CN117289245A - Multi-laser radar external parameter automatic calibration method and computer readable storage medium - Google Patents
Multi-laser radar external parameter automatic calibration method and computer readable storage medium Download PDFInfo
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Abstract
A multi-laser radar external parameter automatic calibration method and a computer readable storage medium. The method comprises the following steps: 101. automatic calibration is carried out between a main vision radar and a vehicle body in the multi-laser radar; 102. performing automatic joint calibration on the rest laser radars and the main vision radar; and 103, verifying and performing error analysis on the calibration results obtained in the steps 101 and 102.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicle, in particular to an external parameter automatic calibration method of a multi-laser radar.
Background
The automatic driving technology in the artificial intelligence field is a hot topic in recent years, and comprises complex tasks such as sensing, control, ground planning and the like, intelligent sensing is a prerequisite for realizing unmanned driving, the primary task is to realize the fusion sensing capability of multiple sensors, and the fusion detection capability of an algorithm also depends on the joint calibration precision between radars. After the radar is loaded, the installation error is eliminated through radar calibration, so that an automatic driving system can accurately position where each sensor is installed. Radar calibration lays a solid foundation for subsequent map establishment, positioning, perception and control, and is a core part and a precondition for stable operation of an automatic driving system.
In the prior art, the traditional calibration method between the single laser radar and the vehicle body or the calibration method of the multiple laser radars is mostly manual calibration, which is time-consuming and difficult to ensure in precision. Moreover, the requirements on heading angle accuracy are higher and higher when the radar and the vehicle body are calibrated, and the traditional method is difficult to meet the high-accuracy requirements. In addition, the traditional laser radar calibration technology needs to be performed in a specific scene, and has a plurality of limitations on the calibration scene.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides an external parameter automatic calibration method of a multi-laser radar and a computer readable storage medium.
The invention provides an external parameter automatic calibration method of a multi-laser radar, which comprises the following steps:
101. automatic calibration is carried out between a main vision radar and a vehicle body in the multi-laser radar;
102. performing automatic joint calibration on the rest laser radars and the main vision radar; and
103. and (5) verifying and performing error analysis on the calibration results obtained in the steps 101 and 102.
In one embodiment, step 101 further comprises the steps of:
201: acquiring original point cloud data of the primary vision radar, filtering non-ground point cloud data, and obtaining ground point cloud data;
202: obtaining an initial value X of an external parameter of the primary vision radar 0 、Y 0 、Z 0 、α 0 、β 0 、γ 0 Wherein X is 0 ,Y 0 ,Z 0 For the coordinates of the primary radar with respect to the vehicle body coordinate system (X,y, Z), alpha 0 Is the initial value of the pitch angle alpha of the main vision radar relative to the vehicle body, beta 0 Is the initial value of the roll angle beta of the main vision radar relative to the vehicle body, gamma 0 An initial value of a heading angle gamma of the main vision radar relative to the vehicle body;
203: constructing a ground levelness function f of the ground under a laser radar coordinate system: f= |a|+|b|, wherein a and B are constants a and B in a plane equation Z ' =ax ' +by ' +c of the ground point cloud data under a laser radar coordinate system, a first algorithm is adopted to take the minimum value of the ground levelness function f as an optimization target, and a group of alpha value, beta value and Z value are updated in a preset step;
204: according to the updated alpha value, beta value and Z value, carrying out three-dimensional coordinate transformation on the ground point cloud data through a transformation matrix R and a translation matrix T;
205: performing least square fitting on the three-dimensional coordinate transformed ground point cloud data to update a plane equation of the ground point cloud data under a laser radar coordinate system;
206: updating the ground levelness function f according to the updated plane equation, judging whether the value of the ground levelness function f is smaller than a preset threshold value or whether the iteration number reaches a preset value N, and if not, returning to execute the steps 203-206; if yes, outputting an alpha value, a beta value and a Z value, and executing step 207;
207: and setting step length according to the precision requirement, traversing the course angle gamma, the Y value and the X value, and when the diameter of the point cloud cluster is minimum, considering that the course angle gamma, the Y value and the X value are closest to accurate values at the moment, thereby obtaining final course angle gamma, Y value and X value.
In one embodiment, the first algorithm is a particle swarm optimization algorithm.
In one embodiment, the transformation matrix R is associated with a change in alpha, beta values and the translation matrix T is associated with a change in Z values.
