A vehicle-mounted surround-view camera calibration method, device and storage medium

Abstract

This application relates to a method, apparatus, and storage medium for calibrating a vehicle-mounted surround-view camera, comprising: acquiring a fisheye image captured by a fisheye camera; identifying calibration target feature points in the fisheye image and acquiring the pixel coordinates of the calibration target feature points; acquiring the world coordinates of the calibration target feature points and calculating the extrinsic parameters of the fisheye camera based on the pixel coordinates and world coordinates of the calibration target feature points; identifying correction target feature points in the fisheye image and acquiring the first pixel coordinates of the correction target feature points; acquiring the world coordinates of the correction target feature points and calculating the second pixel coordinates of the correction target feature points based on the world coordinates and extrinsic parameters of the correction target feature points; determining whether the fisheye camera has passed calibration based on the difference between the first pixel coordinates and the second pixel coordinates; through this application, automatic calibration error verification can be performed.

Description

Technical Field

[0001] This application relates to the field of vehicle perception technology, specifically to a method and apparatus for calibrating an in-vehicle surround-view camera, and a storage medium thereof. Background Technology

[0002] In autonomous driving, cameras are indispensable perception components. Among them, fisheye cameras, due to their large field of view (FOV), are commonly used in panoramic imaging and parking applications. When applying fisheye cameras, the calibration of their intrinsic and extrinsic parameters is an unavoidable and extremely important step. Accurate calibration results significantly improve vehicle surround-view stitching and parking space recognition. In the field of extrinsic parameter calibration, current methods use a single target pattern. The fisheye camera is aimed at the target to capture a fisheye image, and existing OpenCV open-source algorithms are used to extract feature points and calculate extrinsic parameters from this image. However, current calibration methods primarily focus on whether the calibration is successful, lacking automatic calibration error verification. Unsatisfactory calibration results cannot be automatically rejected, requiring manual inspection, which is time-consuming. Summary of the Invention

[0003] The purpose of this application is to provide a method and apparatus for calibrating a vehicle-mounted surround-view camera, as well as a computer-readable storage medium, for automatic calibration error verification.

[0004] To achieve the above objectives, this application provides a method for calibrating an in-vehicle surround-view camera, wherein the in-vehicle surround-view camera includes multiple fisheye cameras;

[0005] The method includes:

[0006] Acquire a fisheye image captured by the fisheye camera; wherein, a calibration target is arranged at a preset position in the field of view of the fisheye camera, and a correction target is distributed around the calibration target;

[0007] Identify the calibration target in the fisheye image, extract feature points from the calibration target to obtain the calibration target feature points, and obtain the pixel coordinates of the calibration target feature points;

[0008] Obtain the world coordinates of the calibration target feature points, and calculate the extrinsic parameters of the fisheye camera based on the pixel coordinates and world coordinates of the calibration target feature points;

[0009] Identify the correction target in the fisheye image, extract the correction target feature points, and obtain the first pixel coordinates of the correction target feature points;

[0010] Obtain the world coordinates of the correction target feature point, and calculate the second pixel coordinates of the correction target feature point based on the world coordinates of the correction target feature point and the extrinsic parameters;

[0011] The fisheye camera is calibrated based on the difference between the first pixel coordinate and the second pixel coordinate.

[0012] According to the above method, in some embodiments, calculating the second pixel coordinates of the correction target feature point based on the world coordinates of the correction target feature point and the extrinsic parameters includes:

[0013] The coordinates of the calibration target feature point in the camera coordinate system are calculated based on the world coordinates of the calibration target feature point and the extrinsic parameters.

[0014] The intrinsic parameters of the fisheye camera are obtained, and the coordinates of the correction target feature points in the camera coordinate system are converted into corresponding pixel coordinates according to the intrinsic parameters. The pixel coordinates are then subjected to distortion correction processing according to the intrinsic parameters to obtain the second pixel coordinates.

