A calibration method and system for vehicle-mounted laser radar based on multiple robotic arms

Abstract

The present invention relates to a calibration method and system for a vehicle-mounted laser radar based on multiple robotic arms. The method comprises: M1. During the calibration process of a vehicle-mounted laser radar installed on the multiple robotic arms, data information on the motion trajectories of multiple sets of robotic arms, data information on the motion trajectories of laser radars, and data information on the positions of the laser radars are collected; M2. Based on the data information on the motion trajectories of the multiple sets of robotic arms and the data information on the motion trajectories of the laser radars, a cyclic shift fusion algorithm based on the Laplace distribution of trajectory points is used to fuse the motion trajectory points of the laser radars and the multiple sets of robotic arms, thereby obtaining data information on the motion trajectory points of the fused laser radars. The present invention can not only adapt to the needs of different sensor types and complex environments, thereby improving the flexibility and universality of the system, but also ensure that the relative positional relationship between sensors can be precisely controlled, thereby improving calibration accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of laser radar calibration, and in particular to a calibration method and system for a vehicle-mounted laser radar based on multiple robotic arms. Background Art

[0002] With the continuous development and popularization of autonomous driving technology, automotive-grade lidar has become a core component of self-driving cars. In actual production, every self-driving car requires radar calibration to ensure its accuracy and stability. However, traditional calibration methods require individual calibration for each vehicle, which is time-consuming, labor-intensive, and costly.

[0003] Existing radar calibration is based on the fact that the vehicle needs to be completely off the production line and the high-voltage power is completed before the radar calibration work is allowed. This greatly increases the calibration time and cannot be done directly on the same vehicle model. The calibrated radar cannot be placed directly in a fixed position.

[0004] In the field of sensor calibration, especially in the joint calibration of multiple sensors (such as radar and camera), the existing technology has the following major problems:

[0005] 1. Limited calibration accuracy: Since the relative position relationship between sensors is difficult to accurately control, the calibration accuracy is limited, which affects the subsequent fusion and utilization of sensor data.

[0006] 2. High system complexity: Existing calibration methods often require complex equipment and processes, such as the use of calibration boards and motion estimation algorithms, which increase the complexity and cost of the system.

[0007] 3. Lack of flexibility: Existing calibration methods are often targeted at specific sensor combinations and scenarios, lack sufficient flexibility, and are difficult to adapt to the needs of different sensor types and complex environments. Summary of the Invention

[0008] In view of the above problems, the present invention provides a calibration method and system for a vehicle-mounted lidar based on multiple robotic arms, which can not only adapt to the needs of different sensor types and complex environments, improve the flexibility and universality of the system, but also ensure that the relative position relationship between sensors can be precisely controlled, thereby improving the calibration accuracy.

[0009] In order to achieve the above-mentioned and other related purposes, the present invention provides the following technical solutions:

[0010] A calibration method for a vehicle-mounted laser radar based on a multi-manipulator system, the method comprising:

[0011] M1. During the calibration process of the vehicle-mounted lidar installed on multiple manipulators, data information on the motion trajectories of multiple manipulators, the motion trajectories of the lidars, and the position of the lidars are collected;

[0012] M2. Based on the data information of the motion trajectories of the multiple sets of manipulators and the data information of the motion trajectories of the lidar, a cyclic shift fusion algorithm based on the Labrash distribution of trajectory points is used to fuse the motion trajectory points of the lidar and the multiple sets of manipulators to obtain the fused data information of the motion trajectory points of the lidar;

[0013] M3. Based on the fused data information of the laser radar's motion trajectory points and the data information of the laser radar's position, a three-dimensional calibration space model of the laser radar is constructed, and the laser radar's calibration matrix is ​​characterized to obtain data information of the laser radar's calibration matrix;

[0014] M4. Based on the data information of the laser radar calibration matrix, establish the laser radar calibration function F, calculate the laser radar calibration result, and obtain the data information of the laser radar calibration result.

