CN117310666A - Automatic calibration device and method for ADAS laser radar for vehicle offline detection - Google Patents
Automatic calibration device and method for ADAS laser radar for vehicle offline detection Download PDFInfo
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- CN117310666A CN117310666A CN202311407287.5A CN202311407287A CN117310666A CN 117310666 A CN117310666 A CN 117310666A CN 202311407287 A CN202311407287 A CN 202311407287A CN 117310666 A CN117310666 A CN 117310666A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/497—Means for monitoring or calibrating
Abstract
The invention discloses an automatic calibration device for ADAS laser radar for vehicle offline detection, which relates to the technical field of ADAS calibration. The invention also discloses an automatic calibration method of the ADAS laser radar for vehicle offline detection, which comprises the following steps: s100, acquiring vehicle parameters and target parameters; s200, collecting laser radar point cloud data; s300, preprocessing point cloud data; s400, coarsely registering the point cloud; s500, fine point cloud registration; s600, storing a calibration result. The invention improves the precision and reliability of laser radar sensor calibration, improves the universality of laser radar target devices, improves the integral calibration efficiency, greatly promotes the application of ADAS systems in the field of automobile manufacturing, and further improves the driving safety and the driving reliability.
Description
Technical Field
The invention relates to the technical field of ADAS calibration, in particular to an automatic calibration device and method for ADAS laser radar for vehicle offline detection.
Background
With the rapid development of modern automobile technology, the application of advanced driving assistance system (Advanced Driver Assistance Systems, abbreviated as ADAS) has become one of the key factors for improving driving safety and reducing traffic accident risk. The ADAS system uses various sensors to sense the environment surrounding the vehicle and provide driver warning, intervention, and even autonomous control functions to reduce the likelihood of human driving errors. The laser radar is used as an important sensing device, and the laser beam is emitted and the reflection of the laser radar is measured to obtain three-dimensional space information of the environment, so that key data support is provided for an ADAS system.
However, lidar requires accurate calibration during manufacturing to ensure that it accurately senses the surrounding environment and provides reliable data. Calibration is a key step in lidar applications, which involves calibrating actual measurements to theoretical models to eliminate systematic errors and uncertainties. The traditional calibration method generally needs complicated equipment and manual intervention, is time-consuming and is easily influenced by environmental factors, and the improvement of production efficiency and calibration precision is limited.
Aiming at an ADAS laser radar automatic calibration method, an ADAS laser radar automatic calibration device and an ADAS laser radar automatic calibration system for vehicle offline detection, the main problems existing in the prior art are as follows:
1. the traditional laser radar calibration method involves manual intervention, and the skill level of an operator and the manual measurement method can be directly related to the calibration precision;
2. the existing laser radar target device for vehicle offline detection is fixed in position and poor in universality, and can only be adapted to a single vehicle type;
3. the existing vehicle offline detection laser radar calibration flow belongs to series, and is time-consuming, long and low in efficiency.
Accordingly, those skilled in the art have been working to develop an automatic calibration device and method for an ADAS lidar for vehicle off-line detection.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the present invention aims to solve the technical problem of how to realize automatic calibration of a laser radar when a vehicle goes offline, so as to simplify the calibration process, improve the efficiency and ensure the calibration accuracy.
In one embodiment of the invention, an automatic calibration device for ADAS laser radar for vehicle offline detection is provided, which comprises a portal frame, a right portal frame, a cross rod, a fixed bracket, a calibration plate and a control and power module;
the portal frames are bearing support frames of an ADAS laser radar automatic calibration device for vehicle offline detection, are fixedly installed on the ground and are immovable and used for connecting cross bars, and comprise a left portal frame and a right portal frame, wherein the left portal frame and the right portal frame have the same specification and are longitudinally and symmetrically arranged on the left side and the right side of a vehicle to be calibrated, and electric slide rails are longitudinally installed on the left portal frame and the right portal frame;
the cross rod is arranged on the electric slide rails of the left portal frame and the right portal frame, is provided with a cross slide rail, is connected with the fixed bracket, and controls the transverse translation and the vertical movement of the fixed bracket;
the fixed bracket is arranged on the cross slide rail on the cross rod and is connected with the calibration plate;
the calibration plate is used for calibrating a target used by the laser radar and is connected to the fixed bracket in a semi-fixed mode;
the control and power module comprises a servo motor and a control circuit, and controls the calibration plate to move to a designated position in the longitudinal direction, the transverse direction and the vertical direction.
