CN114296044A - Laser radar fault diagnosis method and device - Google Patents
Laser radar fault diagnosis method and device Download PDFInfo
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Abstract
The application provides a laser radar fault diagnosis method and a laser radar fault diagnosis device, wherein the method comprises the following steps: under the condition that the voltage of each subsystem included in the laser radar to be diagnosed is within a set voltage threshold range, determining whether a communication channel corresponding to the laser radar to be diagnosed has a fault or not according to a preset communication protocol, a preset protection measure and a data integrity protection algorithm; and under the condition that the communication channel is not in fault and meets the characteristic matching triggering condition, performing functional diagnosis according to the received point cloud information of the target object to determine the fault state of the laser radar to be diagnosed. Therefore, the method and the device can realize fault diagnosis of the laser radar to be diagnosed, reduce the difficulty and workload of manual inspection, and improve the fault diagnosis efficiency of the laser radar to be diagnosed.
Description
Technical Field
The application relates to the technical field of laser radars, in particular to a laser radar fault diagnosis method and device.
Background
The laser radar is a radar system which can emit laser beam to detect the position, speed and other characteristic quantities of target, and its working principle is that it can emit detection signal (laser beam) to target, and can receive the signal returned by target (target echo), then can compare the target echo with detection signal, and after proper treatment, can obtain the related information of target, such as target distance, azimuth, height, speed, attitude and even form, so that it can detect, track and identify target.
If the laser radar fails, the obtained related information of the target is inaccurate, and therefore the detection, tracking and identification results of the target are influenced. At present, whether the laser radar has faults or not is mainly determined through a manual checking mode, but the manual checking mode has the problems of untimely manual checking, complex flow, large workload and the like, so that the efficiency of laser radar fault diagnosis is low.
Disclosure of Invention
In view of this, the present application provides a laser radar fault diagnosis method and apparatus, which are used to improve laser radar fault diagnosis efficiency, and the technical scheme is as follows:
a laser radar fault diagnosis method is applied to a laser radar controller and comprises the following steps:
under the condition that the voltage of each subsystem included in the laser radar to be diagnosed is within a set voltage threshold range, determining whether a communication channel corresponding to the laser radar to be diagnosed is in fault according to a preset communication protocol, a preset protection measure and a data integrity protection algorithm, wherein the communication channel is used for transmitting point cloud information of a target object;
and under the condition that the communication channel is not in fault and meets the characteristic matching triggering condition, performing functional diagnosis according to the received point cloud information of the target object to determine the fault state of the laser radar to be diagnosed.
Optionally, the point cloud information of the target object includes first point cloud information and second point cloud information, where the first point cloud information is obtained in any scene, the second point cloud information is obtained in a situation where the X axis of the laser radar to be diagnosed is perpendicular to the target surface, and the test distance requirement is that the point cloud information is obtained in a situation where laser beams emitted by all lasers included in the laser radar to be diagnosed can scan the target surface, and the target surface is a plane or a curved surface with a known shape;
according to the received point cloud information of the target object, performing functional diagnosis to determine the fault state of the laser radar to be diagnosed, wherein the method comprises the following steps:
calculating a characteristic correlation value according to the first point cloud information and a pre-stored standard point cloud characteristic, wherein the standard point cloud characteristic is a point cloud characteristic determined according to point cloud information of a target object obtained by a normal laser radar;
and under the condition that whether the characteristic correlation value is smaller than a set correlation threshold value or not, determining the fault state of the laser radar to be diagnosed according to the second point cloud information.
Optionally, the standard point cloud features include: standard projection profile features and standard geometric features;
calculating a characteristic correlation value according to the first point cloud information and a pre-stored standard point cloud characteristic, wherein the calculation comprises the following steps:
calculating a projection contour correlation coefficient corresponding to the first point cloud information according to the first point cloud information and the standard projection contour characteristics;
calculating a geometric feature correlation coefficient corresponding to the first point cloud information according to the first point cloud information and the standard geometric feature;
and calculating a characteristic correlation value according to the projection profile correlation coefficient and the geometric characteristic correlation coefficient.
Optionally, calculating a projection contour correlation coefficient corresponding to the first point cloud information according to the first point cloud information and the standard projection contour feature, including:
projecting the first point cloud information to a preset plane polar coordinate system to obtain first point cloud information under the preset plane polar coordinate system;
dividing each point cloud information contained in the first point cloud information under a preset plane polar coordinate system into preset sector areas respectively according to the horizontal angle resolution of the laser radar to obtain point cloud information contained in each sector area respectively;
calculating the radial minimum value corresponding to each sector area according to the point cloud information contained in each sector area to obtain the radial minimum value corresponding to each sector area;
calculating the correlation percentage corresponding to each sector area according to the radial minimum value and the standard projection profile characteristic corresponding to each sector area;
and taking the average value of the correlation percentages respectively corresponding to the fan-shaped areas as the correlation coefficient of the projection profile.
Optionally, the standard geometric features include the following features: the center of mass, the length, the width and the height of the standard point cloud cluster are obtained, and the obtaining scene of the standard point cloud cluster is the same as that of the first point cloud information;
according to the first point cloud information and the standard geometric features, calculating a geometric feature correlation coefficient corresponding to the first point cloud information, wherein the calculation comprises the following steps:
calculating the mass center of the point cloud cluster contained in the first point cloud information and the maximum value and the minimum value of the point cloud cluster contained in the first point cloud information in each coordinate axis direction according to the first point cloud information;
calculating the length, width and height of the point cloud cluster contained in the first point cloud information according to the maximum value and the minimum value of the point cloud cluster contained in the first point cloud information in each coordinate axis direction;
calculating correlation percentages respectively corresponding to the centroid, the length, the width and the height of the point cloud cluster contained in the first point cloud information according to the centroid, the length, the width and the height of the point cloud cluster contained in the first point cloud information and standard geometric characteristics;
and taking the average value of the correlation percentages respectively corresponding to the centroid, the length, the width and the height of the point cloud cluster contained in the first point cloud information as a geometric characteristic correlation coefficient.
