Camera calibration method and device, target positioning method and device

Abstract

This specification relates to the field of camera calibration technology and provides a camera calibration method and device, as well as a target positioning method and device. The camera calibration method includes: obtaining multiple environmental images generated by a camera to be calibrated; establishing an undistorted imaging model corresponding to the environmental images; calculating the intrinsic parameter matrix and extrinsic parameter matrix of the undistorted imaging model; establishing a distortion fitting model for the environmental images; using a nonlinear fitting algorithm to determine the distortion parameters corresponding to the distortion fitting model for the environmental images before and after distortion; using the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix as decision variables to establish an objective function of the distortion fitting model under regularization constraints; using a nonlinear optimization algorithm to optimize the objective function to optimize the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix; and completing camera calibration based on the optimized intrinsic parameter matrix, extrinsic parameter matrix, and distortion parameters. Through the embodiments of this specification, the efficiency and accuracy of camera calibration can be improved.

Description

Technical Field

[0001] This specification relates to the field of camera calibration technology, and in particular to a camera calibration method and device, and a target positioning method and device. Background Art

[0002] Intelligent video surveillance technology utilizes high-definition cameras and advanced computer vision technology to monitor and analyze visual data of a specific area in real time, automatically detecting and identifying various events and objects, such as people, vehicles, objects, and even behavioral patterns, to provide more efficient monitoring and security management.

[0003] Camera calibration is an important prerequisite for realizing intelligent video surveillance. The accuracy of its calibration results and the stability of the algorithm directly affect the accuracy of the results produced by the camera. The existing camera calibration method requires the use of a calibration object with known size. By establishing the correspondence between the points with known coordinates on the calibration object and their image points, a certain algorithm is used to obtain the internal and external parameters of the camera model. However, the calibration object is always required during the calibration process, and the production accuracy of the calibration object will affect the calibration results. In addition, the calibration object cannot be placed in some scenes. As a result, the existing camera calibration method has the problems of complex camera calibration task operation and low positioning accuracy. Therefore, there is an urgent need for a camera calibration method that simplifies the camera calibration process, improves the camera calibration accuracy, and can be applied to complex scene monitoring such as refining scenes. Summary of the Invention

[0004] In view of the problems that the current camera calibration method has the problems of complex camera calibration task operation and low positioning accuracy, this solution is proposed to overcome the above problems or at least partially solve the above problems.

[0005] On the one hand, some embodiments of this specification are directed to providing a camera calibration method, the method comprising:

[0006] Obtain multiple environment images generated by the camera to be calibrated;

[0007] Establishing a distortion-free imaging model according to a perspective imaging process of a checkerboard calibration plate in the environmental image;

[0008] Calculating the intrinsic parameter matrix and the extrinsic parameter matrix of the undistorted imaging model;

[0009] Establishing a distortion fitting model of the environmental image;

[0010] Determining distortion parameters corresponding to the distortion fitting model using a nonlinear fitting algorithm for the environmental image before and after distortion;

[0011] Taking the distortion parameters, internal parameter matrix and external parameter matrix as decision variables, establishing the objective function of the distortion fitting model under regularity constraints;

[0012] Optimizing the objective function using a nonlinear optimization algorithm to optimize the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix;

[0013] The camera calibration is completed based on the optimized intrinsic parameter matrix, extrinsic parameter matrix and distortion parameters.

[0014] Furthermore, a distortion-free imaging model is established according to the perspective imaging process of the checkerboard calibration plate in the environmental image, including:

[0015] Determine, based on two sets of coordinate information of the checkerboard calibration plate in the world coordinate system and the pixel coordinate system and the perspective imaging process, a perspective imaging relationship of the checkerboard calibration plate mapped from the world coordinate system to the camera coordinate system and the pixel coordinate system in sequence;

[0016] Performing a product operation on a mapping relationship of the checkerboard calibration plate from the world coordinate system to the camera coordinate system and a mapping relationship of the checkerboard calibration plate from the camera coordinate system to the pixel coordinate system to obtain a product operation matrix;

[0017] The checkerboard calibration plate is mapped from the world coordinate system to the pixel coordinate system according to the product operation matrix to establish the distortion-free imaging model.