In one embodiment, the step 102 further comprises the steps of:
401: obtaining original ground point cloud data of a radar to be calibrated in the rest radars and external parameter initial values X0, Y0, Z0, alpha 0, beta 0 and gamma 0;
402: calculating point cloud data of an overlapping region of the radar to be calibrated and the main vision radar from the original ground point cloud data of the radar to be calibrated through a point cloud segmentation and clustering algorithm, registering the main vision radar point cloud data of the overlapping region with the Lei Dadian cloud data to be calibrated by adopting a second algorithm based on external parameter initial values X0, Y0, Z0, alpha 0, beta 0 and gamma 0 of the radar to be calibrated, and calculating accurate X, Y, Z, alpha, beta and gamma;
403: and calibrating other radars to be calibrated according to the steps 401 and 402, and solving X, Y, Z, alpha, beta and gamma of the other radars to be calibrated, thereby realizing multi-radar combined calibration.
In one embodiment, the second algorithm is an iterative closest point algorithm.
In one embodiment, the second algorithm is a normal distribution transformation algorithm.
In one embodiment, the second algorithm is a deep learning based algorithm.
In one embodiment, the step 103 further comprises the steps of:
501: verifying the numerical value difference in the z direction of each radar adjacent area, and considering that the automatic joint calibration among multiple radars meets the requirement when the difference between the z values near each radar adjacent area is within a preset value;
502: judging whether the pitch angle alpha and the roll angle beta of different radars are consistent by verifying whether the parameters of the fitted ground plane equation of each radar are consistent; when the parameters are consistent, the automatic joint calibration among multiple radars is considered to meet the requirements;
503: verifying whether X, Y and course angle gamma corresponding to each radar are consistent or whether the deviation between X, Y and course angle gamma corresponding to each radar is within a preset range, and considering that the automatic joint calibration among multiple radars meets the requirement when the X, Y and course angle gamma corresponding to each radar are consistent or the deviation is within the preset range.
In one embodiment, step 503 further comprises:
the deviation between the radar course angles gamma is estimated through the transverse displacement of the global coordinates of the targets in the vehicle moving process;
the deviation between the radars X is evaluated through the longitudinal displacement of the global coordinates of the targets in the vehicle movement process;
the deviation between the radars Y is evaluated by calculating the lateral displacement of the global coordinates of the target after the vehicle moves once from the left and right sides of the target, respectively.
In one embodiment, the preset value is 5cm.
The invention also provides a computer readable storage medium, on which computer instructions are stored, which when run execute the multi-laser radar external parameter automatic calibration method of the invention.
The method is applied to the intelligent driving vehicle, effectively solves the problems of complex implementation, low precision and dependence on other sensors of the calibration method, greatly realizes the unmanned automation degree, provides possibility for realizing unmanned industrialized landing, lays a solid foundation in the aspects of environment sensing and map building positioning, provides preconditions for realizing automatic calibration of the radar and the camera, and provides a foundation for real-time automatic calibration due to the change of the vibration direction of the sensor in the unmanned vehicle.
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The foregoing summary of the invention, as well as the following detailed description of the invention, will be better understood when read in conjunction with the accompanying drawings. It is to be noted that the drawings are merely examples of the claimed invention. In the drawings, like reference numbers indicate identical or similar elements.
FIG. 1 shows a general flow chart of a method for automatically calibrating a multiple lidar reference according to an embodiment of the invention;
FIG. 2 shows a flow chart of automatic calibration between a primary radar and a vehicle body in a multi-laser radar external parameter automatic calibration method according to an embodiment of the invention;
FIG. 3A shows a point cloud cluster map before heading angles γ, Y, and X are not adjusted;
FIG. 3B shows a point cloud cluster map obtained by the heading angle γ, Y, and X calibration algorithm (i.e. step 207) according to the present invention;
FIG. 4 illustrates a flow chart for automatic joint calibration between multiple radars according to an embodiment of the present invention;
FIG. 5 is a flow chart showing verification and error analysis of the automatic calibration results of the multi-lidar according to an embodiment of the present invention.
Detailed Description
The detailed features and advantages of the present invention will be readily apparent to those skilled in the art from the following detailed description, claims, and drawings that follow.
Compared with the prior art, the external parameter automatic calibration method of the multi-laser radar can automatically adjust the pitch angle and the roll angle of the single-laser radar relative to the vehicle body; the calibration precision of the radar and the heading angle of the vehicle body is effectively improved, and the calibration time is greatly shortened; and moreover, the multi-radar combined calibration can be automatically carried out, the precision requirement of a multi-radar fusion detection algorithm is met, and the calibration scene is unlimited.
