Static carrier laser radar calibration method based on total station

Abstract

The invention provides a static carrier laser radar calibration method based on a total station. A laser radar installed on a static carrier is used for completing one-frame original point cloud collection on a multi-plane scene, a point cloud segmentation and threshold constraint method is used for rapidly extracting multiple plane point cloud data, and meanwhile a total station is used for collecting all plane discrete points to obtain corresponding plane point cloud data. And calculating plane parameters acquired by the laser radar and the total station based on a plane fitting method, and fitting planes of the laser radar and the total station by using least square constraint, so that a rotation translation matrix between a laser radar coordinate system and a total station coordinate system can be calculated. Then measuring the fixed points of the static carrier by using a total station to obtain the coordinates of the fixed points under a total station system, and calculating a rotation translation matrix of a carrier coordinate system and a total station coordinate system according to the coordinate parameters of the fixed points during carrier design. And finally, the rotation and translation matrix between the laser radar and the total station coordinate system and the rotation and translation matrix between the carrier and the total station coordinate system time are synthesized, and the installation calibration parameters of the laser radar and the static carrier can be calculated.

Description

Technical Field

[0001] This invention belongs to the field of sensor parameter calibration, specifically relating to a static carrier lidar calibration method based on a total station. Background Technology

[0002] LiDAR (LiDAR) is a high-precision measurement sensor with advantages such as a large field of view and high accuracy, and it is currently widely used in autonomous driving and intelligent robotics. LiDAR uses the time-of-flight method to measure distance and a servo mechanism to measure the beam angle, thus achieving target point cloud measurement in LiDAR coordinate system. In practical applications, LiDAR is mounted on a moving platform, and obtaining the calibration relationship between the LiDAR coordinate system and the platform coordinate system is a pressing problem that needs to be solved.

[0003] Current mainstream methods are calibration methods based on motion information or calibration methods based on the transfer of other sensors. These methods are cumbersome and difficult to operate. To address this issue, this study uses a total station as the transfer reference, enabling the calibration of the lidar sensor and the stationary carrier's calibration parameters even when the carrier is stationary. Summary of the Invention

[0004] The objective of this invention is to propose a static carrier lidar calibration method based on a total station. A lidar mounted on a static carrier acquires a single frame of raw point cloud data for a multi-planar scene. Multiple planar point cloud data are quickly extracted using point cloud segmentation and threshold constraints. Simultaneously, a total station acquires corresponding planar point cloud data for discrete points on each plane. Planar parameters acquired by the lidar and total station are calculated using a plane fitting method. Least square constraints are used to fit the lidar and total station planes, allowing for the calculation of the rotation and translation matrix between the lidar and total station coordinate systems. Then, the total station measures fixed points on the static carrier to obtain their coordinates in the total station system. Based on the coordinate parameters of the fixed points during carrier design, the rotation and translation matrix between the carrier and total station coordinate systems can be calculated. Finally, by combining the rotation and translation matrix between the lidar and total station coordinate systems and the rotation and translation matrix between the carrier and total station coordinate systems over time, the installation and calibration parameters of the lidar and the static carrier can be calculated.

[0005] The technical solution of the present invention: A static carrier lidar calibration method based on a total station includes the following steps: Step 1: Set up the lidar calibration environment. Fix the lidar on the carrier platform and place multiple (no less than 3) non-parallel planar calibration plates in the lidar's field of view. Set up the total station next to the carrier platform. Step 2: Activate the lidar, acquire a complete frame of the original point cloud, and convert the point cloud coordinates from the polar coordinate system [r, α, θ] to the right-handed Cartesian coordinate system [x, y, z]. Step 3: Preprocess the lidar point cloud data. First, ground noise needs to be filtered out. Then, the DBSCAN method (density-based clustering algorithm) is used to extract the planar point cloud segmentation. The RANSAC strategy (random sample consensus strategy) is used to calculate the planar parameters of each calibration board. Step 4: Use a total station to collect data from no less than 10 points for each calibration plate, and use the RANSAC strategy to calculate the planar parameters of each calibration plate; Step 5: Based on the lidar plane parameters obtained in Step 3, calculate the distance from the point cloud data points in the lidar system to the corresponding plane in the total station system; based on the total station plane parameters obtained in Step 4, calculate the distance from the data acquisition points in the total station system to the corresponding plane in the lidar system. Combine the cross-calculation results and establish a nonlinear optimization model based on the least squares principle, where the optimized parameters are the rotation and translation matrices of the plane parameters calculated using the two methods. That is, the transformation relationship between the lidar coordinate system and the total station coordinate system; Step 6: Use a total station to measure the fixed points on the carrier platform, obtaining the coordinates of multiple (no less than 3) fixed points on the carrier platform. Combine the coordinate information of these fixed points on the carrier platform with the least squares constraint calculation to obtain the transformation relationship between the total station coordinate system and the carrier coordinate system. ; Step 7: Combine the rotation and translation matrices between the lidar and total station coordinate systems. Rotation and translation matrix of the carrier and total station coordinate system over time The installation and calibration parameters of the lidar and the carrier platform were calculated.

