Camera calibration method and electronic device

Abstract

This invention discloses a camera calibration method and electronic device. The method includes: calibrating the camera to obtain camera calibration parameters; establishing a correspondence between pixels and latitude and longitude based on the camera calibration parameters; acquiring a ground truth vehicle data file and a calibration camera data file; displaying the ground truth vehicle trajectory generated from the ground truth vehicle data file and the calibration camera vehicle trajectory generated from the calibration camera data file; receiving user modifications to the recognition parameters, updating the recognition parameters, updating the center pixel of the recognition target based on the updated recognition parameters, generating and displaying the updated calibration camera vehicle trajectory based on the latitude and longitude corresponding to the updated center pixel of the recognition target; and outputting the updated recognition parameters and camera calibration parameters after the user finishes modifying the recognition parameters. This invention significantly improves efficiency and reduces human resource costs in multi-scenario, multi-calibration testing scenarios.

Description

Technical Field

[0001] This invention relates to the field of vehicle-related technologies, and in particular to a camera calibration method, electronic device, storage medium, and computer program product. Background Technology

[0002] With the development of the autonomous driving industry, the calibration of camera intrinsic and extrinsic parameters is a crucial foundation for subsequent detection and tracking in roadside perception algorithms based on autonomous driving. Camera calibration is fundamental to many tasks, and its accuracy and speed determine the upper limit of downstream solution fusion. Furthermore, with the increasingly rapid pace of algorithm iteration, how to achieve efficient, fast, and accurate camera calibration and rapid calibration testing is a key concern for technical personnel.

[0003] Camera calibration primarily aims to obtain the camera's internal parameters (such as focal length and distortion) and external parameters (such as the relative position and orientation between cameras). Current technology for camera calibration and accuracy testing involves first manually driving a real-world vehicle to obtain actual real-time kinematics (RTK) vehicle trajectory data, and then comparing this data with the vehicle trajectory data obtained after camera calibration. RTK is a high-precision positioning technology commonly used in surveying and navigation. RTK technology utilizes signals from multiple receivers (such as GPS receivers) and, through real-time processing and differential techniques, provides centimeter-level positioning accuracy.

[0004] For analyzing multiple deployments across intersections, road segments, and other scenarios, current technologies for camera calibration only allow adjustments to calibration parameters through third-party tools or code projects to view the calibrated results. They lack the ability to visualize or provide a more intuitive way to compare calibration results and adjust parameters. Furthermore, non-developers cannot participate in the entire calibration and testing process. Therefore, current technologies require significant time and manpower for camera calibration and testing. Summary of the Invention

[0005] Therefore, it is necessary to provide a camera calibration method, electronic device, storage medium, and computer program product to address the technical problems of unintuitive and inefficient camera calibration in existing technologies.

[0006] This invention provides a camera calibration method, comprising:

[0007] The camera is calibrated to obtain camera calibration parameters, and a correspondence between pixels and latitude and longitude is established based on the camera calibration parameters;

[0008] Acquire ground truth vehicle data file and calibration camera data file. The ground truth vehicle data file includes multiple ground truth vehicle data frames, and each ground truth vehicle data frame includes at least the latitude and longitude of the ground truth vehicle during operation. The calibration camera data file includes multiple camera data frames, and each camera data frame includes at least the latitude and longitude of the center pixel of the target identified by the camera from the ground truth vehicle.

[0009] Display the ground truth vehicle trajectory generated based on the ground truth vehicle data file, and the calibration camera vehicle trajectory generated based on the calibration camera data file;

[0010] The system obtains the user's modification of the recognition parameters, updates the recognition parameters, which are used to determine the center pixel of the target being recognized by the camera, updates the center pixel of the target based on the updated recognition parameters, and generates and displays the updated calibration camera vehicle trajectory based on the latitude and longitude corresponding to the center pixel of the target.

[0011] After the user finishes modifying the recognition parameters, the updated recognition parameters and camera calibration parameters are output.

[0012] Furthermore, the display of the ground truth vehicle trajectory generated based on the ground truth vehicle data file and the calibration camera vehicle trajectory generated based on the calibration camera data file includes:

[0013] The first layer of the display area shows the ground truth vehicle trajectory generated based on the ground truth vehicle data file, and the second layer of the display area shows the calibration camera vehicle trajectory generated based on the calibration camera data file.

[0014] Furthermore, the recognition parameters include vertical adjustment parameters and horizontal adjustment parameters, and updating the center pixel of the recognition target based on the updated recognition parameters includes:

[0015] Obtain the horizontal coordinates of the bottom midpoint pixel of the target bounding box, the vertical coordinates of the bottom midpoint pixel, the horizontal width of the target bounding box, and the vertical height of the target bounding box;

[0016] Based on the updated horizontal adjustment parameters and the horizontal coordinates of the bottom center pixel, calculate the horizontal proportion of the center pixel; based on the updated vertical adjustment parameters and the vertical coordinates of the bottom midpoint pixel, calculate the vertical proportion of the center pixel.

[0017] The horizontal coordinates of the center pixel of the target are calculated based on the horizontal ratio of the center pixel, the horizontal coordinates of the bottom midpoint pixel, and the horizontal width of the target box.

[0018] The vertical coordinates of the center pixel of the target are calculated based on the vertical proportion of the center pixel, the vertical coordinates of the bottom midpoint pixel, and the vertical height of the target box.

[0019] Furthermore, it also includes:

[0020] Based on the true vehicle data file and calibration camera data file, generate a position positioning accuracy trend chart; and / or

[0021] Based on the true vehicle data file and the calibration camera data file, a heading angle positioning accuracy trend chart is generated.

[0022] Furthermore, the step of generating a position positioning accuracy trend chart based on the true vehicle data file and the calibration camera data file includes:

[0023] The timestamp of each real vehicle data frame in the real vehicle data file is used as the base timestamp;

[0024] For each reference timestamp, the timestamp closest to the reference timestamp in the calibration camera data file is taken as the camera timestamp, the latitude and longitude corresponding to the camera timestamp in the calibration camera data file is taken as the camera latitude and longitude, and the latitude and longitude corresponding to the reference timestamp in the true vehicle data file is taken as the true vehicle latitude and longitude. The positional distance difference between the camera latitude and longitude and the true vehicle latitude and longitude is calculated.

[0025] A location positioning accuracy trend chart is generated based on the location distance difference corresponding to each of the aforementioned reference timestamps.

