the self-checking theory point set and the self-checking distribution point set is less than or equal to a preset threshold value, the self-checking is confirmed to be successful. The preset threshold in the embodiment of the present application is an empirical threshold of a person skilled in the art, and the specific value is not limited herein.
Step S26: and outputting a point cloud self-checking result, wherein the self-checking result indicates that the laser radar is abnormal.
Referring to fig. 3, fig. 3 is a schematic flowchart illustrating a laser radar self-inspection method according to another embodiment of the present disclosure.
Specifically, as shown in fig. 3, the laser radar self-inspection method according to the embodiment of the present application specifically includes the following steps:
step S31: and searching the reflectivity threshold of the self-checking theoretical point cloud from a preset theoretical table based on the height information and the angle information.
Step S32: and collecting real-time self-checking distribution point clouds through a laser radar, and obtaining the reflectivity of each self-checking distribution point in the self-checking distribution point clouds.
In the embodiment of the present application, steps S31 to S32 are substantially the same as steps S11 to S12 in the embodiment shown in fig. 1, and are not repeated herein.
Step S33: and calculating the difference value between the reflectivity of each self-detection distribution point in the self-detection distribution point cloud and the reflectivity threshold value.
Step S34: and accumulating the difference values of all the distribution points and carrying out average operation to obtain the reflectivity difference value.
In the embodiment of the 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 average operation to obtain the reflectivity difference value.
Step S35: and judging whether the reflectivity difference value is larger than a preset threshold value or not.
In the embodiment of the application, when the reflectivity difference value between the self-checking theoretical point set and the self-checking distribution point set is greater than the preset threshold value, the step S38 is executed, and recalibration or inspection of the laser radar is required; and when the reflectivity difference value of the self-checking theory point set and the self-checking distribution point set is less than or equal to a preset threshold value, the self-checking is confirmed to be successful. The preset threshold in the embodiment of the present application is an empirical threshold of a person skilled in the art, and the specific value is not limited herein.
Step S36: and outputting a point cloud self-checking result, wherein the self-checking result indicates that the laser radar is abnormal.
It will be understood by those skilled in the art that in the method of the present invention, the order of writing the steps does not imply a strict order of execution and any limitations on the implementation, and the specific order of execution of the steps should be determined by their function and possible inherent logic.
In order to implement the laser radar self-checking method of the foregoing embodiment, the present application further provides a laser radar self-checking device, and specifically refer to fig. 4, where fig. 4 is a schematic structural diagram of an embodiment of the laser radar self-checking device provided in the present application.
As shown in fig. 4, the lidar self-test apparatus 400 provided by the present application includes a theoretical acquisition module 41, a real-time acquisition module 42, and a distributed self-test module 43.
The theoretical acquisition module 41 is configured to acquire a self-checking theoretical point cloud of the laser radar.
And the real-time acquisition module 42 is used for acquiring real-time self-checking distribution point cloud through the laser radar.
A distribution self-checking module 43, configured to compare the distribution condition of the self-checking theoretical point cloud with the distribution condition of the self-checking distribution point cloud, and determine whether the distribution difference of the point clouds exceeds the preset threshold; if yes, outputting a point cloud self-checking result, and indicating that the laser radar is abnormal by the self-checking result.
In order to implement the laser radar self-checking method according to the foregoing embodiment, the present application further provides another laser radar self-checking device, and specifically refer to fig. 5, where fig. 5 is a schematic structural diagram of another embodiment of the laser radar self-checking device according to the present application.
The lidar self-test equipment 500 of the embodiment of the application comprises a memory 51 and a processor 52, wherein the memory 51 and the processor 52 are coupled.
The memory 51 is used for storing program data, and the processor 52 is used for executing the program data to implement the lidar self-test method described in the above embodiments.
In the present embodiment, the processor 52 may also be referred to as a CPU (Central Processing Unit). Processor 52 may be an integrated circuit chip having signal processing capabilities. The processor 52 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor 52 may be any conventional processor or the like.
To implement the lidar self-testing method of the above embodiment, the present application further provides a computer-readable storage medium, as shown in fig. 6, wherein the computer-readable storage medium 600 is used for storing program data 61, and when the program data 61 is executed by the processor, the program data is used to implement the lidar self-testing method of the above embodiment.