[0015] Furthermore, the method further comprises:

[0016] M5. Based on the data information of the laser radar calibration results, establish the laser radar verification function Q,

[0017]

[0018] Among them, x is the data information of the laser radar calibration result, α1, α2 and α3 are the bias factors of the laser radar, the laser radar calibration result is evaluated, and the data information of the laser radar calibration result evaluation value is output.

[0019] Furthermore, based on the data information of the evaluation value of the calibration result of the laser radar, a preset threshold is set. If the evaluation value of the calibration result of the laser radar is less than the preset threshold, the requirements are met and the calibration is completed. If the evaluation value of the calibration result of the laser radar is greater than the threshold, the requirements are not met and the process returns to step M2.

[0020] Furthermore, in step M2, the cyclic shift fusion algorithm based on the Lablasian distribution of trajectory points is used to fuse the motion trajectory points of the laser radar and the multiple groups of robotic arms, including:

[0021] M21. Based on the data information of the motion trajectories of the multiple sets of robotic arms, establish the Lablas distribution function W of the multiple sets of robotic arms,

[0022]

[0023] Wherein, y is the data information of the motion trajectories of the multiple groups of robotic arms, β1, β2, and β3 are the scale parameters of the motion trajectories of the multiple groups of robotic arms, and the Labradorian distribution of the motion trajectories of the multiple groups of robotic arms is characterized to obtain the data information of the Labradorian distribution of the motion trajectories of the multiple groups of robotic arms;

[0024] M22. Based on the data information of the laser radar's motion trajectory, establish the laser radar's Labrash distribution function R,

[0025]

[0026] Wherein, z is the data information of the laser radar's motion trajectory, δ1, δ2, and δ3 are the scale parameters of the laser radar's motion trajectory, and the Labradorian distribution of the laser radar's motion trajectory is characterized to obtain the data information of the Labradorian distribution of the laser radar's motion trajectory;

[0027] M23. Based on the Labras distribution data information of the laser radar's motion trajectory and the Labras distribution data information of the multiple sets of robotic arms' motion trajectories, a cyclic shift fusion function U of the trajectory points is established.

[0028]

[0029] Among them, r1 is the data information of the Labrador distribution of the motion trajectory of the laser radar, r2 is the data information of the Labrador distribution of the motion trajectories of multiple groups of robotic arms, γ1, γ2 and γ3 are the fusion factors of the trajectory points. The motion trajectory points of the laser radar and multiple groups of robotic arms are fused to obtain the data information of the motion trajectory points of the fused laser radar.

[0030] Furthermore, the constraints of the fusion factors γ1, γ2 and γ3 of the trajectory points are:

[0031] Furthermore, the constraint function f1 of the scale parameters δ1, δ2 and δ3 of the laser radar's motion trajectory is,

[0032]

[0033] The constraint function f2 of the scale parameters β1, β2 and β3 of the motion trajectories of the multiple groups of robotic arms is,

[0034]

[0035] Among them, the value range of the constraint function f1 is (0,1), and the value range of the constraint function f2 is (2,4).

[0036] Furthermore, in step M3, constructing a three-dimensional calibration space model of the laser radar and characterizing the calibration matrix of the laser radar includes:

[0037] M31. Based on the fused laser radar motion trajectory point data information and the laser radar position data information, construct the laser radar relative position sequence function P,

[0038] Among them, h1 is the data information of the motion trajectory point of the fused laser radar, h2 is the data information of the position of the laser radar, η1, η2 and η3 are the bias parameters of the laser radar, and the relative position sequence of the laser radar is calculated to obtain the data information of the relative position sequence of the laser radar;

[0039] M32. Input the data information of the relative position sequence of the laser radar into the three-dimensional calibration space model of the laser radar for training and learning, and determine the three-dimensional calibration space function S of the laser radar.

[0040]

[0041] Where q is the data information of the relative position sequence of the laser radar, λ1, λ2 and λ3 are the three-dimensional calibration factors of the laser radar, and the data information of the relative position sequence of the trained laser radar is obtained;

[0042] M33. Based on the data information of the relative position sequence of the trained laser radar, the data information of the motion trajectory points of the fused laser radar and the data information of the position of the laser radar are input to characterize the calibration matrix of the laser radar and obtain the data information of the calibration matrix of the laser radar.