[0026] Furthermore, the step of generating a heading angle positioning accuracy trend chart based on the true vehicle data file and the calibration camera data file includes:

[0027] The timestamp of each real vehicle data frame in the real vehicle data file is used as the base timestamp;

[0028] For each reference timestamp, the timestamp closest to the reference timestamp in the calibration camera data file is taken as the camera timestamp, the heading angle corresponding to the camera timestamp in the calibration camera data file is taken as the camera heading angle, and the heading angle corresponding to the reference timestamp in the true vehicle data file is taken as the true vehicle heading angle. The heading angle difference between the camera heading angle and the true vehicle heading angle is calculated.

[0029] Based on the heading angle difference corresponding to each of the aforementioned reference timestamps, a heading angle positioning accuracy trend chart is generated.

[0030] Furthermore, the step of calibrating the camera to obtain camera calibration parameters, and establishing a correspondence between pixels and latitude and longitude based on the camera calibration parameters, includes:

[0031] Initial camera calibration parameters are generated based on checkerboard calibration, and these parameters are used to represent the correspondence between pixels and latitude and longitude.

[0032] Display images of the road surface taken by the camera, including one or more road markings;

[0033] The road markings are displayed in the road image based on pixels determined by the initial camera calibration parameters;

[0034] The system obtains the user's modifications to the camera calibration parameters, updates the camera calibration parameters, and displays the road markings in the road image with pixels determined based on the updated camera calibration parameters.

[0035] This invention provides an electronic device, comprising:

[0036] At least one processor; and,

[0037] A memory communicatively connected to at least one of the processors; wherein,

[0038] The memory stores instructions that can be executed by at least one of the processors to enable at least one of the processors to perform the camera calibration method as described above.

[0039] The present invention provides a storage medium that stores computer instructions, which, when executed by a computer, are used to perform all the steps of the camera calibration method as described above.

[0040] The present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the camera calibration method as described above.

[0041] This invention demonstrates ground truth vehicle trajectories generated from ground truth vehicle data files and calibration camera vehicle trajectories generated from calibration camera data files. By acquiring user modifications to recognition parameters, it generates and displays updated calibration camera vehicle trajectories, enabling visual adjustment of recognition parameters. This invention, through visualization, allows users to intuitively understand the changes in vehicle trajectory caused by changes in recognition parameters. This visualization approach enables more efficient and intuitive camera calibration. This invention significantly improves efficiency and reduces human resource costs in multi-scenario, multi-calibration testing scenarios. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating the workflow of a camera calibration method according to an embodiment of the present invention;

[0043] Figure 2 This is a flowchart illustrating the workflow of a camera calibration method according to another embodiment of the present invention;

[0044] Figure 3 This is a diagram showing the chessboard pattern of an example of the present invention within the camera's field of view.

[0045] Figure 4 This is a schematic diagram of feature points in an example of the present invention;

[0046] Figure 5 This is a schematic diagram illustrating the projection effect of an example of the present invention;

[0047] Figure 6 This is a schematic diagram of the calibration device tool interface as an example of the present invention;

[0048] Figure 7 A flowchart illustrating the workflow of a camera calibration method according to a preferred embodiment of the present invention;

[0049] Figure 8 This is a schematic diagram of the hardware structure of an electronic device according to the present invention.

[0050] Figure 9 This is a schematic diagram of the tool interface for calculating and adjusting intrinsic parameters, extrinsic parameters, and distortion parameters, as an example of the present invention. Detailed Implementation

[0051] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. Identical components are indicated by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "up," and "down" used in the following description refer to directions in the accompanying drawings, while the terms "inner" and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0052] like Figure 1 The diagram shown is a flowchart of a camera calibration method according to an embodiment of the present invention, including:

[0053] Step S101: Calibrate the camera to obtain camera calibration parameters, and establish the correspondence between pixels and latitude and longitude based on the camera calibration parameters;

[0054] Step S102: Obtain the ground truth vehicle data file and the calibration camera data file. The ground truth vehicle data file includes multiple ground truth vehicle data frames, and each ground truth vehicle data frame includes at least the latitude and longitude of the ground truth vehicle during operation. The calibration camera data file includes multiple camera data frames, and each camera data frame includes at least the latitude and longitude of the center pixel of the target identified by the camera based on the ground truth vehicle.

[0055] Step S103: Display the ground truth vehicle trajectory generated based on the ground truth vehicle data file, and the calibration camera vehicle trajectory generated based on the calibration camera data file;

[0056] Step S104: Obtain the user's modification of the recognition parameters, update the recognition parameters, the recognition parameters are used to determine the center pixel of the target being recognized by the camera, update the center pixel of the target being recognized based on the updated recognition parameters, and generate and display the updated calibration camera vehicle trajectory based on the latitude and longitude corresponding to the center pixel of the target being recognized.

[0057] Step S105: After the user finishes modifying the recognition parameters, output the updated recognition parameters and camera calibration parameters.

[0058] Specifically, the present invention can be applied to electronic devices with processing capabilities, such as computers.

[0059] Specifically, step S101 is first executed to calibrate the camera and obtain camera calibration parameters. Based on the camera calibration parameters, a correspondence between pixels and latitude and longitude is established.

[0060] Specifically, the existing checkerboard calibration method can be used to calibrate the camera and obtain camera calibration parameters. These parameters are used to establish the correspondence between pixels in the images captured by the camera and the actual latitude and longitude. Camera calibration parameters include, but are not limited to: intrinsic parameters, extrinsic parameters, and distortion parameters.

[0061] Then, step S102 is executed to obtain the ground truth vehicle data file and the calibration camera data file. The ground truth vehicle data file includes multiple ground truth vehicle data frames, and each ground truth vehicle data frame includes at least the latitude and longitude of the ground truth vehicle during operation. The calibration camera data file includes multiple camera data frames, and each camera data frame includes at least the latitude and longitude of the center pixel of the target identified by the camera from the ground truth vehicle.