Optionally, calculating a feature correlation value according to the projection profile correlation coefficient and the geometric feature correlation coefficient, including:
comparing the projection profile correlation coefficient with the geometric characteristic correlation coefficient to obtain weights corresponding to the projection profile correlation coefficient and the geometric characteristic correlation coefficient respectively;
and calculating a characteristic correlation value according to the projection profile correlation coefficient and the geometric characteristic correlation coefficient and the weights respectively corresponding to the projection profile correlation coefficient and the geometric characteristic correlation coefficient.
Optionally, determining a fault state of the laser radar to be diagnosed according to the second point cloud information includes:
under the condition that the total number of the point clouds corresponding to the second point cloud information is smaller than a set point cloud total number threshold value, calculating included angles between each point cloud contained in the second point cloud information and an X axis respectively by adopting a first formula, wherein the first formula is as follows:
in the formula, xi、yi、ziInformation of point cloud i, gamma, contained in the second point cloud informationiAn included angle between the point cloud i and an X axis is defined, wherein i is 1,2, …, n;
according to the vertical angle resolution of the laser radar and the included angles between each point cloud contained in the second point cloud information and the X axis respectively, each point cloud contained in the second point cloud information corresponds to each laser device contained in the laser radar to be diagnosed respectively by adopting a second formula so as to obtain the point cloud corresponding to each laser device respectively, wherein the second formula is as follows: l isj＝Lj∪pi，
In the formula, LjIs a laser j, PiPoint cloud i contained in the second point cloud information;
and for each laser in each laser, if the point cloud number corresponding to the point cloud information corresponding to the laser is smaller than a set point cloud number threshold, determining the fault of the laser, wherein the set point cloud number threshold is determined according to the horizontal angle resolution of the laser radar.
Optionally, the method further includes:
for each of the lasers:
under the condition that the fault of the laser is determined and the number of the point clouds corresponding to the laser is not 0, the point clouds corresponding to the laser are sorted by taking the included angle between the point clouds corresponding to the laser and an X axis as a sorting basis to obtain sorted point clouds corresponding to the laser;
for any adjacent two point clouds in the sorted point clouds corresponding to the laser, calculating the distance between the two point clouds according to the information of the two point clouds contained in the second point cloud information, calculating the distance threshold value corresponding to the two point clouds by adopting a third formula according to the horizontal angle resolution of the laser radar, and calculating the fault visual angle range corresponding to the two point clouds by adopting a fourth formula as the fault visual angle range corresponding to the laser if the distance between the two point clouds is greater than the distance threshold value corresponding to the two point clouds, wherein the third formula is as follows:
in the formula, xi、yi、ziInformation of the previous point cloud i in the two point clouds, omega is the horizontal and angular resolution of the laser radar, dthThe distance threshold value corresponding to the two point clouds; the fourth formula is:
in the formula, xi+1、yi+1、zi+1And f is the information of the next point cloud i +1 in the two point clouds, and the fault view angle range.
Optionally, the second point cloud information includes the point cloud intensity of the target object, and when the to-be-diagnosed lidar is mounted and fixed in a position to detect the target object, the fault state of the to-be-diagnosed lidar is determined according to the second point cloud information, further including:
and calculating the average value of the point cloud intensity of the target object, and determining that the laser radar to be diagnosed has the point cloud intensity abnormal fault under the condition that the difference value between the average value of the point cloud intensity of the target object and the pre-measured standard intensity value is greater than a set intensity threshold value.
A laser radar fault diagnosis device is applied to a laser radar controller and comprises:
the power supply and communication channel fault diagnosis module is used for determining whether a communication channel corresponding to the laser radar to be diagnosed has a fault according to a preset communication protocol, a preset protection measure and a data integrity protection algorithm under the condition that the voltage of each subsystem included in the laser radar to be diagnosed is within a set voltage threshold range, wherein the communication channel is used for transmitting point cloud information of a target object;
and the laser radar fault diagnosis module is used for performing functional diagnosis according to the received point cloud information of the target object under the condition that the communication channel has no fault and meets the characteristic matching triggering condition so as to determine the fault state of the laser radar to be diagnosed.
According to the technical scheme, under the condition that the voltage of each subsystem included in the laser radar to be diagnosed is within the range of the set voltage threshold, whether the communication channel corresponding to the laser radar to be diagnosed is in fault is determined according to the preset communication protocol, the preset protection measures and the data integrity protection algorithm, and under the condition that the communication channel is not in fault and the characteristic matching triggering condition is met, functional diagnosis is performed according to the received point cloud information of the target object to determine the fault state of the laser radar to be diagnosed. Therefore, the method and the device can realize fault diagnosis of the laser radar to be diagnosed, reduce the difficulty and workload of manual inspection, and improve the fault diagnosis efficiency of the laser radar to be diagnosed.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flowchart of a laser radar fault diagnosis method according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a lidar and a lidar controller according to an embodiment of the present disclosure;
fig. 3 is a schematic view of a laser radar scanning provided in an embodiment of the present application;
FIG. 4 is a schematic view illustrating a failure view angle range provided by an embodiment of the present application;
fig. 5 is a schematic structural diagram of a laser radar fault diagnosis device provided in an embodiment of the present application;
fig. 6 is a block diagram of a hardware structure of a lidar fault diagnosis device according to an embodiment of the present disclosure.
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.
The application provides a laser radar fault diagnosis method which can be applied to a laser radar controller. The laser radar fault diagnosis method provided by the present application is described in detail by the following embodiments.