Optionally, in the automatic calibration device for detecting the vehicle offline in the above embodiment, the left portal frame and the right portal frame use aluminum profile materials.
Optionally, in the automatic calibration device for detecting an ADAS laser radar for vehicle offline in any one of the embodiments, the longitudinal length of the left portal frame and the right portal frame is 2 meters, the height is 3 meters, and the lateral distance between the left portal frame and the right portal frame is 4 meters.
Optionally, in the automatic calibration device for detecting the vehicle offline in any embodiment, the cross bar is made of an aluminum profile material.
Optionally, in the automatic calibration device for vehicle offline detection ADAS laser radar in any one of the embodiments, there are 4 cross slide rails.
Optionally, in the automatic calibration device for detecting the vehicle offline in any embodiment, the fixing support is made of aluminum profile materials.
Optionally, in the automatic calibration device for detecting the vehicle offline in any embodiment, the fixed support is driven by the control and power module, so that the movement in the transverse direction and the vertical direction can be realized, wherein the transverse movement range is 0-1 m, the movement precision is 1 mm, the movement range in the vertical direction is 0-1 m, the movement precision is 1 mm, and the repeated positioning precision is not more than +/-1 mm.
Further, in the automatic calibration device for detecting the vehicle offline in the above embodiment, the fixing bracket is an L-shaped bracket, the transverse portion is 20 cm long, and the fixing bracket is wrapped with black foam.
Optionally, in the automatic calibration device for vehicle offline detection ADAS of any one of the embodiments, a base material of the calibration plate is aluminum alloy, and a surface of the calibration plate is covered with a white diffuse reflection coating, and the diffuse reflectance is 80%.
Optionally, in the automatic calibration device for vehicle offline detection ADAS lidar in any of the embodiments above, the calibration plate is sized to be 90 cm wide and 60 cm high.
Further, in the automatic calibration device for detecting the vehicle offline in the above embodiment, the number of calibration plates is 4, and the 4 calibration plates are placed in a pre-designed posture, so that the point cloud is constrained from three rotational degrees of freedom, and the calibration precision is improved.
Optionally, in the automatic calibration device for detecting the vehicle offline in any embodiment, the fixing support and the calibration plate are connected by using a ball hinge, the ball hinge can freely rotate in a release state, and the ball hinge is fixed and can not rotate in a locking state.
Optionally, in the automatic calibration device for detecting the vehicle offline in any embodiment, the calibration plate is locked after rotating to the set position when being used for the first time, the posture of the calibration plate is not changed after the locking, and relative translation and rotation do not occur relative to the fixed bracket in the repeated positioning process of the calibration plate.
Further, in the automatic calibration device for detecting the vehicle offline in the embodiment, the control and power module drives the cross bar to longitudinally move along the left portal frame and the right portal frame, the moving range is 0-1 m, and the moving precision is 1 mm.
Based on any one of the above embodiments, in another embodiment of the present invention, an automatic calibration method for an ADAS lidar for vehicle offline detection is provided, including the following steps:
s100, acquiring vehicle parameters and target parameters, and reading the vehicle parameters and the target parameters;
s200, collecting laser radar point cloud data, and statically collecting a frame of original laser radar point cloud data;
s300, preprocessing point cloud data, and preprocessing original laser radar point cloud data;
s400, performing coarse registration of point cloud, namely performing coarse registration on the preprocessed point cloud data and the preprocessed target point cloud data;
s500, performing fine point cloud registration, namely performing fine registration on the roughly registered point cloud data and the roughly registered target point cloud data;
s600, storing a calibration result, and writing calibration parameters obtained after fine registration into a configuration file in the vehicle domain controller.
Optionally, in the automatic calibration method for the ADAS lidar for vehicle offline detection in the above embodiment, the vehicle parameters in step S100 include vehicle coordinate system definition, vehicle length, width and height information, a distance from an origin of the vehicle coordinate system to the ground, and a distance from the origin of the vehicle coordinate system to the headstock; the target parameters are four vertex space three-dimensional coordinate sets Pt set of all calibration plates under the vehicle coordinate system.