[0062] Specifically, the ground truth vehicle data file is preferably a ground truth vehicle RTK data file. RTK is a technology used for high-precision positioning. An RTK receiver can receive GPS satellite information and calculate high-precision position information. First, an RTK receiver is installed on the vehicle, then connected to the vehicle, and the data generated by the RTK receiver is acquired. This data typically includes the vehicle's position, speed, direction, etc. Ground truth vehicle RTK data is obtained by manually driving the ground truth vehicle. The RTK data is then structured to obtain ground truth vehicle RTK structured data and saved to the ground truth vehicle RTK data file. The ground truth vehicle RTK structured data includes multiple ground truth vehicle data frames. Each ground truth vehicle data frame can include the timestamp, latitude and longitude, and heading angle information of the ground truth vehicle trajectory data. Simultaneously, the ground truth vehicle is photographed by a camera. Raw camera data is obtained from the camera. The raw camera data is structured to generate raw camera structured data and saved to the camera data file. The raw camera structured data includes multiple camera data frames. Each camera data frame can identify the latitude and longitude, timestamp, heading angle, category, and other geographical location information of the trajectory. Specifically, the camera identifies the ground truth vehicle as the target and determines its center pixel based on the identification parameters. The camera continuously captures multiple frames of images, and then, using existing trajectory recognition methods, based on the pixel-latitude-longitude correspondence established by the camera calibration parameters, the latitude and longitude corresponding to the center pixel of the target in each frame are used as the latitude and longitude of each camera data frame. At the same time, the timestamp of the shooting is recorded, and geographical location information such as heading angle and category is determined.

[0063] Initially, the recognition parameters use the default parameters, which include, but are not limited to: vertical recognition adjustment parameters and horizontal recognition adjustment parameters.

[0064] Specifically, the recognition parameters include vertical adjustment parameters and horizontal adjustment parameters, and determining the center pixel of the recognition target based on the recognition parameters includes:

[0065] Obtain the horizontal coordinates of the bottom midpoint pixel of the target bounding box, the vertical coordinates of the bottom midpoint pixel, the horizontal width of the target bounding box, and the vertical height of the target bounding box;

[0066] Calculate the horizontal proportion of the center pixel based on the horizontal adjustment parameters and the horizontal coordinates of the bottom center pixel; calculate the vertical proportion of the center pixel based on the vertical adjustment parameters and the vertical coordinates of the bottom midpoint pixel.

[0067] The horizontal coordinates of the center pixel of the target are calculated based on the horizontal ratio of the center pixel, the horizontal coordinates of the bottom midpoint pixel, and the horizontal width of the target box.

[0068] The vertical coordinates of the center pixel of the target are calculated based on the vertical proportion of the center pixel, the vertical coordinates of the bottom midpoint pixel, and the vertical height of the target box.

[0069] Specifically, yThresh, yRatio1, and yRatio2 are used as vertical adjustment parameters, and xThresh and xRatio1 are used as horizontal recognition adjustment parameters. The target is identified using a two-dimensional bounding box. Based on the width and height of the bounding box, the vertical position of the target's center pixel is adjusted using the vertical adjustment parameters, and the horizontal position of the target's center pixel is adjusted using the horizontal adjustment parameters. The vertical and horizontal recognition parameters together determine the pixel corresponding to the target's center.

[0070] As an example, yThresh is the vertical adjustment threshold, yRatio1 and yRatio2 are two different vertical adjustment ratios, xThresh is the horizontal adjustment threshold, and xRatio1 is the horizontal adjustment ratio.

[0071] Get the horizontal coordinates of the bottom midpoint pixel of the target bounding box (centerX), the vertical coordinates of the bottom midpoint pixel (centery), the horizontal width (objectWidth), and the vertical height (objectHeight) of the target bounding box;

[0072] Based on the horizontal adjustment parameters and the horizontal coordinates of the bottom center pixel, the horizontal proportion of the center pixel is calculated as follows:

[0073] Calculate w_ratio = (centerX / xThresh - 1) * xRatio1, where w_ratio is the horizontal ratio of the center pixel;

[0074] Based on the vertical adjustment parameters and the vertical coordinates of the bottom edge midpoint pixel, the vertical proportion of the center pixel is calculated as follows:

[0075] For the case where yThresh > 0:

[0076] If centerY > yThresh, then calculate h_ratio = (centerY / yThresh - 1) * yRatio1;

[0077] If centerY ≤ yThresh, then calculate h_ratio = (centerY / yThresh - 1) * yRatio2;

[0078] For the case where yThresh ≤ 0: calculate h_ratio = centerY / 720 * yRatio1;

[0079] Where h_ratio is the vertical ratio of the center pixel.

[0080] Finally, the horizontal coordinates of the center pixel of the target are calculated based on the horizontal proportion of the center pixel, the horizontal coordinates of the bottom midpoint pixel, and the horizontal width of the target bounding box. Specifically:

[0081] reflectX = centerX + w_ratio * objectWidth, where reflectX is the x-coordinate of the center pixel of the target being identified;

[0082] The vertical coordinates of the center pixel of the target are calculated based on the vertical proportion of the center pixel, the vertical coordinates of the bottom midpoint pixel, and the vertical height of the target bounding box. Specifically:

[0083] reflectY = centerY - h_ratio * objectHeight, where reflectY is the center pixel coordinate of the target being identified.

[0084] Then, use the calibration device tool to import the true vehicle RTK data file used for calibration as the true vehicle data file, and import the camera raw data file used for calibration as the calibration camera data file.

[0085] Then, step S103 is executed to display the true vehicle trajectory generated based on the true vehicle data file and the calibration camera vehicle trajectory generated based on the calibration camera data file.

[0086] Specifically, a calibration device is used in conjunction with a high-precision map to convert geographic location information into 2D pixels, thereby enabling a comparative display of vehicle trajectories.

[0087] The ground truth vehicle data file is preferably an RTK data file. Ground truth vehicle trajectories are generated from the ground truth vehicle data file using existing RTK trajectory generation methods. For example, in a high-definition map, the latitude and longitude coordinates of each frame of ground truth vehicle data in the ground truth vehicle data file are connected to obtain the ground truth vehicle trajectory. Calibration camera vehicle trajectories are generated from the calibration camera data file using existing camera recognition vehicle trajectory methods. For example, in a high-definition map, the latitude and longitude coordinates of each frame of camera data in the camera data file are connected to obtain the calibration camera vehicle trajectory. The ground truth vehicle trajectories and calibration camera vehicle trajectories are displayed together, for example, on the same high-definition map.

[0088] Then, step S104 is executed to obtain the user's modification of the recognition parameters, update the recognition parameters, the recognition parameters are used to determine the center pixel of the target being recognized by the camera, update the center pixel of the target being recognized based on the updated recognition parameters, and generate and display the updated calibration camera vehicle trajectory according to the latitude and longitude corresponding to the center pixel of the target being recognized.