Referring to fig. 1, a schematic flow chart of a laser radar fault diagnosis method provided in an embodiment of the present application is shown, where the laser radar fault diagnosis method may include:
step S101, under the condition that the voltage of each subsystem included in the laser radar to be diagnosed is within a set voltage threshold range, determining whether a communication channel corresponding to the laser radar to be diagnosed has a fault or not according to a preset communication protocol, a preset protection measure and a data integrity protection algorithm.
In this embodiment, a power supply system of the laser radar to be diagnosed is first subjected to fault diagnosis by the laser radar controller, specifically, the laser radar controller may collect voltages of subsystems included in the laser radar to be diagnosed, and then perform related fault diagnosis according to a collected voltage range.
Optionally, for any subsystem included in the laser radar to be diagnosed, when the voltage of the subsystem is lower than the minimum value in the set voltage threshold range, outputting a low-voltage fault, and feeding back the identification signal as 0; when the subsystem voltage is higher than the maximum value in the set voltage threshold range, outputting a high voltage fault, and feeding back an identification signal of 2; when the voltage of the subsystem is in the set voltage threshold range, the subsystem is characterized as a normal voltage, and the feedback identification signal is 1.
It should be noted that, for different subsystems included in the lidar to be diagnosed, the set voltage threshold ranges may be the same or different, and the specific requirements are determined according to actual situations, which is not limited in this application.
In this embodiment, the communication channel corresponding to the laser radar to be diagnosed is a channel between the laser radar and the laser radar controller, and the communication channel may be used to transmit point cloud information of a target object. Referring to fig. 2, a schematic structural diagram of an optional lidar and a lidar controller to be diagnosed provided in an embodiment of the present application is shown, where as shown in fig. 2, the lidar controller includes a power module, a processing chip, a communication module, a volatile memory, a non-volatile memory, and the like, and the lidar controller and the lidar may communicate with each other through a communication cable (i.e., a communication channel in this step).
It can be understood that if the communication channel is faulty, the point cloud information of the target object acquired by the laser radar cannot be transmitted to the laser radar controller, so that the laser radar controller cannot perform fault diagnosis on the laser radar to be diagnosed according to the point cloud information of the target object, and based on this, it is necessary to first determine whether the communication channel corresponding to the laser radar to be diagnosed is faulty through this step.
Since the transmission of the existing lidar point cloud is mainly based on ethernet, the step is exemplified by fault diagnosis of ethernet communication: if the voltage of each subsystem included in the laser radar to be diagnosed is in the corresponding set voltage threshold range, the relevant diagnosis can be performed according to the preset communication protocol and the preset protection measures, for example, whether the communication channel is interrupted is judged according to the heartbeat (Counter); and diagnosing the data content according to the data integrity protection algorithm, wherein the diagnosis result is divided into normal communication and abnormal communication, if the verification result meets the data integrity protection algorithm, the communication is judged to be normal, and if the verification result does not meet the data integrity protection algorithm, the communication is judged to be abnormal. Here, the data integrity protection algorithm may be, for example, a Cyclic Redundancy Check (CRC) algorithm.
In this step, if it is determined that the communication channel is not interrupted according to the preset communication protocol and the preset protection measure and the communication is determined to be normal according to the data integrity protection algorithm, it is determined that the communication channel corresponding to the laser radar to be diagnosed has no fault; otherwise, determining the communication channel fault corresponding to the laser radar to be diagnosed.
And S102, under the condition that the communication channel is not in fault and the characteristic matching triggering condition is met, performing functional diagnosis according to the received point cloud information of the target object to determine the fault state of the laser radar to be diagnosed.
Before the laser radar controller performs functional detection (the functional detection refers to detection for a target object, and at this time, point cloud information of the target object acquired by the laser radar is transmitted to the laser radar controller through a communication channel), diagnosis (non-functional diagnosis) needs to be performed on the running state of the laser radar to be diagnosed, that is, whether a feature matching triggering condition is met is judged through the step. If the characteristic matching triggering condition is met, the operating state of the laser radar to be diagnosed is normal, then functional detection can be carried out on the basis of the laser radar to be diagnosed, otherwise, if the characteristic matching triggering condition is not met, the operating state of the laser radar to be diagnosed is abnormal, and then subsequent functional detection cannot be carried out on the basis of the laser radar to be diagnosed.
For example, if the functional detection is that the lidar to be diagnosed is mounted and fixed at a certain position to detect the target object, in this step, after the lidar is placed at the fixed position, the point cloud at the fixed position can be detected before the target object enters the detection area (i.e., the area where the fixed position is located), and whether the feature matching triggering condition is met or not is determined according to the point cloud feature at the fixed position, and if the correlation between the point cloud feature at the fixed position and the standard point cloud feature corresponding to the fixed position is greater than a preset threshold, it is determined that the feature matching triggering condition is met.
As described above, if the feature matching triggering condition is satisfied, functional diagnosis may be performed, and at this time, the laser radar controller may receive point cloud information of the target object, and then may perform functional diagnosis according to the received point cloud information of the target object, so as to determine a fault state of the laser radar to be diagnosed.
According to the laser radar fault diagnosis method, under the condition that the voltage of each subsystem included in the laser radar to be diagnosed is within the range of the set voltage threshold, whether a communication channel corresponding to the laser radar to be diagnosed is in fault is determined according to a preset communication protocol, a preset protection measure and a data integrity protection algorithm, and under the condition that the communication channel is not in fault and the characteristic matching triggering condition is met, functional diagnosis is carried out according to received point cloud information of a target object, so that the fault state of the laser radar to be diagnosed is determined. Therefore, the fault diagnosis of the laser radar to be diagnosed can be achieved, the difficulty and the workload of manual inspection are reduced, and the fault diagnosis efficiency of the laser radar to be diagnosed is improved.
In an embodiment of the present application, a process of "performing functional diagnosis according to the received point cloud information of the target object to determine a fault state of the laser radar to be diagnosed" in step S102 is described.