FIG. 1 shows a general flow chart of a method for automatically calibrating a multiple laser radar's external parameters according to an embodiment of the invention. The calibration method includes, but is not limited to, the following steps:
step 101: automatic calibration is carried out between a main vision radar and a vehicle body in the multi-laser radar;
step 102: performing automatic joint calibration on the rest laser radars and the main vision radar; and
step 103: and (3) verifying and analyzing errors of the automatic calibration results of the step 101 and the step 102.
In step 101, the coordinates of the main vision radar with respect to the vehicle body coordinate system are set as (X, Y, Z), and the initial value X of the external parameters of the radar is obtained 0 ,Y 0 ,Z 0 I.e. the factory setting of the external ginseng. Calibration method of this stepAnd (3) calibrating the pitch angle alpha, the roll angle beta and the longitudinal displacement Z of the laser radar by taking the flat ground as a reference. The specific principle is that the plane equation of the ground under the laser radar coordinate system is as follows: z ' =ax ' +by ' +c, where A, B, C is a coefficient of the plane equation, constructing a ground levelness function f of the ground in the lidar coordinate system: f= |a|+|b|. And fitting a plane equation of the ground point cloud under a laser radar coordinate system by adopting a least square method. The minimum value of the ground levelness function f is taken as a nonlinear optimization problem, so that a Particle Swarm Optimization (PSO) algorithm can be adopted, the minimum value of f is taken as an optimization target, and the optimal solution of the pitch angle alpha and the roll angle beta can be automatically obtained.
Fig. 2 shows a flowchart of automatic calibration between a main vision radar and a vehicle body in an external parameter automatic calibration method of a multi-laser radar according to an embodiment of the present invention.
Step 201: acquiring original point cloud data, and filtering non-ground point cloud data to obtain ground point cloud data;
step 202: obtaining an external parameter initial value X of a main vision radar 0 、Y 0 、Z 0 、α 0 、β 0 、γ 0 Wherein X is 0 ,Y 0 ,Z 0 Is the initial value of the coordinates (X, Y, Z) of the main vision radar relative to the vehicle body coordinate system, alpha 0 Is the initial value of the pitch angle alpha of the main vision radar relative to the vehicle body, beta 0 Is the initial value of the roll angle beta of the main vision radar relative to the car body, gamma 0 Is the initial value of the heading angle gamma of the main vision radar relative to the car body.
Step 203: constructing a ground levelness function f of the ground under a laser radar coordinate system: f= |a|+|b|, wherein a and B are constants a and B in a plane equation z ' =ax ' +by ' +c of ground point cloud data in a laser radar coordinate system; and updating a group of alpha value, beta value and Z value by a preset step by adopting a first algorithm with the minimum f value of the ground levelness function as an optimization target.
In one embodiment, the first algorithm is a particle swarm optimization algorithm (PSO algorithm).
Step 204: and according to the updated alpha value, beta value and Z value, carrying out three-dimensional coordinate transformation on the ground point cloud data through a transformation matrix R and a translation matrix T.
In one embodiment, the transformation matrix R is associated with a change in the alpha and beta values and the translation matrix T is associated with a change in the Z values. It is noted that the translation matrix T and the transformation matrix R can be obtained by a person skilled in the art from the variations of the values of α, β and Z.
Step 205: and carrying out least square fitting on the ground point cloud data subjected to the three-dimensional coordinate transformation to update a plane equation of the ground point cloud data under a laser radar coordinate system.
Step 206: updating the ground levelness function f according to the updated plane equation, judging whether the f value is smaller than a preset threshold value or whether the iteration number reaches a preset value N, and if not, returning to the execution step 203-206; if so, the alpha, beta and Z values are output and step 207 is performed.
Step 207: and setting step traversing course angles gamma, Y and X according to the precision requirement, and when the diameter of the point cloud cluster is minimum, considering that the course angle gamma, Y and X are closest to accurate values at the moment, thereby obtaining final course angle gamma, Y and X.