[0089] Specifically, users can directly adjust the values ​​of several recognition parameters using the calibration device tool to adjust the camera positioning. Using the RTK trajectory data of the ground truth vehicle as a benchmark, the vehicle trajectory of multi-point, multi-directional camera positioning is adjusted. The overall interface is displayed as follows. Figure 6 Display. Among them, in Figure 6 In display area 61, the true vehicle trajectory 611 and the calibration camera vehicle trajectory 612 are displayed. The user modifies the phase recognition parameters in parameter adjustment area 62, thereby updating the center pixels of the target recognized by the camera. Then, based on the updated recognition parameters, the center pixels of the recognized target are updated, and the updated calibration camera vehicle trajectory 612 is generated and displayed based on the latitude and longitude corresponding to the updated center pixels of the recognized target. The calibration camera vehicle trajectory 612 is adjusted to approximate the true vehicle trajectory 611.

[0090] Finally, step S105 is executed, and after the user finishes modifying the recognition parameters, the updated recognition parameters and camera calibration parameters are output.

[0091] Specifically, the user adjusts the calibration camera vehicle trajectory 612 to approximate the true vehicle trajectory 611. When the user is satisfied with the consistency between the two, the user can end the modification of the recognition parameters. At this time, the recognition parameters retained after the user ends the modification of the recognition parameters, as well as the camera calibration parameters, are output.

[0092] This invention demonstrates ground truth vehicle trajectories generated from ground truth vehicle data files and calibration camera vehicle trajectories generated from calibration camera data files. By acquiring user modifications to recognition parameters, it generates and displays updated calibration camera vehicle trajectories, enabling visual adjustment of recognition parameters. This invention, through visualization, allows users to intuitively understand the changes in vehicle trajectory caused by changes in recognition parameters. This visualization approach enables more efficient and intuitive camera calibration. This invention significantly improves efficiency and reduces human resource costs in multi-scenario, multi-calibration testing scenarios.

[0093] like Figure 2 The diagram shown is a flowchart of a camera calibration method according to another embodiment of the present invention, including:

[0094] Step S201: Generate initial camera calibration parameters based on checkerboard calibration, wherein the camera calibration parameters are used to represent the correspondence between pixels and latitude and longitude.

[0095] Step S202: Display a road image taken by the camera, including one or more road markings;

[0096] Step S203: Display the road markings in the road image using pixels determined based on the initial camera calibration parameters;

[0097] Step S204: Obtain the user's modification of the camera calibration parameters, update the camera calibration parameters, and display the pixels of the road markings determined based on the updated camera calibration parameters in the road image.

[0098] Step S205: Obtain the ground truth vehicle data file and the calibration camera data file. The ground truth vehicle data file includes multiple ground truth vehicle data frames, and each ground truth vehicle data frame includes at least the latitude and longitude of the ground truth vehicle during operation. The calibration camera data file includes multiple camera data frames, and each camera data frame includes at least the latitude and longitude of the center pixel of the target identified by the camera from the ground truth vehicle.

[0099] Step S206: Display the ground truth vehicle trajectory generated based on the ground truth vehicle data file in the first layer of the display area, and the calibration camera vehicle trajectory generated based on the calibration camera data file in the second layer of the display area.

[0100] Step S207: Obtain the user's modification of the recognition parameters, update the recognition parameters, the recognition parameters are used to determine the center pixel of the target being recognized by the camera, update the center pixel of the target being recognized based on the updated recognition parameters, and generate and display the updated calibration camera vehicle trajectory based on the latitude and longitude corresponding to the center pixel of the target being recognized.

[0101] Step S208: After the user finishes modifying the recognition parameters, output the updated recognition parameters and camera calibration parameters.

[0102] Step S209: Generate a position positioning accuracy trend chart based on the true vehicle data file and the calibration camera data file; and / or

[0103] Based on the true vehicle data file and the calibration camera data file, a heading angle positioning accuracy trend chart is generated.

[0104] Specifically, step S201 is executed first to generate initial camera calibration parameters based on checkerboard calibration. These camera calibration parameters are used to represent the correspondence between pixels and latitude and longitude.

[0105] Specifically, this embodiment uses a checkerboard calibration method to generate camera calibration parameters. Camera calibration parameters include, but are not limited to: intrinsic parameters, extrinsic parameters, and distortion parameters.

[0106] Checkerboard calibration involves placing a checkerboard pattern at different locations within the camera's field of view and then using Zhang Zhengyou's calibration method to obtain the camera's intrinsic parameters and distortion parameters. For example... Figure 3 The image shown is a display of the checkerboard pattern within the camera's field of view. Then, RTK was used to record the positions as feature points at different locations in the real-world scene, such as... Figure 4The image shows the distribution of feature points 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, and 16. The pixel coordinates and corresponding RTK coordinates of these feature points in the image captured by the camera are input into the algorithm. Simultaneously, the calculated camera intrinsic parameters and distortion parameters are input to obtain the extrinsic parameters. Based on the camera intrinsic parameters, distortion parameters, and extrinsic parameters, the true latitude and longitude of each 3D ground point in the scene can be projected onto the image, obtaining the correspondence between the pixels in the image captured by the camera and the true latitude and longitude. The projection effect is shown below. Figure 5 As shown, the chessboard grid marking method can be completed. Among them, Figure 5 The yellow line in the image refers to the projection of the actual latitude and longitude onto the image plane, demonstrating the process of projecting the latitude and longitude of the real map onto the image.

[0107] After completing the checkerboard calibration, initial camera calibration parameters will be obtained. These parameters include, but are not limited to, intrinsic parameters, extrinsic parameters, and distortion parameters. The camera calibration parameters determine the correspondence between each pixel in the image captured by the camera and the actual latitude and longitude.

[0108] Then, step S202 is executed to display a road image captured by the camera, including one or more road markings.

[0109] Specifically, the overall interface design of the calibration device tool was carried out using PyQt. PyQt is a cross-platform Python library used for creating desktop and mobile applications. It is based on the Qt framework and provides a rich set of GUI components and tool libraries. Qt is a cross-platform C++ development framework used for creating desktop and mobile applications. It provides a rich set of GUI components, tool libraries, and platform-specific functionalities.