Optionally, the point cloud information of the target object includes first point cloud information and second point cloud information, where the first point cloud information is point cloud information obtained in any scene, the second point cloud information is point cloud information obtained in a scene where an X axis of the laser radar to be diagnosed is perpendicular to the target surface, and the test distance requirement is that the point cloud information is obtained in a scene where laser beams emitted by all lasers included in the laser radar to be diagnosed can scan the target surface, and optionally, the target surface is a plane or a curved surface with a known shape. For example, referring to the laser radar scanning schematic diagram shown in fig. 3, the scene corresponding to the second point cloud information is: firstly, detecting a front visual angle of a laser radar to be diagnosed (the laser radar to be diagnosed comprises at least one laser), enabling an X axis of the laser radar to be diagnosed to be vertical to a wall surface (the wall surface is a plane), and enabling laser beams emitted by all lasers contained in the laser radar to be diagnosed to be capable of being scanned to the wall surface according to a testing distance requirement; the scene corresponding to the first point cloud information is not limited in the present application, that is, as long as the scene capable of obtaining the point cloud information can be all used as the scene corresponding to the first point cloud information, for example, the scene corresponding to the first point cloud information may be the same as the scene corresponding to the second point cloud information, and certainly, may also be different.
Based on this, the process of performing functional diagnosis according to the received point cloud information of the target object to determine the fault state of the laser radar to be diagnosed may include:
and S1, calculating a characteristic correlation value according to the first point cloud information and the pre-stored standard point cloud characteristics.
The standard point cloud feature is prior information pre-stored by the laser radar controller, and specifically, the standard point cloud feature is determined according to point cloud information of a target object obtained by a normal laser radar.
Optionally, the standard point cloud feature may include: standard projection profile features and standard geometric features. Optionally, the standard geometric features include the following features: the center of mass, the length, the width and the height of the standard point cloud cluster are obtained, and the obtaining scene of the standard point cloud cluster is the same as the obtaining scene of the first point cloud information.
Based on this, the process of "calculating the feature correlation value according to the first point cloud information and the standard point cloud feature" in this step may include:
and S11, calculating a projection contour correlation coefficient corresponding to the first point cloud information according to the first point cloud information and the standard projection contour characteristics.
For the received first point cloud information, a feature detection algorithm can be adopted to calculate projection profile features of a target object corresponding to the first point cloud information, and then the projection profile features are matched with standard projection profile features stored in advance to obtain projection profile correlation coefficients corresponding to the first point cloud information, and the specific implementation mode can include the following steps:
and S111, projecting the first point cloud information to a preset plane polar coordinate system to obtain the first point cloud information under the preset plane polar coordinate system.
Here, the preset planar polar coordinate system may be an XOY planar polar coordinate system, and in this step, the first point cloud information may be projected into the XOY planar polar coordinate system to obtain the first point cloud information in the XOY planar polar coordinate system.
And S112, dividing the cloud information of each point contained in the first point cloud information under the preset plane polar coordinate system into preset sector areas respectively according to the horizontal angle resolution of the laser radar to obtain point cloud information contained in each sector area respectively.
Each of the preset sector areas is obtained by dividing according to the horizontal angular resolution of the lidar, for example, the horizontal angular resolution of the lidar is 0.2 °, and then the 360 ° environment can be divided into 1800 sector areas. Here, the lidar horizontal angular resolution refers to the horizontal angular resolution of the lidar to be diagnosed.
And S113, calculating the radial minimum value corresponding to each sector area according to the point cloud information contained in each sector area to obtain the radial minimum value corresponding to each sector area.
The radial minimum value corresponding to a sector area is the minimum value in the distance values between the point cloud information contained in the sector area and the origin of the preset planar polar coordinate system.
In this step, the distance values between each point cloud included in each sector area and the origin of the preset planar polar coordinate system can be calculated according to the point cloud information included in each sector area, and then the radial minimum values corresponding to each sector area can be calculated.
For example, for a sector area, the distance values between each point cloud included in the sector area and the origin of the preset planar polar coordinate system calculated in this step are 2, 3, 4, 5, and 6, respectively, and then the radial minimum value corresponding to the sector area is 2.
And S114, calculating the correlation percentage corresponding to each sector area according to the radial minimum value and the standard projection profile characteristic corresponding to each sector area.
Here, the radial minimum value corresponding to each sector area is the projection profile feature of the target object, that is, the correlation percentage corresponding to each sector area can be calculated according to the projection profile feature and the standard projection profile feature of the target object in this step.
Specifically, the projection profile features of the target object may be compared with the standard projection profile features to obtain the correlation percentage corresponding to each sector area.
And S115, taking the average value of the correlation percentages corresponding to the fan-shaped areas as a projection contour correlation coefficient.
And S12, calculating a geometric feature correlation coefficient corresponding to the first point cloud information according to the first point cloud information and the standard geometric feature.
For the received first point cloud information, a feature detection algorithm may be adopted to calculate geometric features of a point cloud cluster composed of point clouds included in the first point cloud information (for convenience of description, the point cloud cluster is subsequently defined as the point cloud cluster included in the first point cloud information), and then the geometric features are matched with pre-stored standard geometric features to obtain a geometric feature correlation coefficient corresponding to the first point cloud information, and the specific implementation manner may include the following steps:
s121, calculating the mass center of the point cloud cluster contained in the first point cloud information and the maximum value and the minimum value of the point cloud cluster contained in the first point cloud information in each coordinate axis direction according to the first point cloud information.
Optionally, the first point cloud information refers to coordinate values of point cloud clusters, and the coordinate value of each point cloud in the point cloud clusters may be represented by (x)i,yi,zi) Wherein i is 1,2, …, n; then, optionally, the centroid P of the point cloud cluster contained in the first point cloud informationc(xc，yc，zc) The calculation formula of (2) is as follows:
optionally, the maximum value and the minimum value of the point cloud cluster included in the first point cloud information in each coordinate axis direction are respectively:
and S122, calculating the length, width and height of the point cloud cluster contained in the first point cloud information according to the maximum value and the minimum value of the point cloud cluster contained in the first point cloud information in each coordinate axis direction.