The present application further provides a computer program product, wherein the computer program product includes a computer program operable to cause a computer to execute the lidar self-inspection method according to the embodiment of the present application. The computer program product may be a software installation package.
The lidar self-checking method according to the above embodiments of the present application may be implemented in the form of a software functional unit, and may be stored in a device, for example, a computer readable storage medium, when the lidar self-checking method is sold or used as an independent product. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (10)
1. A laser radar self-checking method is characterized by comprising the following steps:
acquiring self-checking theoretical point cloud of the laser radar;
collecting real-time self-checking distribution point cloud through the laser radar;
comparing the distribution condition of the self-checking theoretical point cloud with the distribution condition of the self-checking distribution point cloud, and judging whether the distribution difference of the point clouds exceeds the preset threshold value;
if yes, outputting a point cloud self-checking result, and indicating that the laser radar is abnormal by the self-checking result.
2. The lidar self-test method according to claim 1,
the distribution condition of the self-checking theoretical point cloud is as follows: the method comprises the following steps of distributing point cloud data points of a first proportion in a first distance range, distributing point cloud data points of a second proportion in a second distance range, and distributing point cloud data points of a third proportion in a third distance range;
wherein the first distance range, the second distance range and the third distance range are arranged in order of distance from near to far.
3. The lidar self-test method according to claim 1 or 2,
the acquiring of the self-checking theoretical point cloud of the laser radar comprises the following steps:
acquiring height information and angle information of the laser radar;
and searching the self-checking theoretical point cloud from a preset theoretical table based on the height information and the angle information.
4. The lidar self-inspection method according to claim 2, wherein the determining whether the distribution difference of the point cloud exceeds a preset threshold comprises:
and calculating the difference between the distribution proportion of the self-checking distribution point cloud in the first distance range and the first proportion, and judging whether the difference exceeds the preset threshold value.
5. The lidar self-test method according to claim 1,
the judging whether the distribution difference of the point cloud exceeds a preset threshold value comprises the following steps:
calculating the difference value between the coordinate value of each self-checking theoretical point in the self-checking theoretical point cloud and the coordinate value of the corresponding self-checking distribution point in the self-checking distribution point cloud;
accumulating all the difference values to obtain distance difference values;
and judging whether the distance difference value is larger than a preset distance difference threshold value.
6. The lidar self-test method according to claim 5,
the method further comprises the following steps:
and calculating a reflectivity difference value between the reflectivity of each self-checking distribution point in the self-checking distribution point cloud and the theoretical reflectivity, and judging whether the reflectivity difference value is greater than a reflectivity difference threshold value.
7. The lidar self-test method according to claim 6,
wherein the determining whether the reflectivity difference value is greater than a difference threshold value comprises:
calculating the difference value between the reflectivity of each self-detection distribution point in the self-detection distribution point cloud and the reflectivity threshold;
accumulating the difference values of all the distribution points and carrying out average operation to obtain a reflectivity difference value;
and judging whether the reflectivity difference value is larger than a preset reflectivity difference threshold value.
8. The lidar self-test method according to claim 1,
after the real-time self-checking distribution point cloud is collected through the laser radar, the radar self-checking method further comprises the following steps:
identifying based on the self-checking distribution point cloud, and judging whether an obstacle exists;
if yes, confirming that the self-checking fails.
9. Lidar self-test device, characterized in that the lidar self-test device comprises a processor and a memory, the memory having stored therein program data, the processor being configured to execute the program data for implementing the lidar self-test method according to any of claims 1-8.
10. A computer-readable storage medium for storing program data, which when executed by a processor, is configured to implement the lidar self-test method of any of claims 1-8.
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| CN115047471A (en) * | 2022-03-30 | 2022-09-13 | 北京一径科技有限公司 | Method, device and equipment for determining laser radar point cloud layering and storage medium |
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| CN115047471A (en) * | 2022-03-30 | 2022-09-13 | 北京一径科技有限公司 | Method, device and equipment for determining laser radar point cloud layering and storage medium |
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