[0110] like Figure 9 As shown, in Figure 9 In display area 91, a road surface image 911 is displayed, which includes multiple road markings 913. Road markings 913 include, but are not limited to, arrows, lane lines, etc.

[0111] Then, step S203 is performed to display the road markings in the road image using pixels determined based on the initial camera calibration parameters.

[0112] Specifically, a high-definition map corresponding to the road surface image is obtained. The latitude and longitude of multiple points on the boundary line of the road markings are then extracted from the high-definition map. Based on the initial camera calibration parameters, the latitude and longitude of these points on the boundary line are converted into pixels and displayed in the road surface image 911. The display can be achieved by using preset colors for the pixels. Figure 9 As shown, pixel 912 corresponding to the road marking is displayed in green.

[0113] Then, step S204 is executed to obtain the user's modification of the camera calibration parameters, update the camera calibration parameters, and display the pixels of the road markings determined based on the updated camera calibration parameters in the road image.

[0114] During the calibration process, issues such as measurement accuracy may arise, leading to inaccuracies in the camera calibration parameters. Therefore, users can modify the camera calibration parameters.

[0115] like Figure 9 As shown, in the calibration parameter adjustment area 92, the user can modify the camera calibration parameters. The calibration parameter adjustment area 92 includes an intrinsic parameter adjustment area 921, an extrinsic parameter adjustment area 922, and a distortion parameter adjustment area 923.

[0116] After the user modifies the camera calibration parameters, the updated camera calibration parameters will update the correspondence between pixels and latitude and longitude. Therefore, the latitude and longitude of multiple points on the boundary line of the road markings obtained from the high-precision map will be converted into pixels determined based on the updated camera calibration parameters and displayed again in road image 911. Thus, the user will see a change in the pixels used for display, intuitively observing the change in camera calibration parameters. By modifying the camera calibration parameters, the user can ensure that the displayed pixels are consistent with the road markings. When the user stops modifying the calibration parameters, the camera calibration parameters will stop updating; at this point, the accuracy of the camera calibration parameters best matches the user's expectations.

[0117] Then, step S205 is executed to obtain the ground truth vehicle data file and the calibration camera data file. The ground truth vehicle data file includes multiple ground truth vehicle data frames, and each ground truth vehicle data frame includes at least the latitude and longitude of the ground truth vehicle during operation. The calibration camera data file includes multiple camera data frames, and each camera data frame includes at least the latitude and longitude of the center pixel of the target identified by the camera from the ground truth vehicle.

[0118] Specifically, import the ground truth vehicle data file and the calibration camera data file into the calibration device tool. Simultaneously acquire the trajectory data recognized by the camera and the ground truth vehicle trajectory data under both camera calibration methods.

[0119] The ground truth vehicle trajectory data is stored in a ground truth vehicle data file. Preferably, the ground truth vehicle data file is a ground truth vehicle RTK data file. RTK is a technology used for high-precision positioning. An RTK receiver can receive GPS satellite information and calculate high-precision position information. First, an RTK receiver is installed on the vehicle, then connected to the vehicle, and the data generated by the RTK receiver is acquired. This data typically includes the vehicle's position, speed, and direction. Ground truth vehicle RTK data is obtained by manually driving the ground truth vehicle. The RTK data is then structured to obtain ground truth vehicle RTK structured data and saved in the ground truth vehicle RTK data file. The ground truth vehicle RTK structured data includes the timestamp, latitude and longitude, and heading angle information of the ground truth vehicle trajectory data. Simultaneously, the ground truth vehicle is photographed using a camera, such as a roadside device. Raw camera data is obtained from the camera, and this raw camera data is structured to generate raw camera structured data and saved in the camera data file. The raw camera structured data includes the identified ground truth vehicle trajectory latitude and longitude, timestamp, heading angle, category, and other geographical location information.

[0120] Then, step S206 is executed, displaying the ground truth vehicle trajectory generated based on the ground truth vehicle data file in the first layer of the display area, and the calibration camera vehicle trajectory generated based on the calibration camera data file in the second layer of the display area.

[0121] Specifically, the calibration device tool sets up a whole area to display a high-precision map and vehicle trajectory. By combining the high-precision map with the calibration device tool, the geographic location information is converted into 2D pixels, thereby enabling a comparison and display of the ground truth vehicle trajectory and the vehicle trajectory from the calibration camera.

[0122] The ground truth vehicle trajectory is generated using an existing RTK trajectory algorithm based on the ground truth vehicle data file, while the calibration camera vehicle trajectory is generated using an existing camera trajectory recognition method based on the camera calibration parameters and the calibration camera data file.

[0123] like Figure 6 As shown, in Figure 6 In display area 61, the center latitude and longitude of the intersection are used as the reference. As the origin of the local coordinates, the region of interest is set to determine the map display area. Then, the map's geodetic coordinate system is converted to the local coordinate system, and then to pixel coordinates. The ground truth vehicle trajectory 611, the calibration camera vehicle trajectory 612, and the high-precision map are then projected onto the display area 61. Specifically, three layers are set up in the display area 61: the bottom layer contains the high-precision map; the middle layer contains the ground truth vehicle trajectory 611, which is displayed on the high-precision map based on the latitude and longitude of each trajectory point in the ground truth vehicle trajectory 611; and the top layer contains the camera vehicle trajectory 612, which is displayed on the high-precision map based on the latitude and longitude of each trajectory point in the calibration camera vehicle trajectory 612.

[0124] Then, step S207 is executed to obtain the user's modification of the recognition parameters, update the recognition parameters, which are used to determine the center pixel of the target being recognized by the camera, update the center pixel of the target being recognized based on the updated recognition parameters, and generate and display the updated calibration camera vehicle trajectory based on the latitude and longitude corresponding to the center pixel of the target being recognized.

[0125] Camera positioning can be adjusted directly by adjusting the values ​​of several recognition parameters using a calibration device. The RTK trajectory data of the ground truth vehicle is used as a benchmark to adjust the multi-point, multi-directional checkerboard positioning.