Optionally, the length L, the width W, and the height H of the point cloud cluster included in the first point cloud information are respectively:
and S123, calculating correlation percentages respectively corresponding to the centroid, the length, the width and the height of the point cloud cluster contained in the first point cloud information according to the centroid, the length, the width and the height of the point cloud cluster contained in the first point cloud information and the standard geometric characteristics.
Here, the centroid, length, width, and height of the point cloud cluster included in the first point cloud information are the four geometric features of the target object, that is, the correlation percentage corresponding to each sector area can be calculated according to the geometric features and the standard geometric features of the target object in this step.
Specifically, for the four geometric features of the centroid, the length, the width and the height of the point cloud cluster included in the first point cloud information, the four geometric features may be respectively compared with the standard geometric features corresponding to the four geometric features to obtain the correlation percentages respectively corresponding to the four geometric features, that is, the correlation percentages respectively corresponding to the centroid, the length, the width and the height of the point cloud cluster included in the first point cloud information are obtained.
And S125, taking the average values of the correlation percentages respectively corresponding to the centroid, the length, the width and the height of the point cloud cluster contained in the first point cloud information as the geometric characteristic correlation coefficients.
And S13, calculating a characteristic correlation value according to the projection profile correlation coefficient and the geometric characteristic correlation coefficient.
Optionally, the process of this step may include:
s131, comparing the projection profile correlation coefficient with the geometric feature correlation coefficient to obtain weights corresponding to the projection profile correlation coefficient and the geometric feature correlation coefficient respectively.
Optionally, different weight percentages may be added to the projection profile correlation coefficient and the geometric feature correlation coefficient according to differences in the laser radar detection object and the actual application scene, for example, when the reliability of the projection profile correlation coefficient is smaller than the geometric feature correlation coefficient, the projection profile correlation coefficient weight ratio is set to 0.4, and the geometric feature correlation coefficient weight ratio is set to 0.6; otherwise, setting the weight ratio of the correlation coefficient of the projection profile to be 0.6 and the weight ratio of the correlation coefficient of the geometric features to be 0.4.
S132, calculating a characteristic correlation value according to the projection profile correlation coefficient and the geometric characteristic correlation coefficient, and weights corresponding to the projection profile correlation coefficient and the geometric characteristic correlation coefficient respectively.
For example, a weighted summation method may be used to calculate the feature correlation value.
And S2, determining the fault state of the laser radar to be diagnosed according to the second point cloud information under the condition that the characteristic correlation value is smaller than the set correlation threshold value.
In this step, a correlation threshold may be set in advance, and then the characteristic correlation value calculated in S1 is compared with the set correlation threshold; if the characteristic correlation value is larger than or equal to the set correlation threshold value, it is indicated that the laser radar to be diagnosed is likely to have no fault, otherwise, if the characteristic correlation value is smaller than the set correlation threshold value, it is indicated that the laser radar to be diagnosed is likely to have a fault, and at this time, the fault state of the laser radar to be diagnosed can be determined according to the second point cloud information.
Here, the "determining the fault state of the laser radar to be diagnosed according to the second point cloud information" refers to performing quality diagnosis on the second point cloud information acquired by the laser radar to be diagnosed, so as to determine whether the laser radar to be diagnosed has a fault.
In an optional embodiment, the step of "determining the fault state of the lidar to be diagnosed according to the second point cloud information" specifically includes: and determining whether each laser included in the laser radar to be diagnosed fails according to the second point cloud information, wherein the laser included in the laser radar to be diagnosed can comprise a laser transmitter and a laser receiver.
Specifically, the process of this step may include:
and S21, under the condition that the total number of the point clouds corresponding to the second point cloud information is smaller than a set threshold value of the total number of the point clouds, calculating included angles between each point cloud contained in the second point cloud information and the X axis by adopting a first formula.
Here, the total number of point clouds corresponding to the second point cloud information refers to the total number of point clouds in a point cloud cluster included in the second point cloud information.
The step can be implemented by a point cloud total corresponding to the second point cloud information received in the region of interest (ROI)Recording the number of the points and setting a threshold value P of the total number of the point cloudsnumComparing, if less than the threshold value P of the total amount of the set point cloudsnumAnd preliminarily diagnosing as a pre-abnormal laser radar, wherein the region of interest refers to the region where the target object is located.
When the preliminary diagnosis is the pre-abnormal laser radar, the step can adopt the following first formula to calculate the included angles between each point cloud contained in the second point cloud information and the X axis respectively.
Taking the example that the second point cloud information includes n point clouds, the first formula may be:
in the formula, xi、yi、ziInformation of point cloud i, gamma, contained in the second point cloud informationiThe included angle between the point cloud i and the X axis is 1,2, …, n.
And S22, according to the vertical angle resolution of the laser radar and the included angle between each point cloud contained in the second point cloud information and the X axis, respectively corresponding each point cloud contained in the second point cloud information to each laser contained in the laser radar to be diagnosed by adopting a second formula so as to obtain the point cloud corresponding to each laser.
In this step, if the total number of point clouds corresponding to the second point cloud information is smaller than the threshold value of the total number of point clouds, the vertical angular resolution θ of the laser radar and the included angle γ between each point cloud included in the second point cloud information and the X axis respectively can be determined according to the vertical angular resolution θ of the laser radar and the included angle γ between each point cloud included in the second point cloud information and the X axisi(the included angle can be shown in fig. 3), each point cloud included in the second point cloud information is respectively corresponding to each laser included in the laser radar to be diagnosed.
Alternatively, the second formula may be: l isj＝Li∪pi，
In the formula, LjIs a laser j, PiThe point cloud i in the second point cloud information.
And S23, for each laser in each laser, if the point cloud number corresponding to the laser is smaller than the set point cloud number threshold, determining that the laser has a fault.
