Multi-angle joint external parameter calibration method and system for camera and radar under rotation condition

Abstract

This invention discloses a method and system for multi-angle joint extrinsic parameter calibration of a camera and radar under rotation conditions, comprising: acquiring target images of the camera at horizontal 0 degrees and horizontal 180 degrees, and the corresponding target point clouds at the two horizontal angles; performing coordinate transformation on the target point clouds at the two horizontal angles so that the target point clouds are projected onto the target images at the corresponding horizontal angles, with the projected point cloud points coinciding with the image pixels, thereby obtaining the coordinate transformation matrix of the camera and radar at the two horizontal angles, i.e., the calibration extrinsic parameter matrix; acquiring the translation and rotation matrices of the camera relative to the horizontal angle after rotating by a certain angle, and obtaining the extrinsic parameter matrix at any rotation angle based on the calibration extrinsic parameter matrix, translation matrix, and rotation matrix. This achieves multi-angle joint calibration of the camera and radar.

Description

Technical Field

[0001] This invention relates to the field of camera calibration technology, and in particular to a method and system for multi-angle joint external parameter calibration of cameras and radars under rotation conditions. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Currently, the technology of using standardized high-resolution CCD industrial cameras to record images of defects is widely used, for example in the field of detecting defects on the surface of highway tunnel linings. However, high-precision cameras have a relatively small field of view, and multiple cameras are needed to capture comprehensive lining information. Multiple cameras lead to high equipment costs and prevent lightweight designs, and require road closures, causing inconvenience and limiting their application. Secondly, in existing joint calibration methods, the camera and radar can only be rigidly connected, and their positions are singular and fixed, resulting in only one set of calibration values. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a method and system for joint extrinsic parameter calibration of a camera and radar under rotation conditions. By controlling the camera's rotation angle, the relative positions of the camera and radar are changed. The extrinsic parameter matrix of the camera's horizontal angle is extended to obtain the extrinsic parameter matrix under any rotation angle, reducing the workload of extrinsic parameter calibration, lowering the calibration difficulty, and realizing joint calibration of the camera and radar at multiple angles.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for multi-angle joint extrinsic parameter calibration of a camera and radar under rotation conditions, comprising:

[0007] The camera and radar intrinsic parameters are calibrated in advance, and the target deployment method is determined based on the calibrated camera intrinsic parameters.

[0008] Acquire target images from the camera at 0 degrees and 180 degrees horizontally, as well as target point clouds collected by radar at the two horizontal angles.

[0009] The target point clouds at two horizontal angles are transformed into coordinates so that the target point clouds are projected onto the target images at the corresponding horizontal angles, and the projected points of the point clouds coincide with the image pixels. This yields the coordinate transformation matrix between the camera and the radar at the two horizontal angles, which is the calibration extrinsic parameter matrix.

[0010] After the camera rotates by a certain angle, obtain the translation and rotation matrices relative to the horizontal angle. Based on the calibration extrinsic matrix, translation matrix, and rotation matrix, obtain the extrinsic matrix at any rotation angle.

[0011] As an alternative implementation, the targets are placed as follows: Let the camera's horizontal field of view height be h1*h2, and the depth of field range of the camera's focal length f be j1~j2. Then, the targets are evenly and alternately distributed, with a placement height range of h1~h2, a distance from the camera range of (f+j1)~(f+j2), and a target point interval of... l is the horizontal length of the camera's field of view, and n is the number of targets.

[0012] As an alternative implementation, the camera is rotated by a turntable with a controllable rotation angle. The turntable is rigidly connected to the radar, and the positions of the turntable and the radar remain unchanged, so as to change the relative position of the camera and the radar.

[0013] As an alternative implementation, the process of coordinate transformation of the target point cloud includes: transforming the target point cloud from the lidar coordinate system to the camera coordinate system, then transforming it from the camera coordinate system to the reference normalized plane coordinate system, and projecting it onto the pixel plane of the target image so that the point cloud projection points and the image pixels coincide.

[0014] As an alternative implementation, for any rotation angle, a set of extrinsic parameters is obtained based on the calibration extrinsic parameter matrices of horizontal zero degree and horizontal 180 degree respectively. If the difference between the two sets of extrinsic parameter matrices does not exceed the threshold, the final extrinsic parameter matrix is ​​determined based on the relationship between the rotation angle and zero degree and 180 degree.

[0015] As an alternative implementation, if the rotation angle is greater than zero degrees and less than 90 degrees, the calibration extrinsic parameter matrix at horizontal zero degrees shall be used; if the rotation angle is greater than 90 degrees and less than 180 degrees, the calibration extrinsic parameter matrix at horizontal 180 degrees shall be used.