Optionally, in the automatic calibration method for vehicle offline detection ADAS in any of the foregoing embodiments, the laser radar point cloud data in step S200 is a frame of original point cloud data pc_src collected by the laser radar device, where the point cloud data includes N reflection points, N is an integer greater than 0, the reflection point format is (x, y, z, intensity), x represents an x-axis coordinate of a reflection point in the laser radar coordinate system, y represents a y-axis coordinate of a reflection point in the laser radar coordinate system, z represents a z-axis coordinate of a reflection point in the laser radar coordinate system, and intensity represents reflection intensity information of the reflection point.
Further, in the automatic calibration method for the ADAS lidar for vehicle offline detection in the above embodiment, the intensity value is between 0 and 255.
Optionally, in the automatic calibration method for detecting ADAS lidar for vehicle offline in any embodiment, the preprocessing of the point cloud data in step S300 includes intensity filtering, and the point cloud is filtered according to the reflected intensity.
Further, in the automatic calibration method for detecting the vehicle offline and ADAS laser radar in the above embodiment, the preprocessing of the point cloud data in step S300 further includes range filtering, and the point cloud is filtered according to the distance range (x/y/z).
Further, in the automatic calibration method for detecting the vehicle offline in the above embodiment, the preprocessing of the point cloud data in step S300 further includes the point cloud downsampling, filtering out most of the invalid reflection points in the original point cloud data, and obtaining the preprocessed point cloud data pc_pre, where the preprocessed point cloud data only includes reflection points representing the calibration board.
Optionally, in the automatic calibration method for vehicle offline detection ADAS in any of the foregoing embodiments, the coarse registration of the point cloud in step S400 includes a 4x4 matrix representing translation and rotation, which is estimated preliminarily, so that the preprocessed point cloud data pc_pre and the target point cloud data pc_dst are approximately overlapped in space, where the target point cloud pc_dst is constructed by interpolation according to the target parameter pt_set obtained in step S100.
Optionally, in the automatic calibration method for the ADAS lidar for vehicle offline detection in the above embodiment, step S400 includes:
s410, clustering point cloud data, namely clustering point clouds representing each calibration plate in the preprocessed point cloud data PC_pre and the preprocessed target point cloud data PC_dst by using a DBSCAN algorithm;
s420, calculating three-dimensional coordinates, and calculating the three-dimensional coordinates of the central point of each calibration plate in the preprocessed point cloud data PC_pre and the preprocessed target point cloud data PC_dst to respectively obtain a point cloud data clustering point set CPts_pre and a target point cloud data clustering point set CPts_dst;
s430, calculating a coarse registration matrix, and calculating a registration result of a point cloud data clustering point set CPts_pre and a target point cloud data clustering point set CPts_dst by using a Super4PCS algorithm to obtain a 4x4 coarse registration matrix M0=super 4PCS (CPts_pre, CPts_dst);
s440, calculating rough registration point cloud data, multiplying and converting the preprocessed point cloud data PC_pre and a rough registration matrix M0 to obtain rough registration point cloud data PC_sup=PC_pre.M0 T 。
Optionally, in the automatic calibration method for the ADAS lidar for vehicle offline detection in any of the above embodiments, step S500 includes:
s510, calculating a fine registration matrix, and calculating a registration result of coarse registration point cloud data PC_sup and target point cloud data PC_dst by using an Iterative Closest Point (ICP) algorithm to obtain a 4x4 fine registration matrix M1=ICP (PC_sup, PC_dst);
and S520, calculating an optimal registration matrix, and combining the coarse registration matrix M0 and the fine registration matrix M1 to obtain an optimal registration matrix M=M1.M0, namely, a laser radar external parameter calibration result.
Optionally, in the automatic calibration method for the ADAS lidar for vehicle offline detection in any embodiment, the storing of the calibration result in step S600 is specifically writing the calibration result into a configuration file of a vehicle domain controller, so that the subsequent use is convenient.
The invention designs the ADAS laser radar automatic calibration device for vehicle offline detection, which improves the calibration precision, can be compatible with different vehicle types, saves the cost and improves the universality of the production line; the invention designs the automatic calibration method for the ADAS laser radar for vehicle offline detection, which improves the whole flow efficiency of ADAS calibration and the accuracy of the calibration result. The invention improves the precision and reliability of laser radar sensor calibration, improves the universality of laser radar target devices, improves the integral calibration efficiency, greatly promotes the application of ADAS systems in the field of automobile manufacturing, and further improves the driving safety and the driving reliability.