[0018] Furthermore, before calculating the intrinsic parameter matrix and the extrinsic parameter matrix of the distortion-free imaging model, the method further includes:

[0019] Acquire a color calibration plate image, pixel coordinates of preset calibration points in the color calibration plate image, and three-dimensional coordinates of the preset calibration points;

[0020] Inputting the color calibration plate image into a pre-trained neural network model to identify the pixel coordinates of a preset area in the color calibration plate image;

[0021] Establishing a coordinate mapping model between the pixel coordinates of the preset calibration points and the three-dimensional coordinates of the preset calibration points;

[0022] Inputting the pixel coordinates of the preset area into the coordinate mapping model to obtain the three-dimensional coordinates of the preset area;

[0023] According to the three-dimensional coordinates of the preset area, coordinate information of a plurality of target points in the checkerboard calibration plate having the same position as the color calibration plate in the world coordinate system is determined.

[0024] Furthermore, calculating the intrinsic parameter matrix and the extrinsic parameter matrix of the distortion-free imaging model includes:

[0025] Inputting coordinate information of multiple target points in the checkerboard calibration plate in the world coordinate system and coordinate information of the target points in the pixel coordinate system into the undistorted imaging model to obtain a homography matrix;

[0026] Determining the constraint equation corresponding to the symmetric matrix of the intrinsic parameter matrix according to the coordinate information of the target point of the checkerboard calibration plate in the plurality of environmental images in the pixel coordinate system;

[0027] Based on the constraint equation, the symmetric matrix is calculated using a fitting algorithm;

[0028] Determine an internal parameter matrix corresponding to the symmetric matrix;

[0029] The product of the inverse matrix of the intrinsic parameter matrix and the homography matrix is used as the extrinsic parameter matrix.

[0030] Furthermore, a nonlinear fitting algorithm is used to determine the distortion parameters corresponding to the distortion fitting model, including:

[0031] Inputting coordinate information of a plurality of target points in the checkerboard calibration plate in the pixel coordinate system into the distortion fitting model to obtain a plurality of constraint equations constructed by distortion parameters;

[0032] A nonlinear fitting algorithm is used to fit a plurality of constraint equations constructed by the distortion parameters, and the distortion parameters are obtained according to the fitting results.

[0033] Furthermore, the distortion parameters, the internal parameter matrix, and the external parameter matrix are used as decision variables to establish the objective function of the distortion fitting model under regularization constraints, including:

[0034] Using the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix as decision variables, determining the degree of difference between the environment image before and after the distortion under the current decision variables and the regularization terms corresponding to the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix;

[0035] The objective function is established by performing a weighted summation on the difference degree and the regularization term.

[0036] Furthermore, the objective function is optimized using a nonlinear optimization algorithm to optimize the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix, including:

[0037] Take the current decision variable as the current iteration point;

[0038] Constructing a similar function to the target function in the neighborhood of the current iteration point;

[0039] Calculate the minimum value corresponding to the similarity function in the neighborhood, and use the minimum point corresponding to the minimum value as the next iteration point;

[0040] Calculating the similarity between the target function and the similarity function according to the current iteration point and the next iteration point;

[0041] If the similarity is greater than a first threshold, taking the next iteration point as the current iteration point;

[0042] If the similarity is not greater than a first threshold, determining an iteration step length according to the similarity, iterating the current iteration point using the iteration step length, and using an iteration result as the current iteration point;

[0043] Repeat the above steps of updating the current iteration point until the objective function converges.

[0044] Based on the same inventive concept, some embodiments of this specification further provide a target positioning method, the method comprising:

[0045] Receiving an image to be positioned; the image to be positioned is generated by a camera calibrated using the method described in any of the aforementioned embodiments;

[0046] The image to be positioned is input into a pre-trained target positioning model to obtain a positioning result.

[0047] On the other hand, some embodiments of this specification further provide a camera calibration device, the device comprising:

[0048] An acquisition module is used to acquire multiple environment images generated by the camera to be calibrated;

[0049] An imaging modeling module, configured to establish a distortion-free imaging model based on a perspective imaging process of a checkerboard calibration plate in the environmental image;

[0050] A parameter calculation module, used to calculate the intrinsic parameter matrix and the extrinsic parameter matrix of the distortion-free imaging model;

[0051] A distortion modeling module, used to establish a distortion fitting model of the environment image;

[0052] A distortion fitting module is used to determine the distortion parameters corresponding to the distortion fitting model using a nonlinear fitting algorithm for the environmental image before and after distortion;

[0053] An objective function establishment module is used to establish an objective function of the distortion fitting model under regularity constraints by taking the distortion parameters, the internal parameter matrix and the external parameter matrix as decision variables;

[0054] An optimization module, configured to optimize the objective function using a nonlinear optimization algorithm to optimize the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix;

[0055] The calibration module is used to complete the camera calibration based on the optimized intrinsic parameter matrix, extrinsic parameter matrix and distortion parameters.