[0016] As an alternative implementation, the joint extrinsic parameter matrix of the camera and the radar is obtained based on the extrinsic parameter matrix of the camera at any rotation angle. Based on the joint extrinsic parameter matrix, the image acquired by the camera and the point cloud acquired by the radar are registered. The point cloud pose is assigned to the corresponding image pixel, and the pixel value of the image pixel is assigned to the corresponding point cloud point, thereby obtaining a point cloud image with color information and pose information.

[0017] Secondly, the present invention provides a multi-angle joint extrinsic parameter calibration system for a camera and radar under rotation conditions, comprising:

[0018] The internal parameter calibration module is configured to pre-calibrate the internal parameters of the camera and radar, and determine the target deployment method based on the calibrated camera internal parameters.

[0019] The data acquisition module is configured to acquire target images from the camera at horizontal zero degrees and horizontal 180 degrees, as well as target point clouds acquired by radar at the two horizontal angles.

[0020] The horizontal calibration module is configured to perform coordinate transformation on the target point clouds at two horizontal angles so that the target point clouds are projected onto the target images at the corresponding horizontal angles, and the point cloud projection points coincide with the image pixels, thereby obtaining the coordinate transformation matrix between the camera and the radar at the two horizontal angles, i.e., the calibration extrinsic parameter matrix.

[0021] The multi-angle joint calibration module is configured to obtain the translation and rotation matrices of the camera relative to the horizontal angle after rotating by a certain angle, and to obtain the extrinsic parameter matrix at any rotation angle based on the calibration extrinsic parameter matrix, translation matrix, and rotation matrix.

[0022] Thirdly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0023] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0025] This invention proposes a method and system for multi-angle joint extrinsic parameter calibration of cameras and radars under rotation conditions. It employs specially designed target points arranged according to camera parameters to form a target range for collecting calibration data. By combining multiple rotational angles with multi-device joint calibration, it solves the problem that traditional calibration methods only allow for rigid connection between the camera and radar, resulting in only one set of calibration values. Simultaneously, it uses two sets of data to obtain multiple sets of extrinsic parameter data from other angles, reducing the workload and difficulty of actual calibration. Furthermore, by utilizing reusable camera equipment at multiple angles, it expands the equipment's application range, reduces the number of devices used, and achieves lightweighting and lower R&D costs.

[0026] This invention proposes a method and system for multi-angle joint extrinsic parameter calibration of cameras and radar under rotation conditions. A target with a crosshair grid and a circle center is used to improve pixel-level calibration accuracy. A tripod is used to control the target height and distance from the camera, enabling the acquisition of a sufficient number of feature points within the camera's field of view at different angles. The coordinate relationship between the camera and radar at horizontal angles, i.e., the calibration matrix, is obtained through numerical calculation and registration. Extended calculations are then used to obtain extrinsic parameter matrices for other arbitrary angles. The accuracy of the obtained point cloud and image data is verified through registration, while accurate pose information is obtained, laying the foundation for subsequent identification and localization.

[0027] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0028] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0029] Figure 1 This is a schematic diagram of the multi-angle joint extrinsic parameter calibration method for camera and radar under rotation conditions provided in Embodiment 1 of the present invention. Detailed Implementation

[0030] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0031] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0032] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0034] Example 1

[0035] This embodiment provides a method for multi-angle joint extrinsic parameter calibration of a camera and radar under rotation conditions, such as... Figure 1 As shown, it includes:

[0036] The camera and radar intrinsic parameters are calibrated in advance, and the target deployment method is determined based on the calibrated camera intrinsic parameters.

[0037] Acquire target images from the camera at 0 degrees and 180 degrees horizontally, as well as target point clouds collected by radar at the two horizontal angles.

[0038] The target point clouds at two horizontal angles are transformed into coordinates so that the target point clouds are projected onto the target images at the corresponding horizontal angles, and the projected points of the point clouds coincide with the image pixels. This yields the coordinate transformation matrix between the camera and the radar at the two horizontal angles, which is the calibration extrinsic parameter matrix.

[0039] After the camera rotates by a certain angle, obtain the translation and rotation matrices relative to the horizontal angle. Based on the calibration extrinsic matrix, translation matrix, and rotation matrix, obtain the extrinsic matrix at any rotation angle.

[0040] In this embodiment, the camera's intrinsic parameters are calibrated in advance using the checkerboard calibration method to determine the position of the camera's optical center.