[0126] Specifically, the initial intrinsic, extrinsic, and distortion parameters are obtained through checkerboard calibration, and specific adjustments can be made according to... Figure 9 The interface is adjusted to obtain the adjusted intrinsic, extrinsic, and distortion parameters. The interface then displays the adjustment area 62 for calculating the recognition parameters of the target's center pixel. Recognition parameters include, but are not limited to: yThresh, xThresh, yRatio1, yRatio2, and xRatio1. By adjusting the corresponding recognition parameters, the user affects the center pixel of the target acquired by the camera. The obtained intrinsic, extrinsic, and distortion parameters are then used to calculate the latitude and longitude of the center pixel mapping, thereby adjusting the latitude and longitude of each trajectory point in the calibration camera vehicle trajectory 612. Based on the adjusted latitude and longitude of each trajectory point, the display position of the calibration camera vehicle trajectory 612 on the high-precision map is adjusted. The user can use the true vehicle trajectory 611 (the black trajectory) as a benchmark and then adjust the recognition parameters to make the calibrated camera vehicle trajectory 612 recognized by the camera closer to the true vehicle trajectory 611.

[0127] In one embodiment, the recognition parameters include vertical adjustment parameters and horizontal adjustment parameters, and updating the center pixel of the recognition target based on the updated recognition parameters includes:

[0128] Obtain the horizontal coordinates of the bottom midpoint pixel of the target bounding box, the vertical coordinates of the bottom midpoint pixel, the horizontal width of the target bounding box, and the vertical height of the target bounding box;

[0129] Based on the updated horizontal adjustment parameters and the horizontal coordinates of the bottom center pixel, calculate the horizontal proportion of the center pixel; based on the updated vertical adjustment parameters and the vertical coordinates of the bottom midpoint pixel, calculate the vertical proportion of the center pixel.

[0130] The horizontal coordinates of the center pixel of the target are calculated based on the horizontal ratio of the center pixel, the horizontal coordinates of the bottom midpoint pixel, and the horizontal width of the target box.

[0131] The vertical coordinates of the center pixel of the target are calculated based on the vertical proportion of the center pixel, the vertical coordinates of the bottom midpoint pixel, and the vertical height of the target box.

[0132] Specifically, yThresh, yRatio1, and yRatio2 are used as vertical adjustment parameters, and xThresh and xRatio1 are used as horizontal recognition adjustment parameters. The target is identified using a two-dimensional bounding box. Based on the width and height of the bounding box, the vertical position of the target's center pixel is adjusted using the vertical adjustment parameters, and the horizontal position of the target's center pixel is adjusted using the horizontal adjustment parameters. The vertical and horizontal recognition parameters together determine the pixel corresponding to the target's center.

[0133] As an example, yThresh is the vertical adjustment threshold, yRatio1 and yRatio2 are two different vertical adjustment ratios, xThresh is the horizontal adjustment threshold, and xRatio1 is the horizontal adjustment ratio.

[0134] Get the horizontal coordinates of the bottom midpoint pixel of the target bounding box (centerX), the vertical coordinates of the bottom midpoint pixel (centery), the horizontal width (objectWidth), and the vertical height (objectHeight) of the target bounding box;

[0135] Based on the updated horizontal adjustment parameters and the horizontal coordinates of the bottom center pixel, the horizontal proportion of the center pixel is calculated as follows:

[0136] Calculate w_ratio = (centerX / xThresh - 1) * xRatio1, where w_ratio is the horizontal ratio of the center pixel;

[0137] Based on the updated vertical adjustment parameters and the vertical coordinates of the bottom edge midpoint pixel, the vertical proportion of the center pixel is calculated as follows:

[0138] For the case where yThresh > 0:

[0139] If centerY > yThresh, then calculate h_ratio = (centerY / yThresh - 1) * yRatio1;

[0140] If centerY ≤ yThresh, then calculate h_ratio = (centerY / yThresh - 1) * yRatio2;

[0141] For the case where yThresh ≤ 0: calculate h_ratio = centerY / 720 * yRatio1;

[0142] Where h_ratio is the vertical ratio of the center pixel.

[0143] Finally, the horizontal coordinates of the center pixel of the target are calculated based on the horizontal proportion of the center pixel, the horizontal coordinates of the bottom midpoint pixel, and the horizontal width of the target bounding box. Specifically:

[0144] reflectX = centerX + w_ratio * objectWidth, where reflectX is the x-coordinate of the center pixel of the target being identified;

[0145] The vertical coordinates of the center pixel of the target are calculated based on the vertical proportion of the center pixel, the vertical coordinates of the bottom midpoint pixel, and the vertical height of the target bounding box. Specifically:

[0146] reflectY = centerY - h_ratio * objectHeight, where reflectY is the center pixel coordinate of the target being identified.

[0147] Then, step S208 is executed, and after the user finishes modifying the recognition parameters, the updated recognition parameters and camera calibration parameters are output.

[0148] When the end user stops making modifications, a set of parameters will be obtained in the recognition parameter adjustment area 62. This set of parameters will be used as the updated recognition parameters. Similarly, in... Figure 9 The updated camera calibration parameters can also be obtained from the calibration parameter adjustment area 92 shown. During output, both the updated recognition parameters and the updated camera calibration parameters are output simultaneously.

[0149] After obtaining the final recognition parameters and camera calibration parameters, in subsequent use, the optimal center pixel in the target is calculated using the final recognition parameters, and the calculated center pixel is converted into accurate latitude and longitude using the final camera calibration parameters, so as to provide a basis for subsequent trajectory budgeting or perception of autonomous driving.

[0150] In addition to performing camera calibration, this embodiment also performs accuracy testing, enabling calibration and accuracy testing of true data at different points and directions directly through calibration devices.

[0151] Execute step S209, generating a position positioning accuracy trend chart based on the true vehicle data file and calibration camera data file; and / or

[0152] Based on the true vehicle data file and the calibration camera data file, a heading angle positioning accuracy trend chart is generated.

[0153] Accuracy testing includes positioning accuracy testing and / or heading angle accuracy testing.

[0154] In one embodiment, generating a position positioning accuracy trend chart based on the true vehicle data file and the calibration camera data file includes:

[0155] The timestamp of each real vehicle data frame in the real vehicle data file is used as the base timestamp;

[0156] For each reference timestamp, the timestamp closest to the reference timestamp in the calibration camera data file is taken as the camera timestamp, the latitude and longitude corresponding to the camera timestamp in the calibration camera data file is taken as the camera latitude and longitude, and the latitude and longitude corresponding to the reference timestamp in the true vehicle data file is taken as the true vehicle latitude and longitude. The positional distance difference between the camera latitude and longitude and the true vehicle latitude and longitude is calculated.

[0157] A location positioning accuracy trend chart is generated based on the location distance difference corresponding to each of the aforementioned reference timestamps.