[0015] According to the above method, in some embodiments, the calibration target includes a square target and a circular target, and the calibration target feature points include multiple feature points, including the center point of the square target and the center point of the circular target;

[0016] The calibration target includes a chessboard target, and the feature points of the calibration target include multiple corner points of the chessboard target.

[0017] According to the above method, in some embodiments, determining whether the fisheye camera has passed calibration based on the difference between the first pixel coordinates and the second pixel coordinates includes:

[0018] The difference between the first pixel coordinate and the second pixel coordinate of each corner point is calculated to obtain multiple differences; the average of the multiple differences is calculated, and the fisheye camera is determined to pass the calibration based on the comparison result of the average value and a preset threshold.

[0019] According to the above method, in some embodiments, it further includes:

[0020] When all the fisheye cameras are calibrated, the fisheye images captured by the multiple fisheye cameras are subjected to distortion correction processing to obtain multiple fisheye images after distortion correction. The multiple fisheye images are then stitched together in a panoramic view to obtain a panoramic image.

[0021] This application embodiment also provides a vehicle-mounted surround view camera calibration device, wherein the vehicle-mounted surround view camera includes a plurality of fisheye cameras;

[0022] The device includes:

[0023] An image acquisition module is used to acquire fisheye images captured by the fisheye camera; wherein, a calibration target is arranged at a preset position in the field of view of the fisheye camera, and a correction target is distributed around the calibration target.

[0024] The first feature point extraction module is used to identify the calibration target in the fisheye image, extract feature points from the calibration target to obtain the calibration target feature points, and obtain the pixel coordinates of the calibration target feature points.

[0025] The extrinsic parameter calculation module is used to obtain the world coordinates of the calibration target feature points and calculate the extrinsic parameters of the fisheye camera based on the pixel coordinates and world coordinates of the calibration target feature points.

[0026] The second feature point extraction module is used to identify the correction target in the fisheye image, extract the correction target feature points, and obtain the first pixel coordinates of the correction target feature points.

[0027] The pixel coordinate calculation module is used to obtain the world coordinates of the correction target feature point, and calculate the second pixel coordinates of the correction target feature point based on the world coordinates of the correction target feature point and the extrinsic parameters;

[0028] The calibration result output module is used to determine whether the fisheye camera has passed calibration based on the difference between the first pixel coordinate and the second pixel coordinate.

[0029] According to the above apparatus, in some embodiments, the pixel coordinate calculation module is used for:

[0030] The coordinates of the calibration target feature point in the camera coordinate system are calculated based on the world coordinates of the calibration target feature point and the extrinsic parameters.

[0031] The intrinsic parameters of the fisheye camera are obtained, and the coordinates of the correction target feature points in the camera coordinate system are converted into corresponding pixel coordinates according to the intrinsic parameters. The pixel coordinates are then subjected to distortion correction processing according to the intrinsic parameters to obtain the second pixel coordinates.

[0032] According to the above-described apparatus, in some embodiments, the calibration target includes a grid target and a circular target, and the calibration target feature points include multiple feature points, including the center point of the grid target and the center point of the circular target.

[0033] The calibration target includes a chessboard target, and the feature points of the calibration target include multiple corner points of the chessboard target.

[0034] According to the above apparatus, in some embodiments, the calibration result output module is specifically used for:

[0035] The difference between the first pixel coordinate and the second pixel coordinate of each corner point is calculated to obtain multiple differences; the average of the multiple differences is calculated, and the fisheye camera is determined to pass the calibration based on the comparison result of the average value and a preset threshold.

[0036] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the vehicle surround-view camera calibration method described above.

[0037] This application provides a method and apparatus for calibrating a vehicle-mounted surround-view camera, as well as a computer-readable storage medium. It includes a calibration target and a correction target. During calibration, a fisheye image captured by the fisheye camera includes the calibration target and the correction target. Feature points of the calibration target in the fisheye image can be used to calibrate the extrinsic parameters of the fisheye camera, and feature points of the correction target in the fisheye image can be used to automatically verify calibration errors. Calibration results that do not meet the requirements can be automatically rejected without manual inspection, thus avoiding lengthy processing times. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings required in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart of a vehicle surround view camera calibration method according to one embodiment of this application.