[0006] In one possible embodiment, in step 1, the lidar and the carrier must be fixedly installed. Once the calibration environment is set up, the carrier, lidar, total station, and calibration board cannot be moved throughout the entire process to reduce calibration errors caused by external interference. In one possible embodiment, in step 1, the selected calibration plates are 1m×1m in size and there are no fewer than 3 of them. The calibration planes should be evenly and without obstruction distributed in the field of view of the lidar and the total station at different distances and angles. The distance between each calibration plate is generally greater than 1m.

[0007] If the calibration planes obstruct each other, each plane can be measured separately, and the calibration plane parameters can be extracted by combining the results of multiple measurements.

[0008] In one possible embodiment, in step 2, the original point cloud data of the lidar is in polar coordinates [r, α, θ], where r = ct / 2 represents the distance to the target point measured by the time-of-flight method, α and θ represent the vertical and horizontal angles of the laser beam, respectively; c represents the speed of light, and t represents time; the coordinate values ​​are converted from the polar coordinate system to the right-handed Cartesian coordinate system, and the conversion process is as follows: .

[0009] In one possible embodiment, in step 3, the lidar completes the acquisition of a frame of raw point cloud data of the calibration environment, which mainly includes multiple planar calibration board point clouds and ground point clouds; for the ground point cloud data, the RANSAC method is used in combination with the constraint that the ground plane normal vector is approximately [0,0,1] to quickly fit the ground plane parameters and filter out the ground point cloud from the raw point cloud, thereby reducing the impact on the extraction of calibration plane point clouds.

[0010] In one possible embodiment, in step 3, the DBSCAN density clustering method is used to segment the point cloud of the planar calibration board and extract the point cloud data of each calibration plane. The original point cloud of the lidar at the nth plane and the i-th point can be represented as [x ln i ,y ln i ,z ln i [; Set neighborhood parameters according to the actual calibration environment: distance threshold (eps) and point count threshold (pts); The distance threshold is determined based on the distance between calibration boards, and eps can be set to 1m; The point count threshold is determined based on the angular resolution of the lidar, the working distance, and the size of the calibration board. Assuming the working distance is 100m, and the horizontal and vertical angular resolutions of the lidar are both 0.1°, the point cloud interval of the lidar at 100m can be calculated to be 0.175m. The number of point clouds that can be collected on a 1m×1m calibration board is approximately 25. Considering the angular deviation between the calibration board and the lidar based on measurement noise, pts is set to 10.

[0011] In one possible embodiment, in step 3, the RANSAC method is used to extract the planar parameters of each calibration board acquired by the lidar, and the nth planar parameter can be expressed as [a ln ,b ln ,c ln ,d ln ].

[0012] In one possible embodiment, in step 4, a total station is used to collect data on each calibration plane. The original point cloud of the lidar for the nth plane and the i-th point can be represented as [x tn i ,ytn i ,z tn i ], and the RANSAC method is used to extract the plane parameters of each calibration plate collected by the total station. The nth plane parameter can be expressed as [a tn ,b tn ,c tn ,d tn ].