And the set point cloud quantity threshold is determined according to the horizontal angular resolution of the laser radar.
The number of the point clouds corresponding to the lasers is the total number of the point clouds corresponding to the lasers, and in this step, whether each laser fails or not may be determined according to the total number of the point clouds corresponding to each laser, specifically, if the total number of the point clouds corresponding to one laser is 0 (for example, the laser L2 shown in fig. 3), or, although not 0, is smaller than a set point cloud number threshold L obtained according to the horizontal angle resolutionnum(e.g., laser L4 shown in fig. 3), the laser is determined to be faulty. Respectively comparing the total number of the point clouds corresponding to each laser with a set point cloud number threshold value LnumAnd comparing to determine which lasers in each laser fail and which lasers do not fail.
In an optional embodiment, the fault view angle range corresponding to the faulty laser may be further determined in this embodiment, so as to determine the fault type according to the fault view angle range.
Based on this, the present embodiment may further include: for each laser in each laser, if the fault of the laser is determined and the number of the point clouds corresponding to the laser is not 0, sorting the point clouds corresponding to the lasers by taking the included angle between the point clouds corresponding to the lasers and an X axis as a sorting basis to obtain sorted point clouds corresponding to the lasers; and calculating the distance between any two adjacent point clouds in the sorted point clouds corresponding to the laser according to the information of the two point clouds contained in the second point cloud information, calculating the distance threshold value corresponding to the two point clouds by adopting a third formula according to the horizontal angle resolution of the laser radar, and calculating the fault visual angle range corresponding to the two point clouds by adopting a fourth formula as the fault visual angle range corresponding to the laser if the distance between the two point clouds is greater than the distance threshold value corresponding to the two point clouds, so as to obtain the fault visual angle range corresponding to each laser.
Optionally, the included angle between the point cloud corresponding to the laser and the X-axis (i.e. the above-mentioned γ)i) Sequencing the point clouds corresponding to the laser from small to big to obtainThe sorted point cloud corresponding to the laser is shown in fig. 3 or fig. 4.
Optionally, the calculation formula of the distance between two adjacent points of cloud information may be:
in the formula, xi、yi、ziIs the information, x, of the previous point cloud i in two adjacent point clouds (i.e. point cloud i and point cloud i +1)i+1、yi+1、zi+1For the information of the next point cloud i +1 of two adjacent point clouds, diThe distance between the two adjacent point clouds.
Optionally, the third formula (i.e., a calculation formula of a distance threshold corresponding to two adjacent points of cloud information) may be:
in the formula (d)thA distance threshold value, x, corresponding to the two adjacent point cloudsi、yi、ziThe information of the previous point cloud i in the two adjacent point clouds is shown, and omega is the horizontal angle resolution of the laser radar.
Optionally, the fourth formula (i.e. the calculation formula of the failure view angle range) may be:
wherein f is the fault view angle range, xi、yi、ziFor the information of the previous point cloud i in two adjacent point clouds, xi+1、yi+1、zi+1The information of the next point cloud i +1 in the two adjacent point clouds.
It should be noted that, for each laser, there may be a plurality of corresponding failure regions, and correspondingly, there may be a plurality of failure view angle ranges, for example, see the schematic view angle range diagram of the laser radar shown in fig. 4, in this fig. 4, the laser corresponds to 2 failure regions, i.e. ab and cd, and taking the failure region cd as an example, the failure view angle range f calculated by using the above fourth formulaβComprises the following steps:
it is understood that for a laser, if the failure view angle range is a part of the whole laser scanning range, it may be that the laser housing part is blocked or damaged; when the fault view angle range is the whole laser scanning range, the hardware fault of the laser (namely the laser transmitter or the laser receiver) is diagnosed.
In summary, in the embodiment, the fault diagnosis of the laser radar to be diagnosed can be performed by combining the prior information (i.e., the standard point cloud feature, i.e., the standard projection profile feature and the standard geometric feature) stored by the laser radar controller and the point cloud information received in real time, so that the fault diagnosis efficiency of the laser radar to be diagnosed is further improved.
In an optional embodiment, the point cloud information of the target object may further include point cloud intensity of the target object, and based on this, the embodiment provides an auxiliary diagnosis method for performing quality diagnosis on the second point cloud information in a specific scene.
Here, the specific scene means: the laser radar to be diagnosed is installed and fixed at a position to detect a target object (such as a part).
Optionally, in this scenario, the process of "determining the fault state of the lidar to be diagnosed according to the second point cloud information" in S2 may further include: and calculating the average value of the point cloud intensity of the target object, and determining that the laser radar to be diagnosed has the point cloud intensity abnormal fault under the condition that the difference value between the average value of the point cloud intensity of the target object and the pre-measured standard intensity value is greater than a set intensity threshold value.
Specifically, In this embodiment, on the basis of the above "determining whether each laser included In the laser radar to be diagnosed is faulty", an average value of the point cloud intensities of the target object is calculated and compared with a pre-measured standard intensity value In, and if a difference between the average value of the point cloud intensities of the target object and the standard intensity value is greater than a set intensity threshold, that is, a deviation between the average value of the point cloud intensities of the target object and the standard intensity value is greater, it is determined that the laser radar to be diagnosed has a point cloud intensity abnormal fault.
The embodiment of the application also provides a laser radar fault diagnosis device which can be applied to a laser radar controller, and the laser radar fault diagnosis device provided by the embodiment of the application is described below.
Referring to fig. 5, a schematic structural diagram of a lidar fault diagnosis apparatus according to an embodiment of the present disclosure is shown, and as shown in fig. 5, the lidar fault diagnosis apparatus may include: a power and communication channel fault diagnosis module 501 and a lidar fault diagnosis module 502.