[0043] Furthermore, the three-dimensional calibration factors λ1, λ2 and λ3 of the laser radar are,

[0044]

[0045] Among them, q is the data information of the relative position sequence of the lidar.

[0046] Furthermore, the calibration function F of the laser radar is,

[0047]

[0048] Among them, g is the data information of the laser radar calibration matrix, μ1, μ2 and μ3 are the penalty coefficients of the laser radar calibration results.

[0049] In order to achieve the above-mentioned objectives and other related objectives, the present invention also provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the calibration methods for a multi-robotic-arm vehicle-mounted laser radar.

[0050] The present invention has the following positive effects:

[0051] 1. The present invention fuses the motion trajectory points of the laser radar and multiple groups of robotic arms by adopting a cyclic shift fusion algorithm of trajectory points based on the Lablasian distribution, and combines it with the construction of a three-dimensional calibration space model of the laser radar to characterize the calibration matrix of the laser radar. This not only ensures that the relative position relationship between sensors can be precisely controlled, thereby improving the calibration accuracy, but also simplifies the calibration equipment and process, reduces dependence on the calibration board, and reduces system complexity and cost.

[0052] 2. The present invention establishes a calibration function F for the lidar, extrapolates the calibration results of the lidar, and obtains data information of the calibration results of the lidar. This not only adapts to the needs of different sensor types and complex environments, and improves the flexibility and universality of the system, but also accurate calibration helps to reduce the deviation and error between sensor data, thereby improving the accuracy and reliability of data fusion. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Schematic diagram of the method flow of the present invention;

[0054] Figure 2 Schematic diagram of the flow of the cyclic shift fusion algorithm of the trajectory points based on Labrador distribution of the present invention;

[0055] Figure 3 A schematic diagram of the process of constructing a three-dimensional calibration space model of a laser radar according to the present invention; DETAILED DESCRIPTION

[0056] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0057] Example 1: Figure 1 As shown, a calibration method for a vehicle-mounted laser radar based on a multi-manipulator arm, the method comprising:

[0058] M1. During the calibration process of the vehicle-mounted lidar installed on multiple manipulators, data information on the motion trajectories of multiple manipulators, the motion trajectories of the lidars, and the position of the lidars are collected;

[0059] M2. Based on the data information of the motion trajectories of the multiple sets of manipulators and the data information of the motion trajectories of the lidar, a cyclic shift fusion algorithm based on the Labrash distribution of trajectory points is used to fuse the motion trajectory points of the lidar and the multiple sets of manipulators to obtain the fused data information of the motion trajectory points of the lidar;

[0060] M3. Based on the fused data information of the laser radar's motion trajectory points and the data information of the laser radar's position, a three-dimensional calibration space model of the laser radar is constructed, and the laser radar's calibration matrix is ​​characterized to obtain data information of the laser radar's calibration matrix;

[0061] M4. Based on the data information of the laser radar calibration matrix, establish the laser radar calibration function F, calculate the laser radar calibration result, and obtain the data information of the laser radar calibration result.

[0062] In this embodiment, the method further includes:

[0063] M5. Based on the data information of the laser radar calibration results, establish the laser radar verification function Q,

[0064]

[0065] Among them, x is the data information of the laser radar calibration result, α1, α2 and α3 are the bias factors of the laser radar, the laser radar calibration result is evaluated, and the data information of the laser radar calibration result evaluation value is output.

[0066] In this embodiment, a preset threshold is set based on the data information of the calibration result evaluation value of the laser radar. If the calibration result evaluation value of the laser radar is less than the preset threshold, the requirements are met and the calibration is completed. If the calibration result evaluation value of the laser radar is greater than the threshold, the requirements are not met and the process returns to step M2.