[0013] In one possible embodiment, in step 5, the formula for calculating the distance from any point to the plane is: dis = |ax + by + cz + d|, where [a,b,c,d] are the plane parameters and [x,y,z] are the coordinates of any point; Assume the transformation relationship between the lidar coordinate system and the total station coordinate system is as follows: The lidar point cloud is then transformed to the corresponding total station coordinate system according to the above transformation relationship. The point cloud is represented as follows: Transform the total station point cloud to the corresponding lidar coordinate system according to the above transformation relationship. The point cloud is then represented as follows: ; First, calculate the straight-line distances from the k original points on the nth plane of the lidar to the nth fitted plane of the total station, and then establish a sum-of-squares function based on the point weights: ; Calculate the straight-line distance from the u-th original point on the m-th plane of the total station to the m-th fitted plane of the total station, and establish a sum-of-squares function based on the point weights: ; Then, by combining the above formulas, a weighted normalized objective function for the distances to all planes (num calibration planes in total) is constructed: ; Finally, an iterative algorithm is used to find the optimal solution of the objective function. This involves finding a feasible direction from a given initial point using the feasible direction method. Moving along this direction reduces the objective function value. When the designed number of iterations is reached or the tolerance of the objective function value is met, the optimized result of the objective function is considered to have been calculated. This result is expressed as the transformation relationship between the LiDAR coordinate system and the total station coordinate system. .

[0014] In one possible embodiment, in step 6, the coordinates of a set of fixed points in the total station system are P. t The coordinate system in the carrier coordinate system is P. b The relationship between the two is as follows: The transformation relationship between the total station coordinate system and the carrier coordinate system can be directly calculated using the least squares method. .

[0015] In one possible embodiment, in step 7, after performing a coordinate transformation, the transformation relationship between the lidar coordinate system and the carrier coordinate system is as follows: .

[0016] The advantages of this invention are: 1. Using a high-precision total station as the calibration benchmark enables the lidar to achieve a high calibration effect; 2. The static environment is easy to set up and does not depend on the motion state of external carriers or sensor sensing results, making it easy to operate, test and maintain; 3. The method is universal and can be applied to different types of lidar sensors and carrier platforms.

[0017] This invention proposes using a total station as the calibration benchmark for lidar sensors. The calibration environment is easy to set up, and the installation and calibration parameters of the static carrier and lidar can be quickly and efficiently realized, with high real-time performance and high reliability. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A schematic diagram of the lidar calibration environment; Figure 2 Functional architecture diagram for lidar calibration; Figure 3 This is a flowchart illustrating the conversion process between the lidar system and the total station system. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] The features and illustrative embodiments of various aspects of the present invention will now be described in detail. Numerous specific details are set forth in the following detailed description to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention may be practiced without requiring some of these specific details. The following description of embodiments is merely intended to provide a better understanding of the invention by illustrating examples of the invention. The invention is by no means limited to any specific setups and methods set forth below, but covers any improvements, substitutions, and modifications to structures, methods, and devices without departing from the spirit of the invention. Well-known structures and techniques are not shown in the drawings and the following description to avoid unnecessarily obscuring the invention.

[0022] In the description of this invention, it should be noted that the directions or positional relationships indicated by terms such as "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer" are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing and simplifying the invention, and should not be construed as limiting the invention. Furthermore, the use of ordinal numbers (e.g., "first and second," etc.) is for distinguishing objects and is not limited to this order, and should not be construed as indicating or implying relative importance.

[0023] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly, encompassing both direct connection and indirect connection via an intermediate medium. Those skilled in the art can understand the specific meaning of these terms in this invention based on the specific circumstances.

[0024] It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other, and the various embodiments can be referenced and cited in each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0025] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0026] Step 1: Set up the lidar calibration environment. Fix the lidar on the carrier platform and place multiple (no less than 3) non-parallel planar calibration plates in the lidar's field of view. Set up the total station next to the carrier platform. Step 2: Activate the lidar, acquire a complete frame of the original point cloud, and convert the point cloud coordinates from the polar coordinate system [r, α, θ] to the right-handed Cartesian coordinate system [x, y, z]. Step 3: Preprocess the lidar point cloud data. First, ground noise needs to be filtered out. Then, the DBSCAN method (density-based clustering algorithm) is used to extract the planar point cloud segmentation. The RANSAC strategy (random sample consensus strategy) is used to calculate the planar parameters of each calibration board. Step 4: Use a total station to collect data from no less than 10 points for each calibration plate, and use the RANSAC strategy to calculate the planar parameters of each calibration plate; Step 5: Based on the lidar plane parameters obtained in Step 3, calculate the distance from the point cloud data points in the lidar system to the corresponding plane in the total station system; based on the total station plane parameters obtained in Step 4, calculate the distance from the data acquisition points in the total station system to the corresponding plane in the lidar system. Combine the cross-calculation results and establish a nonlinear optimization model based on the least squares principle, where the optimized parameters are the rotation and translation matrices of the plane parameters calculated using the two methods. That is, the transformation relationship between the lidar coordinate system and the total station coordinate system; Step 6: Use a total station to measure the fixed points on the carrier platform, obtaining the coordinates of multiple (no less than 3) fixed points on the carrier platform. Combine the coordinate information of these fixed points on the carrier platform with the least squares constraint calculation to obtain the transformation relationship between the total station coordinate system and the carrier coordinate system. ; Step 7: Combine the rotation and translation matrices between the lidar and total station coordinate systems. Rotation and translation matrix of the carrier and total station coordinate system over time The installation and calibration parameters of the lidar and the carrier platform were calculated.