The conception, specific structure, and technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, features, and effects of the present invention.
Drawings
Fig. 1 is a schematic diagram illustrating a structure of an automatic calibration device for an ADAS lidar for vehicle offline detection according to an exemplary embodiment;
FIG. 2 is a schematic diagram illustrating the connection of a stationary bracket and a calibration plate according to an exemplary embodiment;
fig. 3 is a flowchart illustrating an automatic calibration method of an ADAS lidar for vehicle offline detection according to an example embodiment.
Detailed Description
The following description of the preferred embodiments of the present invention refers to the accompanying drawings, which make the technical contents thereof more clear and easy to understand. The present invention may be embodied in many different forms of embodiments and the scope of the present invention is not limited to only the embodiments described herein.
In the drawings, like structural elements are referred to by like reference numerals and components having similar structure or function are referred to by like reference numerals. The dimensions and thickness of each component shown in the drawings are arbitrarily shown, and the present invention is not limited to the dimensions and thickness of each component. The thickness of the components is schematically and appropriately exaggerated in some places in the drawings for clarity of illustration.
The inventor designs an ADAS laser radar automatic calibration device for vehicle offline detection, which comprises a portal frame, a right portal frame, a cross rod, a fixed bracket, a calibration plate and a control and power module as shown in figure 1;
the automatic calibration device comprises a horizontal rod, a left portal frame, a right portal frame, an electric sliding rail, a left portal frame, a right portal frame, a left portal frame and a right portal frame, wherein the horizontal rod is fixedly arranged on the ground and is used for connecting the horizontal rod;
the cross rod is arranged on the electric slide rails of the left portal frame and the right portal frame, is provided with a cross slide rail, is connected with the fixed bracket, controls the transverse translation and the vertical movement of the fixed bracket, and is made of aluminum profile materials;
the fixed brackets are arranged on cross slide rails on the cross rods and are connected with the calibration plates, 4 cross slide rails are made of aluminum profile materials, the fixed brackets are driven by the control and power module and can realize transverse and vertical movement, wherein the transverse movement range is 0-1 m, the movement precision is 1 mm, the vertical movement range is 0-1 m, the movement precision is 1 mm, the repeated positioning precision is not more than +/-1 mm, the fixed brackets are L-shaped brackets, the transverse part is 20 cm long, and the fixed brackets are wrapped by black foam cotton;
the calibration plate is used for calibrating a target used by the laser radar and is connected to the fixed bracket in a semi-fixed mode, the base material of the calibration plate is aluminum alloy, the surface of the calibration plate is covered with a white diffuse reflection coating, the diffuse reflection rate is 80%, the size of the calibration plate is 90 cm wide and 60 cm high, 4 calibration plates are arranged in total, the 4 calibration plates are placed in a pre-designed posture, the point cloud is restrained from three rotational degrees of freedom, and the calibration precision is improved; the fixing support is connected with the calibration plate through a spherical hinge, the calibration plate can freely rotate under the spherical hinge in a release state, and the calibration plate is fixed and cannot rotate under the spherical hinge in a locking state; when the calibration plate is used for the first time, the calibration plate rotates to a set position and then is locked, the calibration plate does not change the gesture after locking, and relative translation and rotation do not occur relative to the fixed bracket in the repeated positioning process of the calibration plate;
the control and power module comprises a servo motor and a control circuit, the calibration plate is controlled to move to a designated position in the longitudinal direction, the transverse direction and the vertical direction, the control and power module drives the cross rod to move longitudinally along the left portal frame and the right portal frame, the moving range is 0-1 m, and the moving precision is 1 mm.
Based on the above embodiment, the inventor provides an automatic calibration method for an ADAS laser radar for vehicle offline detection, as shown in fig. 2, including the following steps:
s100, acquiring vehicle parameters and target parameters, and reading the vehicle parameters and the target parameters, wherein the vehicle parameters comprise vehicle coordinate system definition, vehicle length, width and height information, a ground distance from an origin of the vehicle coordinate system, and a head distance from the origin of the vehicle coordinate system; the target parameter is a three-dimensional coordinate set pt_set of four vertex spaces of all calibration plates in the vehicle coordinate system.