[0067] In this embodiment, if Figure 2 As shown, in step M2, the cyclic shift fusion algorithm based on the trajectory points of the Lablas distribution is used to fuse the motion trajectory points of the laser radar and multiple groups of robotic arms, including:

[0068] M21. Based on the data information of the motion trajectories of the multiple sets of robotic arms, establish the Lablas distribution function W of the multiple sets of robotic arms,

[0069]

[0070] Wherein, y is the data information of the motion trajectories of the multiple groups of robotic arms, β1, β2, and β3 are the scale parameters of the motion trajectories of the multiple groups of robotic arms, and the Labradorian distribution of the motion trajectories of the multiple groups of robotic arms is characterized to obtain the data information of the Labradorian distribution of the motion trajectories of the multiple groups of robotic arms;

[0071] M22. Based on the data information of the laser radar's motion trajectory, establish the laser radar's Labrash distribution function R,

[0072]

[0073] Wherein, z is the data information of the laser radar's motion trajectory, δ1, δ2, and δ3 are the scale parameters of the laser radar's motion trajectory, and the Labradorian distribution of the laser radar's motion trajectory is characterized to obtain the data information of the Labradorian distribution of the laser radar's motion trajectory;

[0074] M23. Based on the Labras distribution data information of the laser radar's motion trajectory and the Labras distribution data information of the multiple sets of robotic arms' motion trajectories, a cyclic shift fusion function U of the trajectory points is established.

[0075]

[0076] Among them, r1 is the data information of the Labrador distribution of the motion trajectory of the laser radar, r2 is the data information of the Labrador distribution of the motion trajectories of multiple groups of robotic arms, γ1, γ2 and γ3 are the fusion factors of the trajectory points. The motion trajectory points of the laser radar and multiple groups of robotic arms are fused to obtain the data information of the motion trajectory points of the fused laser radar.

[0077] In this embodiment, the constraints of the fusion factors γ1, γ2 and γ3 of the trajectory points are:

[0078]

[0079] In this embodiment, the constraint function f1 of the scale parameters δ1, δ2 and δ3 of the motion trajectory of the laser radar is,

[0080]

[0081] The constraint function f2 of the scale parameters β1, β2 and β3 of the motion trajectories of the multiple groups of robotic arms is,

[0082]

[0083] Among them, the value range of the constraint function f1 is (0,1), and the value range of the constraint function f2 is (2,4).

[0084] Example 2: Based on the vehicle-mounted laser radar calibration method based on multiple robotic arms in Example 1, the present invention is further illustrated and described below.

[0085] like Figure 1 As shown, a calibration method for a vehicle-mounted laser radar based on a multi-manipulator arm, the method comprising:

[0086] M1. During the calibration process of the vehicle-mounted lidar installed on multiple manipulators, data information on the motion trajectories of multiple manipulators, the motion trajectories of the lidars, and the position of the lidars are collected;

[0087] M2. Based on the data information of the motion trajectories of the multiple sets of manipulators and the data information of the motion trajectories of the lidar, a cyclic shift fusion algorithm based on the Labrash distribution of trajectory points is used to fuse the motion trajectory points of the lidar and the multiple sets of manipulators to obtain the fused data information of the motion trajectory points of the lidar;

[0088] M3. Based on the fused data information of the laser radar's motion trajectory points and the data information of the laser radar's position, a three-dimensional calibration space model of the laser radar is constructed, and the laser radar's calibration matrix is ​​characterized to obtain data information of the laser radar's calibration matrix;

[0089] M4. Based on the data information of the laser radar calibration matrix, establish the laser radar calibration function F, calculate the laser radar calibration result, and obtain the data information of the laser radar calibration result.

[0090] In this embodiment, if Figure 3 As shown, in step M3, the construction of the three-dimensional calibration space model of the laser radar and the characterization of the calibration matrix of the laser radar include:

[0091] M31. Based on the fused laser radar motion trajectory point data information and the laser radar position data information, construct the laser radar relative position sequence function P,

[0092] Among them, h1 is the data information of the motion trajectory point of the fused laser radar, h2 is the data information of the position of the laser radar, η1, η2 and η3 are the bias parameters of the laser radar, and the relative position sequence of the laser radar is calculated to obtain the data information of the relative position sequence of the laser radar;

[0093] M32. Input the data information of the relative position sequence of the laser radar into the three-dimensional calibration space model of the laser radar for training and learning, and determine the three-dimensional calibration space function S of the laser radar.