[0027] In one possible embodiment, in step 1, the lidar and the carrier must be fixedly installed. Once the calibration environment is set up, the carrier, lidar, total station, and calibration board cannot be moved throughout the entire process to reduce calibration errors caused by external interference. In one possible embodiment, in step 1, the selected calibration plates are 1m×1m in size and there are no fewer than 3 of them. The calibration planes should be evenly and without obstruction distributed in the field of view of the lidar and the total station at different distances and angles. The distance between each calibration plate is generally greater than 1m.

[0028] If the calibration planes obstruct each other, each plane can be measured separately, and the calibration plane parameters can be extracted by combining the results of multiple measurements.

[0029] In one possible embodiment, in step 2, the original point cloud data of the lidar is in polar coordinates [r, α, θ], where r = ct / 2 represents the distance to the target point measured by the time-of-flight method, α and θ represent the vertical and horizontal angles of the laser beam, respectively; c represents the speed of light, and t represents time; the coordinate values ​​are converted from the polar coordinate system to the right-handed Cartesian coordinate system, and the conversion process is as follows: .

[0030] In one possible embodiment, in step 3, the lidar completes the acquisition of a frame of raw point cloud data of the calibration environment, which mainly includes multiple planar calibration board point clouds and ground point clouds; for the ground point cloud data, the RANSAC method is used in combination with the constraint that the ground plane normal vector is approximately [0,0,1] to quickly fit the ground plane parameters and filter out the ground point cloud from the raw point cloud, thereby reducing the impact on the extraction of calibration plane point clouds.

[0031] In one possible embodiment, in step 3, the DBSCAN density clustering method is used to segment the point cloud of the planar calibration board and extract the point cloud data of each calibration plane. The original point cloud of the lidar at the nth plane and the i-th point can be represented as [x ln i ,y ln i ,z ln i [; Set neighborhood parameters according to the actual calibration environment: distance threshold (eps) and point count threshold (pts); The distance threshold is determined based on the distance between calibration boards, and eps can be set to 1m; The point count threshold is determined based on the angular resolution of the lidar, the working distance, and the size of the calibration board. Assuming the working distance is 100m, and the horizontal and vertical angular resolutions of the lidar are both 0.1°, the point cloud interval of the lidar at 100m can be calculated to be 0.175m. The number of point clouds that can be collected on a 1m×1m calibration board is approximately 25. Considering the angular deviation between the calibration board and the lidar based on measurement noise, pts is set to 10.

[0032] In one possible embodiment, in step 3, the RANSAC method is used to extract the planar parameters of each calibration board acquired by the lidar, and the nth planar parameter can be expressed as [a ln ,b ln ,c ln ,d ln ].

[0033] In one possible embodiment, in step 4, a total station is used to collect data on each calibration plane. The original point cloud of the lidar for the nth plane and the i-th point can be represented as [x tn i ,ytn i ,z tn i ], and the RANSAC method is used to extract the plane parameters of each calibration plate collected by the total station. The nth plane parameter can be expressed as [a tn ,b tn ,c tn ,d tn ].