[0040] Figure 2 This is a schematic diagram of the calibration target and the correction target in one embodiment of this application.

[0041] Figure 3 This is a schematic diagram of a vehicle-mounted surround-view camera calibration device according to one embodiment of this application. Detailed Implementation

[0042] The detailed description of the accompanying drawings is intended to illustrate some embodiments of this application and is not intended to represent only the forms in which this application can be implemented. It should be understood that the same or equivalent functions can be accomplished by different embodiments intended to be included within the spirit and scope of this application.

[0043] One embodiment of this application provides a calibration method for an in-vehicle surround-view camera, wherein the in-vehicle surround-view camera includes a plurality of fisheye cameras;

[0044] The method in this embodiment includes calibrating each fisheye camera separately, see reference. Figure 1 The calibration of each fisheye camera includes the following steps:

[0045] Step S100: Acquire a fisheye image captured by the fisheye camera. A calibration target is positioned at a preset location within the field of view of the fisheye camera, and correction targets are distributed around the calibration target.

[0046] Specifically, when calibrating the vehicle-mounted surround-view camera, the vehicle to be calibrated enters the calibration area. By moving the vehicle and the calibration target and correction target, the calibration target and correction target are positioned at a preset position in the field of view of the fisheye camera. When the fisheye camera is activated to capture images, it can capture images of the calibration target and correction target.

[0047] Before starting the fisheye camera to take pictures, it is necessary to use a surveying tool to measure the coordinate positions of the feature points of the calibration target and the feature points of the correction target in the world coordinate system. Each position is denoted as w(x,y,z). Here, it is assumed that all target positions are on the ground, so z=0, that is, the coordinates are w(x,y,0). Create a configuration file and store the coordinate positions of the feature points of the calibration target and the feature points of the correction target in the world coordinate system (i.e., the world coordinates mentioned in the following steps) in the configuration file. This configuration file is used for calculations in the following steps.

[0048] Step S200: Identify the calibration target in the fisheye image, extract feature points from the calibration target to obtain the calibration target feature points, and obtain the pixel coordinates of the calibration target feature points.

[0049] Specifically, when identifying the calibration target in the fisheye image, some preprocessing can be performed to improve the image clarity, such as using image enhancement algorithms to enhance the fisheye image.

[0050] For example, for a preprocessed fisheye image, the calibration target feature points can be extracted in the following way: the calibration target region is segmented from the fisheye image, the calibration target region is binarized, and then the calibration target feature points are extracted using a preset feature point extraction algorithm; the calibration target feature points can be one feature point or multiple feature points; it should be noted that the feature point extraction algorithm in step S200 is not limited to one type, such as SIFT algorithm, SURF algorithm, FAST algorithm, ORB algorithm, AKAZE algorithm, etc., and the specific algorithm can be considered according to the algorithm's time consumption, effect, and actual needs.

[0051] After obtaining the calibrated target feature points, the sub-pixel point finding algorithm can be used to extract sub-pixels and obtain the pixel coordinates of the calibrated target feature points. Specifically, for image feature-based algorithms, the sub-pixel point finding algorithm can improve the accuracy of matching and reduce the possibility of algorithm errors.

[0052] Step S300: Obtain the world coordinates of the calibration target feature points, and calculate the extrinsic parameters of the fisheye camera based on the pixel coordinates and world coordinates of the calibration target feature points.

[0053] Specifically, the world coordinates of the calibration target feature points are obtained from the configuration file, and a preset calibration algorithm is called. Based on the calibration algorithm, the extrinsic parameters of the fisheye camera can be calculated according to the pixel coordinates and world coordinates of the calibration target feature points. The extrinsic parameters refer to the position and attitude of the camera in space, that is, the rotation and translation of the camera coordinate system (also known as the camera coordinate system) relative to the world coordinate system. Therefore, the extrinsic parameters specifically include the rotation matrix R and the translation matrix T. The calibration algorithm is, for example, the solvePnP() algorithm. The solvePnP() algorithm is an OpenCV function that uses 3D–2D point pairs to solve the camera pose (rotation and translation) and the 3D coordinates of the target point in the camera coordinate system.