In one embodiment, the point cloud cluster refers to a cluster of point cloud clusters formed by different coordinates for the same marker in a plurality of frames acquired by the primary vision radar in a far-to-near process of the vehicle. Specifically, in the process that the vehicle runs along a straight line, the laser radar collects multiple frames of laser point clouds containing the same marker, the marker is extracted from the laser point clouds in a mode of elevation filtering and the like, and then a clustering center (xi ', yi') of the i-th frame marker in a two-dimensional plane of the laser radar coordinate system is obtained by adopting a K-means clustering algorithm. The local coordinates of the markers are converted into the geodetic coordinates by combining with combined inertial navigation vehicle information (GPS), the geodetic coordinates (Xi ', yi') of each frame of the markers are obtained, and due to inaccurate radar course angle, the multi-frame coordinates of the same marker obtained in the process of moving the vehicle from far to near are different, so that a cluster point cloud cluster can be formed. The diameter of the point cloud cluster is related to X, Y and course angle, so the course angle adjustment algorithm principle is that step size is set in a certain range according to accuracy requirements to traverse course angles gamma, Y and X, and when the diameter of the point cloud cluster is minimum, the course angle is considered to be the nearest accurate value, so that the course angles gamma, Y and X are obtained.
Fig. 3A shows a point cloud cluster map before the heading angles γ, Y, and X are not adjusted. Fig. 3B shows a point cloud cluster map obtained by the heading angle γ, Y, and X calibration algorithm (i.e. step 207) according to the present invention.
FIG. 4 shows a flow chart for automatic joint calibration between multiple radars according to an embodiment of the present invention. Because the main vision radar is calibrated with the vehicle body, other radars can realize the calibration of all radars only by taking the main vision radar as a reference to perform multi-radar combined calibration. Other radars obtain initial values X0, Y0, Z0, alpha 0, beta 0 and gamma 0 according to the mounting positions of the vehicles, and then obtain accurate X, Y, Z, alpha, beta and gamma values through a point cloud registration method (ICP). Compared with the prior art, the method has the advantages that in the prior art, each radar needs to be calibrated in a specific scene, but only one calibration scene is needed to be put, namely, a calibration object is put in front of the automobile, and markers are put in the overlapping area of multiple radar point clouds in the far and near directions.
The automatic joint calibration mode between the multiple radars of the invention comprises, but is not limited to, the following steps:
step 401: obtaining original ground point cloud data of a radar to be calibrated in other radars and an external parameter initial value X 0 、Y 0 、Z 0 、α 0 、β 0 、γ 0 . Wherein X is 0 ,Y 0 ,Z 0 For the initial value of the coordinates (X, Y, Z) of the radar to be marked relative to the vehicle body coordinate system, alpha 0 To be the initial value of the pitch angle alpha of the radar to be marked relative to the vehicle body, beta 0 For the initial value of roll angle beta of the radar to be marked relative to the vehicle body, gamma 0 The initial value of the heading angle gamma of the radar to be marked relative to the vehicle body.
Step 402: and (3) solving point cloud data of an overlapping area of the radar to be calibrated and the main vision radar in original ground point cloud data of the radar to be calibrated through a point cloud segmentation and clustering algorithm, registering the main vision radar point cloud data of the overlapping area with the Lei Dadian cloud data to be calibrated by adopting a second algorithm based on external parameter initial values X0, Y0, Z0, alpha 0, beta 0 and gamma 0, and solving accurate X, Y, Z, alpha, beta and gamma.
In one embodiment, the second algorithm is an ICP (iterative closest point) algorithm or NDT algorithm (normal distribution transform) algorithm or a deep learning based method.
Step 403: and calibrating other radars to be calibrated according to the steps 401 and 402, thereby realizing multi-radar combined calibration.
FIG. 5 is a flow chart showing verification and error analysis of the automatic calibration results of the multi-lidar according to an embodiment of the present invention.
501: and verifying the numerical value difference in the z direction of each radar adjacent area, and considering that the automatic joint calibration among multiple radars meets the requirement when the difference between the z values near each radar adjacent area is within a preset value.
Ideally, after calibrating the multi-laser radar, the z values of different radars should be consistent, so that the z values of different radars should be ensured to be within a certain preset value as much as possible.
In one embodiment, the predetermined value is 5cm, preferably 3cm.
502: judging whether the pitch angle alpha and the roll angle beta of different radars are consistent by verifying whether the parameters of the fitted ground plane equation of each radar are consistent; and when the parameters are consistent, the automatic joint calibration among the multiple radars is considered to meet the requirements.
Ideally, the ground plane equation that each radar is ultimately fitted to should be identical, that is, the parameters of the plane equation that each radar is fitted to are identical, and the pitch angle α and roll angle β of each radar should be identical. Thus, the present invention verifies the pitch and roll angles of each radar by verifying the parametric consistency of the fitted ground plane equation for each radar.
503: verifying whether X, Y and course angle gamma corresponding to each radar are consistent or whether the deviation between X, Y and course angle gamma corresponding to each radar is within a preset range, and considering that the automatic joint calibration among multiple radars meets the requirement when the X, Y and course angle gamma corresponding to each radar are consistent or the deviation is within the preset range.