[0034] In one possible embodiment, in step 5, the formula for calculating the distance from any point to the plane is: dis = |ax + by + cz + d|, where [a,b,c,d] are the plane parameters and [x,y,z] are the coordinates of any point; Assume the transformation relationship between the lidar coordinate system and the total station coordinate system is as follows: The lidar point cloud is then transformed to the corresponding total station coordinate system according to the above transformation relationship. The point cloud is represented as follows: Transform the total station point cloud to the corresponding lidar coordinate system according to the above transformation relationship. The point cloud is then represented as follows: ; First, calculate the straight-line distances from the k original points on the nth plane of the lidar to the nth fitted plane of the total station, and then establish a sum-of-squares function based on the point weights: ; Calculate the straight-line distance from the u-th original point on the m-th plane of the total station to the m-th fitted plane of the total station, and establish a sum-of-squares function based on the point weights: ; Then, by combining the above formulas, a weighted normalized objective function for the distances to all planes (num calibration planes in total) is constructed: ; Finally, an iterative algorithm is used to find the optimal solution of the objective function. This involves finding a feasible direction from a given initial point using the feasible direction method. Moving along this direction reduces the objective function value. When the designed number of iterations is reached or the tolerance of the objective function value is met, the optimized result of the objective function is considered to have been calculated. This result is expressed as the transformation relationship between the LiDAR coordinate system and the total station coordinate system. .

[0035] In one possible embodiment, in step 6, the coordinates of a set of fixed points in the total station system are P. t The coordinate system in the carrier coordinate system is P. b The relationship between the two is as follows: The transformation relationship between the total station coordinate system and the carrier coordinate system can be directly calculated using the least squares method. .

[0036] In one possible embodiment, in step 7, after performing a coordinate transformation, the transformation relationship between the lidar coordinate system and the carrier coordinate system is as follows: .

[0037] The above detailed embodiments are a description of the present invention. It should not be considered that the specific embodiments of the present invention are limited to these descriptions. For those skilled in the art, several simple deductions and substitutions can be made without departing from the concept of the present invention, and all of these should be considered to fall within the protection scope of the present invention.

Claims

1. A method for calibrating a static carrier lidar based on a total station, characterized in that, Includes the following steps: Step 1: Set up the lidar calibration environment. Fix the lidar on the carrier platform, place multiple non-parallel planar calibration plates in the lidar's field of view, and set up a total station next to the carrier. Step 2: Activate the lidar, acquire a complete frame of the original point cloud, and convert the point cloud coordinates from the polar coordinate system [r, α, θ] to the right-handed Cartesian coordinate system [x, y, z]. Step 3: Preprocess the lidar point cloud data. First, ground noise needs to be filtered out. Then, the DBSCAN method is used to extract the planar point cloud segmentation. The RANSAC strategy is used to calculate the planar parameters of each calibration board. Step 4: Use a total station to collect data from no less than 10 points for each calibration plate, and use the RANSAC strategy to calculate the planar parameters of each calibration plate; Step 5: Based on the lidar plane coordinates [x] obtained in the previous steps l ,y l ,z l ] and total station plane coordinates [x t ,y t ,z t By combining the distance from the point to the surface calculated by the intersection of planar parameters, a nonlinear optimization model is established based on the least squares principle. The optimized parameters are rotation and translation matrices of the planar parameters obtained by two methods. That is, the transformation relationship between the lidar coordinate system and the total station coordinate system; Step 6: Use a total station to measure the fixed points of the carrier and obtain the coordinates of multiple (no less than 3) fixed points. Combine these coordinates with the coordinate information of the fixed points during the carrier design, and calculate the transformation relationship between the total station coordinate system and the carrier coordinate system using least squares constraints. ; Step 7: Combine the rotation and translation matrices between the lidar and total station coordinate systems. Rotation and translation matrix of the carrier and total station coordinate system over time This allows us to calculate the installation and calibration parameters of the lidar and the stationary carrier.

2. The static carrier lidar calibration method based on a total station according to claim 1, characterized in that, In step 1, the lidar and the carrier must be fixedly installed. Once the calibration environment is set up, the carrier, lidar, total station, and calibration board must not be moved throughout the entire process to reduce calibration errors caused by external interference.

3. The static carrier lidar calibration method based on a total station according to claim 1, characterized in that, In step 1, the selected calibration plates are 1m × 1m in size, and there are no fewer than three. The calibration planes should be evenly distributed and non-obstructive in the fields of view of the lidar and the total station at different distances and angles. The distance between each calibration plate is generally greater than 1m. If the calibration planes obstruct each other, each plane can be measured separately, and the calibration plane parameters can be extracted by combining the results of multiple measurements.