S200, collecting laser radar point cloud data, wherein a frame of original laser radar point cloud data is statically collected, the laser radar point cloud data is a frame of original point cloud data PC_src collected by laser radar equipment, the point cloud data comprises N reflection points, N is an integer greater than 0, the reflection point format is (x, y, z, intensity), x represents the x-axis coordinate of the reflection point under the laser radar coordinate system, y represents the y-axis coordinate of the reflection point under the laser radar coordinate system, z represents the z-axis coordinate of the reflection point under the laser radar coordinate system, intensity represents reflection intensity information of the reflection point, and the intensity value is between 0 and 255.
S300, preprocessing point cloud data, namely preprocessing the original laser radar point cloud data, wherein the preprocessing of the point cloud data comprises intensity filtering, filtering the point cloud according to reflection intensity, filtering the point cloud according to a distance range (x/y/z), downsampling the point cloud, filtering out most invalid reflection points in the original point cloud data, and obtaining preprocessed point cloud data PC_pre, wherein the preprocessed point cloud data only comprises reflection points representing a calibration plate.
S400, performing coarse registration on the preprocessed point cloud data and the target point cloud data, wherein the coarse registration comprises a 4x4 matrix which is preliminarily estimated and represents translation and rotation, so that the preprocessed point cloud data PC_pre and the target point cloud data PC_dst are approximately overlapped in space, and the target point cloud PC_dst is obtained by interpolation construction according to the target parameter Pt_set obtained in the step S100; the method specifically comprises the following steps:
s410, clustering point cloud data, namely clustering point clouds representing each calibration plate in the preprocessed point cloud data PC_pre and the preprocessed target point cloud data PC_dst by using a DBSCAN algorithm;
s420, calculating three-dimensional coordinates, and calculating the three-dimensional coordinates of the central point of each calibration plate in the point cloud data PC_pre and the target point cloud data PC_dst to respectively obtain a point cloud data clustering point set CPts_pre and a target point cloud data clustering point set CPts_dst;
s430, calculating a coarse registration matrix, and calculating a registration result of a point cloud data clustering point set CPts_pre and a target point cloud data clustering point set CPts_dst by using a Super4PCS algorithm to obtain a 4x4 coarse registration matrix M0=super 4PCS (CPts_pre, CPts_dst);
s440, calculating rough registration point cloud data, multiplying and converting the preprocessed point cloud data PC_pre and a rough registration matrix M0 to obtain rough registration point cloud data PC_sup=PC_pre.M0 T 。
S500, performing fine point cloud registration, namely performing fine registration on the roughly registered point cloud data and the roughly registered target point cloud data; comprising the following steps:
s510, calculating a fine registration matrix, and calculating a registration result of coarse registration point cloud data PC_sup and target point cloud data PC_dst by using an Iterative Closest Point (ICP) algorithm to obtain 4x4 fine registration
Registration matrix m1=icp (pc_sup, pc_dst);
and S520, calculating an optimal registration matrix, and combining the coarse registration matrix M0 and the fine registration matrix M1 to obtain an optimal registration matrix M=M1.M0, namely, a laser radar external parameter calibration result.
S600, storing calibration results, namely writing the calibration parameters obtained after fine registration into a configuration file in the vehicle domain controller, wherein the calibration results are written into the configuration file of the vehicle domain controller, so that the vehicle domain controller is convenient to use.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (10)
1. The automatic calibration device for the ADAS laser radar for vehicle offline detection is characterized by comprising a portal frame, a right portal frame, a cross rod, a fixed support, a calibration plate and a control and power module:
the portal frame is a bearing support frame of an ADAS laser radar automatic calibration device for vehicle offline detection, is fixedly arranged on the ground, is immovable and is used for connecting a cross bar, and comprises a left portal frame and a right portal frame, wherein the left portal frame and the right portal frame have the same specification and are longitudinally and symmetrically arranged on the left side and the right side of a vehicle to be calibrated, and electric sliding rails are longitudinally arranged on the left portal frame and the right portal frame;
the cross rod is arranged on the electric slide rails of the left portal frame and the right portal frame, is provided with a cross slide rail, is connected with a fixed bracket, and controls the transverse translation and the vertical movement of the fixed bracket;
the fixed bracket is arranged on the cross slide rail on the cross rod and is connected with the calibration plate;
the calibration plate is used for calibrating a target used by the laser radar and is connected to the fixed bracket in a semi-fixed mode;
the control and power module comprises a servo motor and a control circuit, and controls the calibration plate to move to a designated position in the longitudinal direction, the transverse direction and the vertical direction.