[0158] Specifically, the position accuracy test includes:

[0159] 1) Obtain the latitude and longitude of each real vehicle data frame and the timestamp of each real vehicle data frame from the ground truth vehicle data file and the calibration camera data file.

[0160] 2) Use the timestamp of each real vehicle data frame in the true vehicle data file as the reference data, i.e., the reference timestamp. Align the timestamp of the calibration camera data file with the reference timestamp. That is, find the timestamp that is closest to the reference timestamp in the calibration camera data file as the camera timestamp. Find the latitude and longitude corresponding to the camera timestamp in the calibration camera data file and the latitude and longitude corresponding to the reference timestamp in the true vehicle data file.

[0161] 3) Calculate the positional distance difference between the frame containing the reference timestamp and the frame containing the camera timestamp using geodesic(true vehicle dimension, true vehicle precision, original camera dimension, original camera precision). That is, calculate the positional distance difference between the latitude and longitude corresponding to the camera timestamp and the latitude and longitude corresponding to the reference timestamp. By calculating the positional distance differences of multiple reference timestamps, generate a positional positioning accuracy trend chart related to the positional error of the true vehicle and the original camera through timestamp alignment.

[0162] The location positioning accuracy trend chart is obtained by connecting the location distance differences corresponding to multiple reference timestamps with time as the horizontal axis and location distance difference as the vertical axis.

[0163] In one embodiment, generating a heading angle positioning accuracy trend chart based on the true vehicle data file and the calibration camera data file includes:

[0164] The timestamp of each real vehicle data frame in the real vehicle data file is used as the base timestamp;

[0165] For each reference timestamp, the timestamp closest to the reference timestamp in the calibration camera data file is taken as the camera timestamp, the heading angle corresponding to the camera timestamp in the calibration camera data file is taken as the camera heading angle, and the heading angle corresponding to the reference timestamp in the true vehicle data file is taken as the true vehicle heading angle. The heading angle difference between the camera heading angle and the true vehicle heading angle is calculated.

[0166] Based on the heading angle difference corresponding to each of the aforementioned reference timestamps, a heading angle positioning accuracy trend chart is generated.

[0167] Heading angle accuracy testing includes:

[0168] 1) Obtain the heading angle and timestamp of each real vehicle data frame from the ground truth vehicle data file and the calibration camera data file.

[0169] 2) Using the timestamp of each real vehicle data frame in the true vehicle data file as the reference data, i.e. the reference timestamp, the timestamp of the calibration camera data file is aligned with the reference timestamp. That is, the timestamp closest to the reference timestamp is found in the calibration camera data file and used as the camera timestamp. The heading angle difference value corresponding to the camera timestamp in the calibration camera data file and the heading angle difference value corresponding to the reference timestamp in the true vehicle data file are then searched.

[0170] 3) Calculate the heading angle difference between the frame containing the reference timestamp and the frame containing the camera timestamp. That is, calculate the heading angle difference between the heading angle corresponding to the camera timestamp and the heading angle corresponding to the reference timestamp. By calculating the heading angle difference of multiple reference timestamps, generate a heading angle positioning accuracy trend chart related to the heading angle error between the true vehicle and the original camera through timestamp alignment.

[0171] The heading angle positioning accuracy trend chart is obtained by connecting the heading angle differences corresponding to multiple reference timestamps with time as the horizontal axis and heading angle difference as the vertical axis.

[0172] To address the issues of complexity, low efficiency, and inability to combine camera calibration and accuracy testing in existing technologies, this embodiment combines camera calibration with post-calibration accuracy testing through automated calibration devices and testing methods. This allows for visual adjustment of calibration parameters and simultaneous visual observation of the comparison between calibration trajectory data and ground truth vehicle trajectory data. Furthermore, this embodiment integrates high-precision maps with the entire process of camera calibration accuracy testing, enabling direct position and heading angle accuracy testing immediately after calibration. A visual interface facilitates more efficient and intuitive checkerboard calibration and accuracy testing. This automated approach replaces traditional code-based engineering, eliminating the need for developer-centric methods and significantly improving efficiency while reducing human resource costs in multi-scenario, multi-calibration testing scenarios. It enables more efficient and convenient camera calibration and testing in scenarios requiring numerous calibrations. Moreover, by covering the entire process from camera calibration to accuracy testing, this invention significantly reduces human resource costs.

[0173] like Figure 7 The diagram shown is a flowchart of a camera calibration method according to a preferred embodiment of the present invention, comprising:

[0174] Step S701: Import the true value vehicle RTK structured data, camera raw structured data, and high-precision map into the calibration device tool.

[0175] Step S702: Perform checkerboard calibration, visualize and compare the original trajectory data of RTK and camera, and finally output the checkerboard calibration parameter results;

[0176] Step S703: Perform positioning accuracy test, output position positioning accuracy trend chart and heading angle positioning accuracy trend chart, and output a summary of accuracy test analysis results.

[0177] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0178] like Figure 8 The diagram shown is a hardware structure schematic of an electronic device according to the present invention, comprising:

[0179] At least one processor 801; and,

[0180] A memory 802 is communicatively connected to at least one of the processors 801; wherein,

[0181] The memory 802 stores instructions that can be executed by at least one of the processors to enable the at least one of the processors to perform the camera calibration method as described above.

[0182] Figure 8 Take the 801 processor as an example.

[0183] The electronic device may also include an input device 803 and a display device 804.

[0184] The processor 801, memory 802, input device 803 and display device 804 can be connected by a bus or other means. The figure shows an example of connection by bus.

[0185] The memory 802, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the camera calibration method in the embodiments of this application, for example, Figure 1 , Figure 2 The method flow is shown. The processor 801 executes various functional applications and data processing by running non-volatile software programs, instructions, and modules stored in the memory 802, thereby implementing the camera calibration method in the above embodiments.

[0186] Memory 802 may include a program storage area and a data storage area. The program storage area may store an operating system and an application program required for at least one function. The data storage area may store data created based on the use of the camera calibration method. Furthermore, memory 802 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 802 may optionally include memory remotely located relative to processor 801, and these remote memories may be connected via a network to the apparatus performing the camera calibration method. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0187] The input device 803 can receive user clicks and generate signal inputs related to user settings and function control for camera calibration methods. The display device 804 may include a display screen or other display device.