4. The static carrier lidar calibration method based on a total station according to claim 1, characterized in that, In step 2, the raw point cloud data of the lidar is in polar coordinates [r, α, θ], where r = ct / 2 represents the distance to the target point measured by the time-of-flight method, and α and θ represent the vertical and horizontal angles of the laser beam, respectively. The coordinate values ​​are then converted from polar coordinates to a right-handed Cartesian coordinate system as follows: 。 5. The method for calibrating a static carrier lidar based on a total station according to claim 1, characterized in that, In step 3, the lidar completes the acquisition of one frame of raw point cloud data of the calibration environment, which mainly includes multiple planar calibration board point clouds and ground point clouds. For the ground point cloud data, the RANSAC method is used in conjunction with the constraint that the ground plane normal vector is approximately [0,0,1] to quickly fit the ground plane parameters and filter out the ground point cloud from the raw point cloud, thereby reducing the impact on the extraction of calibration plane point clouds.

6. The method for calibrating a static carrier lidar based on a total station according to claim 1, characterized in that, In step 3, the DBSCAN density clustering method is used to segment the point cloud of the calibration plane and extract the point cloud data of each calibration plane. The original point cloud of the lidar at the nth plane and the i-th point can be represented as [x ln i ,y ln i ,z ln i Set the neighborhood parameters according to the actual calibration environment: distance threshold (eps) and point count threshold (pts). The distance threshold is determined based on the distance between calibration boards, and eps can be set to 1m. The point count threshold is determined based on the lidar's angular resolution, working distance, and calibration board size. Assuming a working distance of 100m, and both the horizontal and vertical angular resolutions of the lidar are 0.1°, the point cloud interval of the lidar at 100m can be calculated to be 0.175m. The number of point clouds that can be collected on a 1m×1m calibration board is approximately 25. Considering the angular deviation between the calibration board and the lidar and the measurement noise, pts is set to 10.

7. The static carrier lidar calibration method based on a total station according to claim 1, characterized in that, In step 3, the RANSAC method is used to extract the planar parameters of each calibration board acquired by the lidar. The nth planar parameter can be expressed as [a ln ,b ln ,c ln ,d ln ].

8. The method for calibrating a static carrier lidar based on a total station according to claim 1, characterized in that, In step 4, a total station is used to collect data on each calibration plane. The original point cloud of the lidar for the nth plane and the i-th point can be represented as [x tn i ,y tn i ,z tn i ], and the RANSAC method is used to extract the plane parameters of each calibration plate collected by the total station. The nth plane parameter can be expressed as [a tn ,b tn ,c tn ,d tn ].

9. The method for calibrating a static carrier lidar based on a total station according to claim 1, characterized in that, In step 5, the formula for calculating the distance from any point to the plane is: dis = |ax + by + cz + d|, where [a, b, c, d] are plane parameters, and [x, y, z] are the coordinates of any point. Assume the transformation relationship between the lidar coordinate system and the total station coordinate system is as follows: The lidar point cloud is then transformed to the corresponding total station coordinate system according to the above transformation relationship. The point cloud is represented as follows: ; Transform the total station point cloud to the corresponding lidar coordinate system according to the above transformation relationship. The point cloud is represented as follows: First, calculate the straight-line distances from the k original points on the nth plane of the lidar to the nth fitted plane of the total station, and then establish a sum-of-squares function based on the point weights: Calculate the straight-line distance from the u-th original point on the m-th plane of the total station to the m-th fitted plane of the total station, and establish a sum-of-squares function based on the point weights: Then, by combining the above formulas, a weighted normalized objective function for the distances to all planes (num calibration planes in total) is constructed: Finally, a nonlinear optimization method was used to calculate the transformation relationship between the radar coordinate system and the total station coordinate system. .

10. A static carrier lidar calibration method based on a total station according to claim 1, characterized in that, In step 6, it is assumed that the transformation relationship between the total station coordinate system and the carrier coordinate system is as follows: The coordinates of a set of fixed points in the total station system are P. t The coordinate system in the carrier coordinate system is P. b The relationship between the two is as follows: The transformation relationship between the total station coordinate system and the vehicle coordinate system can be calculated using the least squares method. .

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