Ideally, the corresponding X, Y and heading angle γ of each radar should be the same or within a certain small range in different directions. This step verifies the consistency of the X, Y and heading angle γ corresponding to each radar to eliminate inertial navigation errors. The deviation of the global coordinates of the center points of the same small object during the movement of the vehicle can be utilized for verification.
Specifically, if there is a deviation between the course angles γ of the radars, the global coordinates of the small target will move laterally during the movement of the vehicle. If there is a deviation between the X's of each radar, there is a longitudinal movement of the global coordinates of the small object. The deviation of Y is verified by the lateral deviation of the global coordinates of the small object being calculated by the vehicle moving once respectively from the left and right sides of the small object.
The invention also provides a computer readable storage medium, on which computer instructions are stored, which when run execute the multi-laser radar external parameter automatic calibration method of the invention.
The method is applied to the intelligent driving vehicle, can effectively solve the problems of complex implementation, low precision and dependence on other sensors of the calibration method, greatly realizes the unmanned automation degree, provides possibility for realizing unmanned industrialized landing, lays a solid foundation in the aspects of environment perception and map building positioning, provides a precondition for realizing automatic calibration of the radar and the camera, and provides a foundation for real-time automatic calibration in the unmanned vehicle because the vibration direction of the sensor is changed.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the above disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations of the present application may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this application, and are therefore within the spirit and scope of the exemplary embodiments of this application.
Flowcharts are used in this application to describe the operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously. At the same time, other operations are added to or removed from these processes.
Meanwhile, the present application uses specific words to describe embodiments of the present application. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present application may be combined as suitable.
Furthermore, those skilled in the art will appreciate that the various aspects of the invention are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.
The computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer readable signal medium may be propagated through any suitable medium including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.
The computer program code necessary for operation of portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, c#, vb net, python, etc., a conventional programming language such as C language, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, ruby and Groovy, or other programming languages, etc. The program code may execute entirely on the user's computer or as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or the use of services such as software as a service (SaaS) in a cloud computing environment.
Furthermore, the order in which the elements and sequences are presented, the use of numerical letters, or other designations are used in the application and are not intended to limit the order in which the processes and methods of the application are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present application. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed herein and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the subject application. Indeed, less than all of the features of a single embodiment disclosed above.
The terms and expressions which have been employed herein are used as terms of description and not of limitation. The use of these terms and expressions is not meant to exclude any equivalents of the features shown and described (or portions thereof), and it is recognized that various modifications are possible and are intended to be included within the scope of the claims. Other modifications, variations, and alternatives are also possible. Accordingly, the claims should be looked to in order to cover all such equivalents.
Also, it should be noted that while the present invention has been described with reference to the particular embodiments presently, it will be appreciated by those skilled in the art that the above embodiments are provided for illustration only and that various equivalent changes or substitutions may be made without departing from the spirit of the invention, and therefore, the changes and modifications to the above embodiments shall fall within the scope of the claims of the present application as long as they are within the true spirit of the invention.
Claims (12)
1. The method for automatically calibrating the external parameters of the multi-laser radar is characterized by comprising the following steps of:
101. automatic calibration is carried out between a main vision radar and a vehicle body in the multi-laser radar;
102. performing automatic joint calibration on the rest laser radars and the main vision radar; and
103. and (5) verifying and performing error analysis on the calibration results obtained in the steps 101 and 102.