The power supply and communication channel fault diagnosis module 501 is configured to determine whether a communication channel corresponding to the laser radar to be diagnosed has a fault according to a preset communication protocol, a preset protection measure and a data integrity protection algorithm when voltages of subsystems included in the laser radar to be diagnosed are within a set voltage threshold range, where the communication channel is used to transmit point cloud information of a target object.
And the laser radar fault diagnosis module 502 is configured to perform functional diagnosis according to the received point cloud information of the target object to determine a fault state of the laser radar to be diagnosed when the communication channel is not faulty and the feature matching triggering condition is met.
In summary, the working principle of the laser radar fault diagnosis apparatus provided in this embodiment is the same as that of the laser radar fault diagnosis method provided in the above embodiment, and is not described herein again.
The embodiment of the application also provides laser radar fault diagnosis equipment. Alternatively, fig. 6 shows a block diagram of a hardware structure of the lidar fault diagnosis device, and referring to fig. 6, the hardware structure of the lidar fault diagnosis device may include: at least one processor 601, at least one communication interface 602, at least one memory 603, and at least one communication bus 604;
in the embodiment of the present application, the number of the processor 601, the communication interface 602, the memory 603, and the communication bus 604 is at least one, and the processor 601, the communication interface 602, and the memory 603 complete communication with each other through the communication bus 604;
the processor 601 may be a central processing unit CPU, or an application Specific Integrated circuit asic, or one or more Integrated circuits configured to implement embodiments of the present invention, or the like;
the memory 603 may include a high-speed RAM memory, and may further include a non-volatile memory (non-volatile memory), etc., such as at least one disk memory;
wherein the memory 603 stores a program, and the processor 601 may call the program stored in the memory 603 for:
under the condition that the voltage of each subsystem included in the laser radar to be diagnosed is within a set voltage threshold range, determining whether a communication channel corresponding to the laser radar to be diagnosed is in fault according to a preset communication protocol, a preset protection measure and a data integrity protection algorithm, wherein the communication channel is used for transmitting point cloud information of a target object;
and under the condition that the communication channel is not in fault and meets the characteristic matching triggering condition, performing functional diagnosis according to the received point cloud information of the target object to determine the fault state of the laser radar to be diagnosed.
Alternatively, the detailed function and the extended function of the program may be as described above.
The embodiment of the application also provides a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the laser radar fault diagnosis method is realized.
Alternatively, the detailed function and the extended function of the program may be as described above.
Finally, it is further noted that, herein, relational terms such as, for example, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A laser radar fault diagnosis method is applied to a laser radar controller and comprises the following steps:
under the condition that the voltage of each subsystem included in the laser radar to be diagnosed is within a set voltage threshold range, determining whether a communication channel corresponding to the laser radar to be diagnosed fails according to a preset communication protocol, a preset protection measure and a data integrity protection algorithm, wherein the communication channel is used for transmitting point cloud information of a target object;
and under the condition that the communication channel is not in fault and meets the characteristic matching triggering condition, performing functional diagnosis according to the received point cloud information of the target object to determine the fault state of the laser radar to be diagnosed.
2. The lidar fault diagnosis method according to claim 1, wherein the point cloud information of the target object includes first point cloud information and second point cloud information, wherein the first point cloud information is obtained in any scene, the second point cloud information is obtained when an X axis of the lidar to be diagnosed is perpendicular to a target surface, a test distance is required to be obtained when laser beams emitted by all lasers included in the lidar to be diagnosed can scan the scene of the target surface, and the target surface is a plane or a curved surface with a known shape;
the functional diagnosis is carried out according to the received point cloud information of the target object so as to determine the fault state of the laser radar to be diagnosed, and the method comprises the following steps:
calculating a characteristic correlation value according to the first point cloud information and a pre-stored standard point cloud characteristic, wherein the standard point cloud characteristic is a point cloud characteristic determined according to point cloud information of the target object obtained by a normal laser radar;
and under the condition that the characteristic correlation value is smaller than the set correlation threshold value, determining the fault state of the laser radar to be diagnosed according to the second point cloud information.
3. The lidar fault diagnosis method according to claim 2, wherein the standard point cloud feature comprises: standard projection profile features and standard geometric features;
calculating a feature correlation value according to the first point cloud information and pre-stored standard point cloud features, wherein the calculating comprises the following steps:
calculating a projection contour correlation coefficient corresponding to the first point cloud information according to the first point cloud information and the standard projection contour feature;
calculating a geometric feature correlation coefficient corresponding to the first point cloud information according to the first point cloud information and the standard geometric feature;
and calculating the characteristic correlation value according to the projection profile correlation coefficient and the geometric characteristic correlation coefficient.
4. The lidar fault diagnosis method according to claim 3, wherein the calculating a projection profile correlation coefficient corresponding to the first point cloud information according to the first point cloud information and the standard projection profile feature comprises:
projecting the first point cloud information to a preset plane polar coordinate system to obtain first point cloud information under the preset plane polar coordinate system;
according to the horizontal angle resolution of the laser radar, dividing point cloud information contained in first point cloud information under the preset planar polar coordinate system into preset sector areas respectively to obtain point cloud information contained in each sector area respectively;
calculating the radial minimum value corresponding to each sector area according to the point cloud information contained in each sector area to obtain the radial minimum value corresponding to each sector area;
calculating the correlation percentage corresponding to each sector area according to the radial minimum value corresponding to each sector area and the standard projection profile characteristic;
and taking the average value of the correlation percentages respectively corresponding to the fan-shaped areas as the correlation coefficient of the projection profile.