2. The automatic calibration device for the ADAS lidar for vehicle offline detection of claim 1, wherein the left portal frame and the right portal frame are made of aluminum profile materials.
3. The automatic calibration device for the ADAS lidar for vehicle drop-off detection of claim 1, wherein the left portal frame and the right portal frame have a longitudinal length of 2 meters, a height of 3 meters, and a lateral distance of 4 meters.
4. The automatic calibration device for the ADAS laser radar for vehicle offline detection according to claim 3, wherein the fixed support is driven by the control and power module and can realize transverse and vertical movement, wherein the transverse movement range is 0-1 m, the movement precision is 1 mm, the vertical movement range is 0-1 m, the movement precision is 1 mm, and the repeated positioning precision is not more than +/-1 mm.
5. The automatic calibration device for the ADAS laser radar for vehicle offline detection according to claim 1, wherein the fixed support is an L-shaped support, the transverse part of the fixed support is 20 cm long, and the fixed support is wrapped by black foam cotton.
6. The automatic calibration device for the ADAS lidar for vehicle offline detection according to claim 1, wherein the fixing support and the calibration plate are connected by a ball hinge, the ball hinge is free to rotate in a release state, and the ball hinge is fixed and cannot rotate in a locking state.
7. An automatic calibration method for an ADAS laser radar for vehicle offline detection, which uses the automatic calibration device for an ADAS laser radar for vehicle offline detection according to any one of claims 1 to 6, and is characterized by comprising the following steps:
s100, acquiring vehicle parameters and target parameters, and reading the vehicle parameters and the target parameters;
s200, collecting laser radar point cloud data, and statically collecting a frame of original laser radar point cloud data;
s300, preprocessing point cloud data, and preprocessing the original laser radar point cloud data;
s400, performing coarse registration of point cloud, namely performing coarse registration on the preprocessed point cloud data and the preprocessed target point cloud data;
s500, performing fine point cloud registration, namely performing fine registration on the roughly registered point cloud data and the roughly registered target point cloud data;
s600, storing a calibration result, and writing calibration parameters obtained after fine registration into a configuration file in the vehicle domain controller.
8. The automatic calibration method for the ADAS laser radar for vehicle offline detection according to claim 7, wherein the vehicle parameters comprise vehicle coordinate system definition, vehicle length, width and height information, vehicle coordinate system origin-to-ground distance and vehicle coordinate system origin-to-head distance; the target parameters are four vertex space three-dimensional coordinate sets Pt_set of all calibration plates under a vehicle coordinate system.
9. The automatic calibration method for ADAS lidar for vehicle offline detection according to claim 7 or 8, wherein the step S400 comprises:
s410, clustering point cloud data, namely clustering point clouds representing each calibration plate in the preprocessed point cloud data PC_pre and the target point cloud data PC_dst by using a DBSCAN algorithm;
s420, calculating three-dimensional coordinates, and calculating the three-dimensional coordinates of the central point of each calibration plate in the preprocessed point cloud data PC_pre and the target point cloud data PC_dst to respectively obtain a point cloud data clustering point set CPts_pre and a target point cloud data clustering point set CPts_dst;
s430, calculating a coarse registration matrix, and calculating a registration result of the point cloud data clustering point set CPts_pre and the target point cloud data clustering point set CPts_dst by using a Super4PCS algorithm to obtain a 4x4 coarse registration matrix M0=super 4PCS (CPts_pre, CPts_dst);
s440, calculating rough registration point cloud data, multiplying and converting the preprocessed point cloud data PC_pre and the rough registration matrix M0 to obtain rough registration point cloud data PC_sup=PC_pre.M0 T 。
10. The automatic calibration method for ADAS lidar for vehicle offline detection of claim 9, wherein the step S600 comprises:
s510, calculating a fine registration matrix, and calculating a registration result of the coarse registration point cloud data PC_sup and the target point cloud data PC_dst by using an Iterative Closest Point (ICP) algorithm to obtain a 4x4 fine registration matrix M1=ICP (PC_sup, PC_dst);
and S520, calculating an optimal registration matrix, and combining the coarse registration matrix M0 and the fine registration matrix M1 to obtain an optimal registration matrix M=M1.M0, namely, a laser radar external parameter calibration result.
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