2. The method for automatically calibrating a multi-laser radar external reference according to claim 1, wherein the step 101 further comprises the steps of:
201: acquiring original point cloud data of the primary vision radar, filtering non-ground point cloud data, and obtaining ground point cloud data;
202: obtaining an initial value X of an external parameter of the primary vision radar 0 、Y 0 、Z 0 、α 0 、β 0 、γ 0 Wherein X is 0 ,Y 0 ,Z 0 Is the initial value of the coordinates (X, Y, Z) of the main vision radar relative to the vehicle body coordinate system, alpha 0 Is the initial value of the pitch angle alpha of the main vision radar relative to the vehicle body, beta 0 Is the initial value of the roll angle beta of the main vision radar relative to the vehicle body, gamma 0 An initial value of a heading angle gamma of the main vision radar relative to the vehicle body;
203: constructing a ground levelness function f of the ground under a laser radar coordinate system: f= |a|+|b|, wherein a and B are constants a and B in a plane equation Z ' =ax ' +by ' +c of the ground point cloud data under a laser radar coordinate system, a first algorithm is adopted to take the minimum value of the ground levelness function f as an optimization target, and a group of alpha value, beta value and Z value are updated in a preset step;
204: according to the updated alpha value, beta value and Z value, carrying out three-dimensional coordinate transformation on the ground point cloud data through a transformation matrix R and a translation matrix T;
205: performing least square fitting on the three-dimensional coordinate transformed ground point cloud data to update a plane equation of the ground point cloud data under a laser radar coordinate system;
206: updating the ground levelness function f according to the updated plane equation, judging whether the value of the ground levelness function f is smaller than a preset threshold value or whether the iteration number reaches a preset value N, and if not, returning to execute the steps 203-206; if yes, outputting an alpha value, a beta value and a Z value, and executing step 207;
207: and setting step length according to the precision requirement, traversing the course angle gamma, the Y value and the X value, and when the diameter of the point cloud cluster is minimum, considering that the course angle gamma, the Y value and the X value are closest to accurate values at the moment, thereby obtaining final course angle gamma, Y value and X value.
3. The method for automatically calibrating the external parameters of the multi-laser radar according to claim 2, wherein the first algorithm is a particle swarm optimization algorithm.
4. The method for automatically calibrating the external parameters of the multi-laser radar according to claim 2, wherein the transformation matrix R is associated with the change of alpha value and beta value, and the translation matrix T is associated with the change of Z value.
5. The method for automatically calibrating a multi-laser radar external reference according to claim 1, wherein the step 102 further comprises the steps of:
401: obtaining original ground point cloud data of a radar to be calibrated in the rest radars and external parameter initial values X0, Y0, Z0, alpha 0, beta 0 and gamma 0;
402: calculating point cloud data of an overlapping region of the radar to be calibrated and the main vision radar from the original ground point cloud data of the radar to be calibrated through a point cloud segmentation and clustering algorithm, registering the main vision radar point cloud data of the overlapping region with the Lei Dadian cloud data to be calibrated by adopting a second algorithm based on external parameter initial values X0, Y0, Z0, alpha 0, beta 0 and gamma 0 of the radar to be calibrated, and calculating accurate X, Y, Z, alpha, beta and gamma;
403: and calibrating other radars to be calibrated according to the steps 401 and 402, and solving X, Y, Z, alpha, beta and gamma of the other radars to be calibrated, thereby realizing multi-radar combined calibration.
6. The method for automatically calibrating a multi-laser radar external reference of claim 5, wherein the second algorithm is an iterative closest point algorithm.
7. The method for automatically calibrating the external parameters of the multi-laser radar according to claim 5, wherein the second algorithm is a normal distribution transformation algorithm.
8. The method for automatically calibrating the external parameters of the multi-laser radar according to claim 5, wherein the second algorithm is a deep learning-based algorithm.
9. The method for automatically calibrating a multi-laser radar according to claim 1, wherein the step 103 further comprises the steps of:
501: verifying the numerical value difference in the z direction of each radar adjacent area, and considering that the automatic joint calibration among multiple radars meets the requirement when the difference between the z values near each radar adjacent area is within a preset value;
502: judging whether the pitch angle alpha and the roll angle beta of different radars are consistent by verifying whether the parameters of the fitted ground plane equation of each radar are consistent; when the parameters are consistent, the automatic joint calibration among multiple radars is considered to meet the requirements;
503: verifying whether X, Y and course angle gamma corresponding to each radar are consistent or whether the deviation between X, Y and course angle gamma corresponding to each radar is within a preset range, and considering that the automatic joint calibration among multiple radars meets the requirement when the X, Y and course angle gamma corresponding to each radar are consistent or the deviation is within the preset range.
10. The method for automatically calibrating a multiple laser radar external reference of claim 9, wherein step 503 further comprises:
the deviation between the radar course angles gamma is estimated through the transverse displacement of the global coordinates of the targets in the vehicle moving process;
the deviation between the radars X is evaluated through the longitudinal displacement of the global coordinates of the targets in the vehicle movement process;
the deviation between the radars Y is evaluated by calculating the lateral displacement of the global coordinates of the target after the vehicle moves once from the left and right sides of the target, respectively.
11. The method for automatically calibrating the external parameters of the multi-laser radar according to claim 9, wherein the preset value is 5cm.
12. A computer readable storage medium having stored thereon computer instructions which, when run, perform the method of automatic calibration of a spread of a multi-lidar according to any of claims 1 to 11.
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