5. The lidar fault diagnosis method according to claim 3, wherein the standard geometric features include the following features: the center of mass, the length, the width and the height of a standard point cloud cluster are obtained, and the obtaining scene of the standard point cloud cluster is the same as that of the first point cloud information;
calculating a geometric feature correlation coefficient corresponding to the first point cloud information according to the first point cloud information and the standard geometric feature, including:
calculating the mass center of the point cloud cluster contained in the first point cloud information according to the first point cloud information, and the maximum value and the minimum value of the point cloud cluster contained in the first point cloud information in each coordinate axis direction;
calculating the length, width and height of the point cloud cluster contained in the first point cloud information according to the maximum value and the minimum value of the point cloud cluster contained in the first point cloud information in each coordinate axis direction;
calculating correlation percentages respectively corresponding to the centroid, the length, the width and the height of the point cloud cluster contained in the first point cloud information according to the centroid, the length, the width and the height of the point cloud cluster contained in the first point cloud information and the standard geometric characteristics;
and taking the average value of the correlation percentages respectively corresponding to the centroid, the length, the width and the height of the point cloud cluster contained in the first point cloud information as the geometric characteristic correlation coefficient.
6. The lidar fault diagnosis method according to claim 3, wherein the calculating the feature correlation value based on the projection profile correlation coefficient and the geometric feature correlation coefficient comprises:
comparing the projection profile correlation coefficient with the geometric feature correlation coefficient to obtain weights corresponding to the projection profile correlation coefficient and the geometric feature correlation coefficient respectively;
and calculating the characteristic correlation value according to the projection profile correlation coefficient and the geometric characteristic correlation coefficient and the weights corresponding to the projection profile correlation coefficient and the geometric characteristic correlation coefficient respectively.
7. The lidar fault diagnosis method according to claim 2, wherein the determining the fault state of the lidar to be diagnosed according to the second point cloud information comprises:
under the condition that the total number of point clouds corresponding to the second point cloud information is smaller than a set point cloud total number threshold value, calculating included angles between each point cloud contained in the second point cloud information and an X axis respectively by adopting a first formula, wherein the first formula is as follows:
in the formula, xi、yi、ziIncluded for the second point cloud informationInformation of the point cloud i, gammaiAn included angle between the point cloud i and an X axis is defined, wherein i is 1,2, …, n;
according to the vertical angle resolution of the laser radar and the included angle between each point cloud contained in the second point cloud information and the X axis, respectively corresponding each point cloud contained in the second point cloud information to each laser device contained in the laser radar to be diagnosed by adopting a second formula so as to obtain the point cloud corresponding to each laser device, wherein the second formula is as follows: l isj＝Lj∪pi，
In the formula, LjIs a laser j, PiThe point cloud i contained in the second point cloud information is obtained;
and for each laser in each laser, if the point cloud number corresponding to the laser is smaller than a set point cloud number threshold, determining the fault of the laser, wherein the set point cloud number threshold is determined according to the horizontal angular resolution of the laser radar.
8. The lidar fault diagnosis method according to claim 7, further comprising:
for each of the lasers:
under the condition that the fault of the laser is determined and the number of the point clouds corresponding to the laser is not 0, the point clouds corresponding to the laser are sorted by taking the included angle between the point clouds corresponding to the laser and an X axis as a sorting basis to obtain sorted point clouds corresponding to the laser;
calculating the distance between any two adjacent point clouds in the sorted point clouds corresponding to the laser according to the information of the two point clouds contained in the second point cloud information, calculating the distance threshold value corresponding to the two point clouds by adopting a third formula according to the horizontal angle resolution of the laser radar, and calculating the fault visual angle range corresponding to the two point clouds as the fault visual angle range corresponding to the laser by adopting a fourth formula if the distance between the two point clouds is greater than the distance threshold value corresponding to the two point clouds, wherein the two point clouds are arranged in a sequence, and the fault visual angle range corresponding to the laser is obtained by adopting the fourth formulaThe third formula is:
in the formula, xi、yi、ziInformation of the previous point cloud i in the two point clouds, omega is the horizontal and angular resolution of the laser radar, dthThe distance threshold value corresponding to the two point clouds; the fourth formula is:
in the formula, xi+1、yi+1、zi+1And f is the information of the next point cloud i +1 in the two point clouds, and the fault view angle range.
9. The lidar fault diagnosis method according to claim 8, wherein the second point cloud information includes a point cloud intensity of the target object, and when the lidar to be diagnosed is mounted and fixed at a position to detect the target object, the lidar to be diagnosed determines a fault state of the lidar to be diagnosed according to the second point cloud information, further comprising:
and calculating the average value of the point cloud intensity of the target object, and determining that the laser radar to be diagnosed has the point cloud intensity abnormal fault under the condition that the difference value between the average value of the point cloud intensity of the target object and a pre-measured standard intensity value is greater than the set intensity threshold value.
10. A laser radar fault diagnosis device is characterized by being applied to a laser radar controller and comprising:
the power supply and communication channel fault diagnosis module is used for determining whether a communication channel corresponding to the laser radar to be diagnosed has a fault according to a preset communication protocol, a preset protection measure and a data integrity protection algorithm under the condition that the voltage of each subsystem included in the laser radar to be diagnosed is within a set voltage threshold range, wherein the communication channel is used for transmitting point cloud information of a target object;
and the laser radar fault diagnosis module is used for performing functional diagnosis according to the received point cloud information of the target object under the condition that the communication channel has no fault and meets the characteristic matching triggering condition so as to determine the fault state of the laser radar to be diagnosed.
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| CN114966631A (en) * | 2022-05-19 | 2022-08-30 | 安徽蔚来智驾科技有限公司 | Fault diagnosis and processing method and device for vehicle-mounted laser radar, medium and vehicle |
| CN115032618A (en) * | 2022-08-12 | 2022-09-09 | 深圳市欢创科技有限公司 | Blind area repairing method and device applied to laser radar and laser radar |
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| CN114966631A (en) * | 2022-05-19 | 2022-08-30 | 安徽蔚来智驾科技有限公司 | Fault diagnosis and processing method and device for vehicle-mounted laser radar, medium and vehicle |
| CN115032618A (en) * | 2022-08-12 | 2022-09-09 | 深圳市欢创科技有限公司 | Blind area repairing method and device applied to laser radar and laser radar |
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