[0094]

[0095] Where q is the data information of the relative position sequence of the laser radar, λ1, λ2 and λ3 are the three-dimensional calibration factors of the laser radar, and the data information of the relative position sequence of the trained laser radar is obtained;

[0096] M33. Based on the data information of the relative position sequence of the trained laser radar, the data information of the motion trajectory points of the fused laser radar and the data information of the position of the laser radar are input to characterize the calibration matrix of the laser radar and obtain the data information of the calibration matrix of the laser radar.

[0097] In this embodiment, the three-dimensional calibration factors λ1, λ2 and λ3 of the laser radar are,

[0098] Among them, q is the data information of the relative position sequence of the lidar.

[0099] In this embodiment, the calibration function F of the laser radar is:

[0100]

[0101] Among them, g is the data information of the laser radar calibration matrix, μ1, μ2 and μ3 are the penalty coefficients of the laser radar calibration results.

[0102] In this embodiment, the present invention provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the calibration methods for a multi-robotic-arm vehicle-mounted laser radar.

[0103] In this embodiment, the present invention provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the calibration methods for a multi-robotic-arm vehicle-mounted laser radar.

[0104] Any reference to memory, storage, database or other media used in the embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0105] In summary, the present invention can not only adapt to the needs of different sensor types and complex environments, improving the flexibility and universality of the system, but also ensure that the relative position relationship between sensors can be precisely controlled, thereby improving calibration accuracy.

[0106] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A calibration method for a vehicle-mounted laser radar based on a multi-manipulator arm, characterized in that: The method comprises: M1. During the calibration process of the vehicle-mounted lidar installed on multiple manipulators, data information on the motion trajectories of multiple manipulators, the motion trajectories of the lidars, and the position of the lidars are collected; M2. Based on the data information of the motion trajectories of the multiple sets of manipulators and the data information of the motion trajectories of the lidar, a cyclic shift fusion algorithm based on the Labrash distribution of trajectory points is used to fuse the motion trajectory points of the lidar and the multiple sets of manipulators to obtain the fused data information of the motion trajectory points of the lidar; M3. Based on the fused data information of the laser radar's motion trajectory points and the data information of the laser radar's position, a three-dimensional calibration space model of the laser radar is constructed, and the laser radar's calibration matrix is ​​characterized to obtain data information of the laser radar's calibration matrix; M4. Based on the data information of the laser radar calibration matrix, establish the laser radar calibration function F, calculate the laser radar calibration result, and obtain the data information of the laser radar calibration result.

2. The calibration method of a multi-manipulator vehicle-mounted laser radar according to claim 1, characterized in that: The method further comprises: M5. Based on the data information of the laser radar calibration results, establish the laser radar verification function Q, Among them, x is the data information of the laser radar calibration result, α1, α2 and α3 are the bias factors of the laser radar, the laser radar calibration result is evaluated, and the data information of the laser radar calibration result evaluation value is output.

3. The calibration method for a multi-manipulator vehicle-mounted laser radar according to claim 2, characterized in that: Based on the data information of the calibration result evaluation value of the laser radar, a preset threshold is set. If the calibration result evaluation value of the laser radar is less than the preset threshold, the requirements are met and the calibration is completed. If the calibration result evaluation value of the laser radar is greater than the threshold, the requirements are not met and return to step M2.