[0041] Specifically, in this embodiment, an 11*6 checkerboard calibration board is used for calibration. The camera is a meters away from the checkerboard calibration board (where a is the camera's focal length). A 4-grid shooting method is used, with the checkerboard calibration board positioned in four directions within the field of view. The calibration board is rotated for shooting; that is, shots are taken in three directions: tilted forward by 10 degrees, horizontally, and tilted backward by 10 degrees in each direction. The camera rotates 10 degrees around the center point in each direction. A total of 54 shots are taken in one direction, and a total of 216 shots are taken in four directions.

[0042] The relationship between the obtained image and the pixel plane is constructed using the homography matrix. The homography matrix is ​​represented as H = K[r1 r2 t]; where H is the homography matrix; K is the intrinsic parameter matrix of the camera; r1 and r2 represent the rotation matrices between the two planes; and t represents the translation matrix.

[0043] By estimating the rotation and translation of the calibration plate plane relative to the camera plane at different positions in front of the camera lens, the homography matrix is ​​obtained, and the camera's intrinsic parameter matrix K is further obtained.

[0044] In this embodiment, a black and white cross grid is selected as the target, with a circular center. The material is KT board, which allows for more precise location of the center point of the black and white cross grid target. It is placed on an adjustable-height tripod. The target placement method is determined based on the calibrated camera parameters. The placement distance and arrangement method are calculated using parameters such as camera depth of field and field of view. Specifically:

[0045] Let the horizontal field of view of the camera be h1*h2, and the depth of field range of the focal length f be j1~j2. Then, the target placement principle is staggered distribution, with a placement height range of h1~h2, and a distance from the camera of (f+j1)~(f+j2), evenly staggered vertically and horizontally; the interval between target points is... (n≥4 and n∈Z), where l is the horizontal length of the camera's field of view and n is the number of targets.

[0046] After the target points are deployed, the camera is mounted on a turntable with controllable rotation angle. The turntable and radar are mounted on a rigid connecting frame, and the positions of the turntable and radar remain fixed. The connecting frame is mounted on the roof of the vehicle platform. This allows for precise control of the camera's rotation angle and the rigid connection between the turntable and radar. By controlling the rotation of the turntable, the relative positions of the camera and radar can be changed. The positions of the camera and radar are not mutually exclusive, which improves the utilization rate of the camera equipment and saves equipment costs.

[0047] This embodiment adopts a mobile data acquisition method. A total station is used to measure the actual coordinates of the target point. After the inertial navigation accuracy is calibrated on the vehicle platform, the target images are acquired by the camera at 0 degrees and 180 degrees horizontally. The point cloud data of the target point at the two horizontal angles are acquired by the radar after internal parameter calibration.

[0048] In this embodiment, during the camera-laser joint extrinsic parameter calibration process, the inertial navigation coordinate system is used as the reference normalized plane coordinate system, and image and point cloud feature points with obvious features, definite absolute coordinates, and time synchronization are selected.

[0049] Let the spatial position of the target point cloud in the lidar coordinate system be (XYZ). T The target point cloud's spatial position in the camera coordinate system is (X... C Y C Z C ) T The projection point of the target point cloud onto the target image is (uv 1). T .

[0050] The process of transforming the target point clouds at two horizontal angles includes:

[0051] Transform the target point cloud from the lidar coordinate system to the camera coordinate system:

[0052]

[0053] Then, the camera coordinate system is transformed to a normalized planar coordinate system and projected onto the pixel plane of the target image:

[0054]

[0055] In this way, the coordinate transformation matrix between the camera and the radar is obtained. The target point cloud is mapped onto the target image through the coordinate transformation matrix. When the point cloud projection point and the image pixel point coincide, that is, until the best effect is obtained, the calibration extrinsic parameter matrix under the horizontal angle is obtained.

[0056] In this embodiment, during the multi-angle extrinsic parameter expansion process, the extrinsic parameter matrix under other rotation angles is obtained by calculating the extrinsic parameter matrix of the horizontal angle, which reduces the workload of extrinsic parameter calibration, lowers the calibration difficulty, solves problems such as the lightweighting of tunnel detection equipment, and realizes the joint calibration of camera and radar at multiple angles.

[0057] In this embodiment, the turntable rotates the camera by α degrees. After rotation, the camera has translation and rotation matrices relative to both 0° and 180° horizontally. From this, the spatial coordinate relationships between the rotation α degrees and 0° and 180° horizontally can be obtained. Furthermore, since the calibration extrinsic parameters at 0° and 180° horizontally have already been determined, the extrinsic parameter matrix at any rotation angle can be obtained.

[0058]

[0059] Where B = (△X △Y △Z) T t0 = (α β γ) is the translation matrix of the camera coordinate origin in three directions, and t0 = (α β γ) is the rotation matrix in three directions.