[0054] Step S400: Identify the correction target in the fisheye image, extract the correction target feature points, and obtain the first pixel coordinates of the correction target feature points.

[0055] For example, for a preprocessed fisheye image, the correction target feature points can be extracted in the following way: the correction target region is segmented from the fisheye image, the correction target region is binarized, and then the correction target feature points are obtained by using a preset feature point extraction algorithm; the correction target feature points can be one feature point or multiple feature points; it should be noted that the feature point extraction algorithm in step S400 is not limited to one type, such as SIFT algorithm, SURF algorithm, FAST algorithm, ORB algorithm, AKAZE algorithm, etc., and the specific algorithm can be considered according to the algorithm's time consumption, effect, and actual needs.

[0056] Step S500: Obtain the world coordinates of the correction target feature point, and calculate the second pixel coordinates of the correction target feature point based on the world coordinates of the correction target feature point and the extrinsic parameters.

[0057] Specifically, the corresponding coordinates of the correction target feature point in the camera coordinate system can be calculated based on the world coordinates of the correction target feature point and the extrinsic parameters. Furthermore, the second pixel coordinates can be obtained based on the corresponding coordinates and the known intrinsic parameters of the fisheye camera.

[0058] Step S600: Determine whether the fisheye camera has passed calibration based on the difference between the first pixel coordinates and the second pixel coordinates.

[0059] Specifically, the calibration error can be automatically checked based on the difference between the first pixel coordinates and the second pixel coordinates to determine whether the calibration has passed.

[0060] In summary, the method of this embodiment sets up a calibration target and a correction target. During calibration, the fisheye camera captures a fisheye image containing both the calibration target and the correction target. The feature points of the calibration target in the fisheye image can be used to calibrate the extrinsic parameters of the fisheye camera, and the feature points of the correction target in the fisheye image can be used to automatically verify the calibration error. Calibration results that do not meet the requirements can be automatically rejected without manual inspection, avoiding lengthy processing times. This embodiment combines calibration and correction targets, enabling an integrated process of calibration and calibration result verification, which greatly improves calibration quality while reducing the workload and time consumption of calibration result verification.

[0061] In some embodiments, step S500 specifically includes:

[0062] Step S501: Calculate the coordinates of the correction target feature point in the camera coordinate system based on the world coordinates of the correction target feature point and the extrinsic parameters;

[0063] For example, let the world coordinates of the feature point of the calibration target be w(X). w ,Y w Z w ), where Z w Let w be 0; let c(ui,vi,0) be the coordinates of w in the camera coordinate system.

[0064] Then we have:

[0065]

[0066] Where R is the rotation matrix of the extrinsic parameters and T is the translation matrix of the extrinsic parameters;

[0067] Step S502: Obtain the intrinsic parameters of the fisheye camera, convert the coordinates of the correction target feature points in the camera coordinate system into corresponding pixel coordinates according to the intrinsic parameters, and perform distortion correction processing on the pixel coordinates according to the intrinsic parameters to obtain the second pixel coordinates.

[0068] Specifically, the intrinsic parameters are known, including the focal length f, the pixel coordinates (u0, v0) of the image center point, and the distortion parameters; based on the intrinsic parameters and c(ui, vi, 0), the corresponding coordinates of c in the pixel coordinate system can be calculated, as follows:

[0069]

[0070] in, Let x be the focal length of the camera in the x-direction; Let be the focal length of the camera in the y-direction.

[0071] In some embodiments, the calibration target includes a grid target and a circular target, and the calibration target feature points include a plurality of feature points, including the center point of the grid target and the center point of the circular target.

[0072] The calibration target includes a chessboard target, and the feature points of the calibration target include multiple corner points of the chessboard target; specifically, the chessboard target includes multiple chessboard squares, and the corner points refer to the four corner points of each chessboard square.