4. The calibration method of a multi-manipulator vehicle-mounted laser radar according to claim 1, characterized in that: In step M2, the cyclic shift fusion algorithm based on the Labrash distribution of trajectory points is used to fuse the motion trajectory points of the laser radar and multiple groups of robotic arms, including: M21. Based on the data information of the motion trajectories of the multiple sets of robotic arms, establish the Lablas distribution function W of the multiple sets of robotic arms, Wherein, y is the data information of the motion trajectories of the multiple groups of robotic arms, β1, β2, and β3 are the scale parameters of the motion trajectories of the multiple groups of robotic arms, and the Labradorian distribution of the motion trajectories of the multiple groups of robotic arms is characterized to obtain the data information of the Labradorian distribution of the motion trajectories of the multiple groups of robotic arms; M22. Based on the data information of the laser radar's motion trajectory, establish the laser radar's Labrash distribution function R, Wherein, z is the data information of the laser radar's motion trajectory, δ1, δ2, and δ3 are the scale parameters of the laser radar's motion trajectory, and the Labradorian distribution of the laser radar's motion trajectory is characterized to obtain the data information of the Labradorian distribution of the laser radar's motion trajectory; M23. Based on the Labras distribution data information of the laser radar's motion trajectory and the Labras distribution data information of the multiple sets of robotic arms' motion trajectories, a cyclic shift fusion function U of the trajectory points is established. Among them, r1 is the data information of the Labrador distribution of the motion trajectory of the laser radar, r2 is the data information of the Labrador distribution of the motion trajectories of multiple groups of robotic arms, γ1, γ2 and γ3 are the fusion factors of the trajectory points. The motion trajectory points of the laser radar and multiple groups of robotic arms are fused to obtain the data information of the motion trajectory points of the fused laser radar.

5. The calibration method for a multi-manipulator vehicle-mounted laser radar according to claim 4, characterized in that: The constraints of the fusion factors γ1, γ2 and γ3 of the trajectory points are:

6. The calibration method for a multi-manipulator vehicle-mounted laser radar according to claim 4, characterized in that: The constraint function f1 of the scale parameters δ1, δ2 and δ3 of the laser radar's motion trajectory is, The constraint function f2 of the scale parameters β1, β2 and β3 of the motion trajectories of the multiple groups of robotic arms is, Among them, the value range of the constraint function f1 is (0,1), and the value range of the constraint function f2 is (2,4).

7. The calibration method of a multi-manipulator vehicle-mounted laser radar according to claim 1, characterized in that: In step M3, the construction of the three-dimensional calibration space model of the laser radar and the characterization of the laser radar calibration matrix include: M31. Based on the fused laser radar motion trajectory point data information and the laser radar position data information, construct the laser radar relative position sequence function P, Among them, h1 is the data information of the motion trajectory point of the fused laser radar, h2 is the data information of the position of the laser radar, η1, η2 and η3 are the bias parameters of the laser radar, and the relative position sequence of the laser radar is calculated to obtain the data information of the relative position sequence of the laser radar; M32. Input the data information of the relative position sequence of the laser radar into the three-dimensional calibration space model of the laser radar for training and learning, and determine the three-dimensional calibration space function S of the laser radar. Where q is the data information of the relative position sequence of the laser radar, λ1, λ2 and λ3 are the three-dimensional calibration factors of the laser radar, and the data information of the relative position sequence of the trained laser radar is obtained; M33. Based on the data information of the relative position sequence of the trained laser radar, the data information of the motion trajectory points of the fused laser radar and the data information of the position of the laser radar are input to characterize the calibration matrix of the laser radar and obtain the data information of the calibration matrix of the laser radar.

8. The calibration method for a multi-manipulator vehicle-mounted laser radar according to claim 7, characterized in that: The three-dimensional calibration factors λ1, λ2 and λ3 of the laser radar are, Among them, q is the data information of the relative position sequence of the lidar.

9. The calibration method for a vehicle-mounted laser radar based on multiple robotic arms according to claim 1, characterized in that: The calibration function F of the laser radar is, Among them, g is the data information of the laser radar calibration matrix, μ1, μ2 and μ3 are the penalty coefficients of the laser radar calibration results.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program that is programmed or configured to execute the calibration method for a vehicle-mounted laser radar based on a multi-robotic arm as described in any one of claims 1 to 9.

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