[0060] In this embodiment, for any rotation angle, a set of extrinsic parameter matrices is obtained based on the horizontal zero degree and the horizontal 180 degree respectively. If the difference between the two sets of extrinsic parameter matrices does not exceed a threshold, the final extrinsic parameter matrix is ​​determined according to the relationship between the rotation angle and the zero degree and 180 degrees. Specifically, if the rotation angle is greater than zero degree and less than 90 degrees, the extrinsic parameter matrix based on the horizontal zero degree is used; if the rotation angle is greater than 90 degrees and less than 180 degrees, the extrinsic parameter matrix based on the horizontal 180 degrees is used; if the rotation angle is 90 degrees, the mean value or any set of extrinsic parameter matrices can be used; if the difference between the two sets of extrinsic parameter matrices exceeds the threshold, the calibration extrinsic parameter matrix is ​​recalculated.

[0061] In this embodiment, the joint extrinsic parameter matrix of the camera and the radar is obtained based on the extrinsic parameter matrix of the camera at any rotation angle. Based on the joint extrinsic parameter matrix, the image acquired by the camera and the point cloud acquired by the radar are registered. The point cloud pose is assigned to the corresponding image pixel, and the pixel value of the image pixel is assigned to the corresponding point cloud point, thereby obtaining a point cloud image with color information and pose information.

[0062] In this embodiment, the above method can be used for the detection and location of surface defects in highway tunnel linings. Specifically: based on the time information of the starting point of the laser point cloud, an image with the same time synchronization is found. Through the correspondence between the joint extrinsic parameter matrix of the camera and the laser point cloud, the image pixel corresponding to the laser point cloud point is calculated. Then, the image and the point cloud are registered, that is, the corresponding laser point cloud pose is assigned to the corresponding image pixel, and the pixel value of the image pixel is assigned to the corresponding laser point cloud point. Finally, a point cloud image with color information is obtained, and the image pixel obtains accurate pose information for subsequent identification and location of defects.

[0063] Example 2

[0064] This embodiment provides a multi-angle joint extrinsic parameter calibration system for cameras and radar under rotation conditions, including:

[0065] The internal parameter calibration module is configured to pre-calibrate the internal parameters of the camera and radar, and determine the target deployment method based on the calibrated camera internal parameters.

[0066] The data acquisition module is configured to acquire target images from the camera at horizontal zero degrees and horizontal 180 degrees, as well as target point clouds acquired by radar at the two horizontal angles.

[0067] The horizontal calibration module is configured to perform coordinate transformation on the target point clouds at two horizontal angles so that the target point clouds are projected onto the target images at the corresponding horizontal angles, and the point cloud projection points coincide with the image pixels, thereby obtaining the coordinate transformation matrix between the camera and the radar at the two horizontal angles, i.e., the calibration extrinsic parameter matrix.

[0068] The multi-angle joint calibration module is configured to obtain the translation and rotation matrices of the camera relative to the horizontal angle after rotating by a certain angle, and to obtain the extrinsic parameter matrix at any rotation angle based on the calibration extrinsic parameter matrix, translation matrix, and rotation matrix.

[0069] It should be noted that the above modules correspond to the steps described in Embodiment 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0070] In further embodiments, the following is also provided:

[0071] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0072] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0073] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0074] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0075] The method in Example 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0076] Those skilled in the art will recognize that the units, i.e., algorithm steps, of the various examples described in connection with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0077] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for camera and radar multi-angle joint extrinsic calibration under rotation conditions, characterized in that, The method comprises the following steps: Pre-calibrate the camera and the radar, and determine the placement method of the target according to the calibrated camera intrinsic parameters; Obtain the target images of the camera at horizontal zero and horizontal 180 degrees, and the corresponding target point clouds collected by the radar at the two horizontal angles; Perform coordinate conversion on the target point clouds at the two horizontal angles respectively, so that the target point clouds are projected into the target images at the corresponding horizontal angles, and the point cloud projection points and the image pixel points coincide, to obtain the coordinate conversion matrix of the camera and the radar at the two horizontal angles, i.e. the calibration extrinsic matrix; Obtain the translation matrix and the rotation matrix of the camera relative to the horizontal angle after the camera is rotated by a certain angle, and obtain the extrinsic matrix at any rotation angle according to the calibration extrinsic matrix, the translation matrix and the rotation matrix; The turntable drives the camera to rotate by α degrees, and after the camera is rotated, there are translation matrix and rotation matrix relative to horizontal zero and horizontal 180 degrees, the spatial coordinate relationship of rotating α degrees with horizontal zero and horizontal 180 degrees is obtained, and the extrinsic matrix at any rotation angle is obtained: wherein, is a translation matrix of the camera coordinate origin in three directions, is a rotation matrix in three directions, is the spatial position of the target point cloud in the camera coordinate system, and t represents the translation matrix. 2.The method of claim 1, wherein, The target is arranged in a manner that: the height of the horizontal field of view of the camera is h1*h2, the depth of field range of the focal length f of the camera is j1~j2, the targets are uniformly staggered, the arrangement height range is h1~h2, the distance range from the camera is (f+j1)~(f+j2), and the target point interval is , l is the length of the horizontal direction of the field of view of the camera, n is the number of targets. 3.The method of claim 1, wherein, The camera is rotated by the turntable with controllable rotation angle, the turntable is rigidly connected with the radar and the position of the turntable and the radar is unchanged, so as to realize the change of the relative position of the camera and the radar. 4.The method of claim 1, wherein, The process of coordinate conversion of the target point cloud includes: transforming the target point cloud from the laser radar coordinate system to the camera coordinate system, then transforming from the camera coordinate system to the reference normalized plane coordinate system, and projecting onto the pixel plane of the target image, so that the point cloud projection points and the image pixel points coincide. 5.The method of claim 1, wherein, For any rotation angle, a group of extrinsic matrices is obtained according to the calibration extrinsic matrices of horizontal zero and horizontal 180 degrees, if the difference between the two groups of extrinsic matrices does not exceed the threshold value, then according to the size relationship of the rotation angle with zero and 180 degrees, the final extrinsic matrix is determined. 6.The method of claim 5, wherein, If the rotation angle is greater than zero and less than 90 degrees, the calibration extrinsic matrix of horizontal zero is used as the reference; if the rotation angle is greater than 90 degrees and less than 180 degrees, the calibration extrinsic matrix of horizontal 180 degrees is used as the reference.

7. The method of claim 1, wherein, According to the extrinsic matrix of the camera at any rotation angle, the joint extrinsic matrix of the camera and the radar is obtained, according to the joint extrinsic matrix, the image collected by the camera and the point cloud collected by the radar are registered, the point cloud pose is assigned to the corresponding image pixel point, and the pixel value of the image pixel point is assigned to the corresponding point cloud point, so as to obtain the point cloud image with color information and pose information.

8. A camera and radar multi-angle joint extrinsic calibration system under rotation conditions, characterized in that, The method comprises the following steps: An intrinsic calibration module is configured to pre-calibrate the camera and the radar, and determine the placement method of the target according to the calibrated camera intrinsic parameters; A data acquisition module is configured to obtain the target images of the camera at horizontal zero and horizontal 180 degrees, and the corresponding target point clouds collected by the radar at the two horizontal angles; A horizontal calibration module is configured to perform coordinate conversion on the target point clouds at the two horizontal angles respectively, so that the target point clouds are projected into the target images at the corresponding horizontal angles, and the point cloud projection points and the image pixel points coincide, to obtain the coordinate conversion matrix of the camera and the radar at the two horizontal angles, i.e. the calibration extrinsic matrix; An extrinsic matrix acquisition module is configured to obtain the translation matrix and the rotation matrix of the camera relative to the horizontal angle after the camera is rotated by a certain angle, and obtain the extrinsic matrix at any rotation angle according to the calibration extrinsic matrix, the translation matrix and the rotation matrix; The multi-angle joint calibration module is configured to obtain a translation matrix and a rotation matrix of the camera after rotating by a certain angle relative to a horizontal angle, and obtain an external parameter matrix at any rotation angle according to the calibration external parameter matrix, the translation matrix and the rotation matrix; The turntable drives the camera to rotate by α degrees, and after the camera rotates, there are translation matrices and rotation matrices relative to horizontal zero degrees and horizontal 180 degrees, the spatial coordinate relationship of rotating by α degrees with horizontal zero degrees and horizontal 180 degrees is obtained, and the external parameter matrix at any rotation angle is obtained: wherein, is a translation matrix of the camera coordinate origin in three directions, is a rotation matrix in three directions, is the spatial position of the target point cloud in the camera coordinate system, and t represents the translation matrix.

9. An electronic device, comprising: The computer program product comprises a memory and a processor, and computer instructions stored in the memory and run on the processor, and when the computer instructions are run by the processor, the method in any one of claims 1-7 is completed.

10. A computer-readable storage medium, characterized in that, The computer program product is used for storing computer instructions, and when the computer instructions are executed by the processor, the method in any one of claims 1-7 is completed.

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