[0073] For example, such as Figure 2 The image shown is a design example of a calibration target and a correction target in this embodiment. Figure 2 It includes multiple square targets and circular targets, with checkerboard targets arranged between the square targets and circular targets.

[0074] It should be noted that current calibration methods suffer from algorithmic defects due to the limited number of feature points on the calibration target, resulting in low calibration accuracy. Therefore, this embodiment proposes to combine square and circular targets as calibration targets to obtain feature points of different types of calibration targets, which is beneficial to improving calibration accuracy.

[0075] In some embodiments, determining whether the fisheye camera has passed calibration based on the difference between the first pixel coordinates and the second pixel coordinates includes:

[0076] The difference between the first pixel coordinate and the second pixel coordinate of each corner point is calculated to obtain multiple differences; the average of the multiple differences is calculated, and the fisheye camera is determined to pass the calibration based on the comparison result of the average value and a preset threshold.

[0077] Specifically, when extracting feature points, since the chessboard target has multiple corner points, each with corresponding first and second pixel coordinates, it is necessary to consider the pixel coordinate deviations of all corner points. The average value of all corner points is used to comprehensively consider all corner points, which can be expressed as the following formula:

[0078]

[0079] Where Err is the average of multiple differences, i represents the i-th corner point, n represents the total number of corner points, and u pi v is the x-coordinate of the first pixel of the i-th corner point. pi Let u be the ordinate of the first pixel coordinate of the i-th corner point. ji v is the x-coordinate of the second pixel of the i-th corner point. ji The ordinate of the second pixel coordinate of the i-th corner point;

[0080] In this embodiment, a preset threshold is set to control the calibration quality. If Err is less than the preset threshold, the calibration is considered successful and the qualified calibration result is saved for subsequent surround view stitching. If Err is greater than the preset threshold, the calibration is considered unsuccessful.

[0081] In some embodiments, it also includes:

[0082] Step S600: When all the fisheye cameras have passed calibration, the fisheye images captured by the multiple fisheye cameras are subjected to distortion correction processing to obtain multiple fisheye images after distortion correction, and the multiple fisheye images are stitched together in a panoramic view to obtain a panoramic image.

[0083] Specifically, generally speaking, a vehicle-mounted surround-view fisheye camera includes four fisheye cameras: front, rear, left, and right, which are used to capture environmental images from different directions of the vehicle. If a vehicle's fisheye camera fails to pass calibration, it needs to be recalibrated. When all of the vehicle's fisheye cameras pass calibration, the fisheye images captured by all the fisheye cameras are processed to remove distortion based on the distortion parameters in the external parameters, and then the surround-view images are stitched together to obtain a panoramic image.

[0084] Another embodiment of this application provides a vehicle surround view camera calibration device, which includes a module that can be used to perform the method steps of the above embodiments, wherein the vehicle surround view camera includes a plurality of fisheye cameras.

[0085] The device includes:

[0086] Image acquisition module 1 is used to acquire fisheye images captured by the fisheye camera. A calibration target is positioned at a preset location within the field of view of the fisheye camera, and correction targets are distributed around the calibration target.

[0087] The first feature point extraction module 2 is used to identify the calibration target in the fisheye image, extract feature points from the calibration target to obtain the calibration target feature points, and obtain the pixel coordinates of the calibration target feature points.

[0088] The extrinsic parameter calculation module 3 is used to obtain the world coordinates of the calibration target feature points and calculate the extrinsic parameters of the fisheye camera based on the pixel coordinates and world coordinates of the calibration target feature points.

[0089] The second feature point extraction module 4 is used to identify the correction target in the fisheye image, extract the correction target feature points, and obtain the first pixel coordinates of the correction target feature points.

[0090] The pixel coordinate calculation module 5 is used to obtain the world coordinates of the correction target feature point and calculate the second pixel coordinates of the correction target feature point based on the world coordinates of the correction target feature point and the external parameters.

[0091] The calibration result output module 6 is used to determine whether the fisheye camera has passed calibration based on the difference between the first pixel coordinate and the second pixel coordinate.