[0188] When one or more modules are stored in the memory 802, and are run by one or more processors 801, the camera calibration method in any of the above method embodiments is executed.

[0189] This invention demonstrates ground truth vehicle trajectories generated from ground truth vehicle data files and calibration camera vehicle trajectories generated from calibration camera data files. By acquiring user modifications to recognition parameters, it generates and displays updated calibration camera vehicle trajectories, enabling visual adjustment of recognition parameters. This invention, through visualization, allows users to intuitively understand the changes in vehicle trajectory caused by changes in recognition parameters. This visualization approach enables more efficient and intuitive camera calibration. This invention significantly improves efficiency and reduces human resource costs in multi-scenario, multi-calibration testing scenarios.

[0190] One embodiment of the present invention provides a storage medium that stores computer instructions, which, when executed by a computer, are used to perform all the steps of the camera calibration method described above.

[0191] In the context of this disclosure, a storage medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. The storage medium can be a machine-readable signal medium or a machine-readable storage medium. Optionally, the storage medium can be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), compact disc ROM (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0192] One embodiment of the present invention provides a computer program product, including a computer program / instructions, which, when executed by a processor, implements the camera calibration method as described above.

[0193] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A camera calibration method, characterized in that, include: The camera is calibrated to obtain camera calibration parameters, and a correspondence between pixels and latitude and longitude is established based on the camera calibration parameters; Acquire ground truth vehicle data file and calibration camera data file. The ground truth vehicle data file includes multiple ground truth vehicle data frames, and each ground truth vehicle data frame includes at least the latitude and longitude of the ground truth vehicle during operation. The calibration camera data file includes multiple camera data frames, and each camera data frame includes at least the latitude and longitude of the center pixel of the target identified by the camera from the ground truth vehicle. Display the ground truth vehicle trajectory generated based on the ground truth vehicle data file, and the calibration camera vehicle trajectory generated based on the calibration camera data file; The system obtains the user's modification of the recognition parameters, updates the recognition parameters, which are used to determine the center pixel of the target being recognized by the camera, updates the center pixel of the target based on the updated recognition parameters, and generates and displays the updated calibration camera vehicle trajectory based on the latitude and longitude corresponding to the center pixel of the target. After the user finishes modifying the recognition parameters, the updated recognition parameters and camera calibration parameters are output.

2. The camera calibration method according to claim 1, characterized in that, The display of the ground truth vehicle trajectory generated based on the ground truth vehicle data file and the calibration camera vehicle trajectory generated based on the calibration camera data file includes: The first layer of the display area shows the ground truth vehicle trajectory generated based on the ground truth vehicle data file, and the second layer of the display area shows the calibration camera vehicle trajectory generated based on the calibration camera data file.

3. The camera calibration method according to claim 1, characterized in that, The recognition parameters include vertical adjustment parameters and horizontal adjustment parameters. Updating the center pixel of the target based on the updated recognition parameters includes: Obtain the horizontal coordinates of the bottom midpoint pixel of the target bounding box, the vertical coordinates of the bottom midpoint pixel, the horizontal width of the target bounding box, and the vertical height of the target bounding box; Based on the updated horizontal adjustment parameters and the horizontal coordinates of the bottom center pixel, calculate the horizontal proportion of the center pixel; based on the updated vertical adjustment parameters and the vertical coordinates of the bottom midpoint pixel, calculate the vertical proportion of the center pixel. The horizontal coordinates of the center pixel of the target are calculated based on the horizontal ratio of the center pixel, the horizontal coordinates of the bottom midpoint pixel, and the horizontal width of the target box. The vertical coordinates of the center pixel of the target are calculated based on the vertical proportion of the center pixel, the vertical coordinates of the bottom midpoint pixel, and the vertical height of the target box.

4. The camera calibration method according to claim 1, characterized in that, Also includes: Based on the true vehicle data file and the calibration camera data file, a position positioning accuracy trend chart is generated; and / or Based on the true vehicle data file and the calibration camera data file, a heading angle positioning accuracy trend chart is generated.

5. The camera calibration method according to claim 3, characterized in that, The step of generating a position positioning accuracy trend chart based on the true vehicle data file and the calibration camera data file includes: The timestamp of each real vehicle data frame in the real vehicle data file is used as the base timestamp; For each reference timestamp, the timestamp closest to the reference timestamp in the calibration camera data file is taken as the camera timestamp, the latitude and longitude corresponding to the camera timestamp in the calibration camera data file is taken as the camera latitude and longitude, and the latitude and longitude corresponding to the reference timestamp in the true vehicle data file is taken as the true vehicle latitude and longitude. The positional distance difference between the camera latitude and longitude and the true vehicle latitude and longitude is calculated. A location positioning accuracy trend chart is generated based on the location distance difference corresponding to each of the aforementioned reference timestamps.

6. The camera calibration method according to claim 3, characterized in that, The step of generating a heading angle positioning accuracy trend chart based on the true vehicle data file and the calibration camera data file includes: The timestamp of each real vehicle data frame in the real vehicle data file is used as the base timestamp; For each reference timestamp, the timestamp closest to the reference timestamp in the calibration camera data file is taken as the camera timestamp, the heading angle corresponding to the camera timestamp in the calibration camera data file is taken as the camera heading angle, and the heading angle corresponding to the reference timestamp in the true vehicle data file is taken as the true vehicle heading angle. The heading angle difference between the camera heading angle and the true vehicle heading angle is calculated. Based on the heading angle difference corresponding to each of the aforementioned reference timestamps, a heading angle positioning accuracy trend chart is generated.

7. The camera calibration method according to any one of claims 1 to 6, characterized in that, The process of calibrating the camera to obtain camera calibration parameters, and establishing a correspondence between pixels and latitude and longitude based on these parameters, includes: Initial camera calibration parameters are generated based on checkerboard calibration, and these parameters are used to represent the correspondence between pixels and latitude and longitude. Display images of the road surface taken by the camera, including one or more road markings; The road markings are displayed in the road image based on pixels determined by the initial camera calibration parameters; The system obtains the user's modifications to the camera calibration parameters, updates the camera calibration parameters, and displays the road markings in the road image with pixels determined based on the updated camera calibration parameters.

8. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by at least one of the processors to enable at least one of the processors to perform the camera calibration method as described in any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium stores computer instructions, which, when executed by the computer, are used to perform all the steps of the camera calibration method as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the camera calibration method as described in any one of claims 1 to 7.

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