[0092] In some embodiments, the pixel coordinate calculation module 5 is specifically used for:

[0093] The coordinates of the calibration target feature point in the camera coordinate system are calculated based on the world coordinates of the calibration target feature point and the extrinsic parameters.

[0094] The intrinsic parameters of the fisheye camera are obtained, and the coordinates of the correction target feature points in the camera coordinate system are converted into corresponding pixel coordinates based on the intrinsic parameters. The pixel coordinates are then subjected to distortion correction processing to obtain the second pixel coordinates.

[0095] In some embodiments, the calibration target includes a grid target and a circular target, and the calibration target feature points include a plurality of feature points, including the center point of the grid target and the center point of the circular target.

[0096] The calibration target includes a chessboard target, and the feature points of the calibration target include multiple corner points of the chessboard target.

[0097] In some embodiments, the calibration result output module 6 is specifically used for:

[0098] The difference between the first and second pixel coordinates of each corner point is calculated to obtain multiple differences. The average of these multiple differences is then calculated, and the fisheye camera is used to determine whether it has passed calibration based on a comparison between the average and a preset threshold.

[0099] The vehicle surround-view camera calibration device described above is merely illustrative. The modules described as separate components may or may not be physically separate. The components of a module may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the vehicle surround-view camera calibration device solution in the embodiments.

[0100] It should be noted that the vehicle surround view camera calibration device of the above embodiments corresponds to the vehicle surround view camera calibration method of the above embodiments. Therefore, the parts of the vehicle surround view camera calibration device of the above embodiments that are not described in detail can be obtained by referring to the content of the vehicle surround view camera calibration method of the above embodiments. That is, the specific steps recorded in the vehicle surround view camera calibration method of the above embodiments can be understood as the functions that the vehicle surround view camera calibration device of the above embodiments can achieve, and will not be repeated here.

[0101] Furthermore, if the vehicle surround view camera calibration device of the above embodiments is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0102] Another embodiment of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the vehicle surround-view camera calibration method as described in the above embodiments.

[0103] Specifically, the computer-readable storage medium may include any entity or recording medium capable of carrying the computer program instructions, such as a USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media.

[0104] Another embodiment of this application provides an electronic device including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle surround view camera calibration method described in the above embodiments.

[0105] The electronic device may also include a bus connecting different components, including memory and processor. The memory may include a computer-readable medium in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The memory may also include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application. The electronic device may also communicate with one or more external devices (e.g., keyboard, pointing device, display, etc.), and with one or more devices that enable a user to interact with the electronic device, and / or with any device (e.g., a network interface card) that enables the electronic device to communicate with one or more other computing devices, such communication may be performed via an input / output (I / O) interface, and the electronic device may also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via a network adapter.

[0106] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technological improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A vehicle-mounted surround-view camera calibration method, characterized in that, The vehicle-mounted surround view camera includes multiple fisheye cameras; The method includes: Acquire a fisheye image captured by the fisheye camera; wherein, a calibration target is arranged at a preset position in the field of view of the fisheye camera, and a correction target is distributed around the calibration target; Identify the calibration target in the fisheye image, extract feature points from the calibration target to obtain the calibration target feature points, and obtain the pixel coordinates of the calibration target feature points; Obtain the world coordinates of the calibration target feature points, and calculate the extrinsic parameters of the fisheye camera based on the pixel coordinates and world coordinates of the calibration target feature points; Identify the correction target in the fisheye image, extract the correction target feature points, and obtain the first pixel coordinates of the correction target feature points; Obtain the world coordinates of the correction target feature point, and calculate the second pixel coordinates of the correction target feature point based on the world coordinates of the correction target feature point and the extrinsic parameters; The fisheye camera is calibrated based on the difference between the first pixel coordinate and the second pixel coordinate.

2. The vehicle-mounted surround-view camera calibration method according to claim 1, characterized in that, The step of calculating the second pixel coordinates of the correction target feature point based on the world coordinates of the correction target feature point and the extrinsic parameters includes: The coordinates of the calibration target feature point in the camera coordinate system are calculated based on the world coordinates of the calibration target feature point and the extrinsic parameters. The intrinsic parameters of the fisheye camera are obtained, and the coordinates of the correction target feature points in the camera coordinate system are converted into corresponding pixel coordinates according to the intrinsic parameters. The pixel coordinates are then subjected to distortion correction processing according to the intrinsic parameters to obtain the second pixel coordinates. 3.The vehicle surround view camera calibration method of claim 1, wherein, The calibration target includes a square target and a circular target, and the calibration target feature points include multiple feature points, including the center point of the square target and the center point of the circular target; The calibration target includes a chessboard target, and the feature points of the calibration target include multiple corner points of the chessboard target.

4. The vehicle surround view camera calibration method of claim 3, wherein, The step of determining whether the fisheye camera has passed calibration based on the difference between the first pixel coordinates and the second pixel coordinates includes: The difference between the first pixel coordinate and the second pixel coordinate of each corner point is calculated to obtain multiple differences; the average of the multiple differences is calculated, and the fisheye camera is determined to pass the calibration based on the comparison result of the average value and a preset threshold.

5. The calibration method for a vehicle-mounted surround-view camera according to any one of claims 1-4, characterized in that, The method includes: When all the fisheye cameras are calibrated, the fisheye images captured by the multiple fisheye cameras are subjected to distortion correction processing to obtain multiple fisheye images after distortion correction. The multiple fisheye images are then stitched together in a panoramic view to obtain a panoramic image.

6. A vehicle-mounted surround view camera calibration device, characterized in that, The vehicle-mounted surround view camera includes multiple fisheye cameras; The device includes: An image acquisition module is used to acquire fisheye images captured by the fisheye camera; wherein, a calibration target is arranged at a preset position in the field of view of the fisheye camera, and a correction target is distributed around the calibration target. The first feature point extraction module is used to identify the calibration target in the fisheye image, extract feature points from the calibration target to obtain the calibration target feature points, and obtain the pixel coordinates of the calibration target feature points. The extrinsic parameter calculation module is used to obtain the world coordinates of the calibration target feature points and calculate the extrinsic parameters of the fisheye camera based on the pixel coordinates and world coordinates of the calibration target feature points. The second feature point extraction module is used to identify the correction target in the fisheye image, extract the correction target feature points, and obtain the first pixel coordinates of the correction target feature points. The pixel coordinate calculation module is used to obtain the world coordinates of the correction target feature point, and calculate the second pixel coordinates of the correction target feature point based on the world coordinates of the correction target feature point and the extrinsic parameters; The calibration result output module is used to determine whether the fisheye camera has passed calibration based on the difference between the first pixel coordinate and the second pixel coordinate.

7. The vehicle-mounted surround-view camera calibration device according to claim 6, characterized in that, The pixel coordinate calculation module is specifically used for: The coordinates of the calibration target feature point in the camera coordinate system are calculated based on the world coordinates of the calibration target feature point and the extrinsic parameters. The intrinsic parameters of the fisheye camera are obtained, and the coordinates of the correction target feature points in the camera coordinate system are converted into corresponding pixel coordinates according to the intrinsic parameters. The pixel coordinates are then subjected to distortion correction processing according to the intrinsic parameters to obtain the second pixel coordinates.

8. The vehicle surround view camera calibration apparatus of claim 6, wherein, The calibration target includes a square target and a circular target, and the calibration target feature points include multiple feature points, including the center point of the square target and the center point of the circular target; The calibration target includes a chessboard target, and the feature points of the calibration target include multiple corner points of the chessboard target.

9. The vehicle surround view camera calibration apparatus of claim 8, wherein, The calibration result output module is specifically used for: The difference between the first pixel coordinate and the second pixel coordinate of each corner point is calculated to obtain multiple differences; the average of the multiple differences is calculated, and the fisheye camera is determined to pass the calibration based on the comparison result of the average value and a preset threshold.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the vehicle surround-view camera calibration method as described in any one of claims 1 to 5.

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