[0056] On the other hand, based on the same inventive concept, some embodiments of this specification further provide a target positioning device, the device comprising:

[0057] A receiving module, configured to receive an image to be positioned; the image to be positioned is generated by a camera calibrated using the method described in any of the aforementioned embodiments;

[0058] The positioning module is used to input the image to be positioned into a pre-trained target positioning model to obtain a positioning result.

[0059] On the other hand, some embodiments of this specification further provide a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the computer program executes instructions of the above method when executed by the processor.

[0060] On the other hand, some embodiments of this specification further provide a computer storage medium having a computer program stored thereon, wherein the computer program executes the instructions of the above method when executed by a processor of a computer device.

[0061] On the other hand, some embodiments of this specification further provide a computer program product, which includes a computer program. When the computer program is executed by a processor of a computer device, the computer program executes instructions of the above method.

[0062] Some embodiments of this specification provide one or more technical solutions that have at least the following technical effects:

[0063] The embodiments of the present specification first automatically obtain multiple environmental images generated by the camera to be calibrated, and then establish a distortion-free imaging model based on the perspective imaging process of the checkerboard calibration plate in the environmental image to determine the intrinsic parameter matrix corresponding to the camera and the extrinsic parameter matrix corresponding to the environmental image under the distortion-free condition, thereby establishing a distortion fitting model of the environmental image, and using a nonlinear fitting algorithm to determine the corresponding distortion parameters. However, since the obtained distortion parameters, intrinsic parameter matrix, and extrinsic parameter matrix are obtained by fitting using an approximate method, an objective function of the distortion fitting model under regularization constraints is established, and the objective function is optimized using a nonlinear optimization algorithm, thereby optimizing the distortion parameters, intrinsic parameter matrix, and extrinsic parameter matrix to ensure that the camera calibration accuracy is improved while ensuring the camera calibration efficiency.

[0064] The above description is only an overview of the technical solutions of some embodiments of this specification. In order to more clearly understand the technical means of some embodiments of this specification, they can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of some embodiments of this specification more obvious and easy to understand, the specific implementation methods of some embodiments of this specification are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate some embodiments of this specification or technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments described in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without inventive work. In the drawings:

[0066] Figure 1 A schematic diagram of an implementation system of a camera calibration method in some embodiments of this specification is shown;

[0067] Figure 2 A flowchart of a camera calibration method in some embodiments of this specification is shown;

[0068] Figure 3 A schematic diagram of the steps for establishing a distortion-free imaging model in some embodiments of this specification;

[0069] Figure 4 A schematic diagram of the process of distortion-free imaging in some embodiments of this specification;

[0070] Figure 5 This is a schematic diagram of the steps of determining the coordinate information of multiple target points on a checkerboard calibration plate in the world coordinate system in some embodiments of this specification;

[0071] Figure 6 Schematic diagram of the steps for calculating the intrinsic parameter matrix and the extrinsic parameter matrix in some embodiments of this specification;

[0072] Figure 7 Schematic diagram of the steps of fitting and obtaining distortion parameters in some embodiments of this specification;

[0073] Figure 8 A schematic diagram of the steps for establishing an objective function in some embodiments of this specification;

[0074] Figure 9 Schematic diagram of the steps for optimizing distortion parameters, intrinsic parameter matrices, and extrinsic parameter matrices in some embodiments of this specification;

[0075] Figure 10 A flow chart of a target positioning method in some embodiments of this specification is shown;

[0076] Figure 11 This is a schematic structural diagram of a camera calibration device in some embodiments of this specification;

[0077] Figure 12 This is a schematic structural diagram of a target positioning device in some embodiments of this specification;

[0078] Figure 13 This is a schematic diagram of the computer device structure provided in some embodiments of this specification.

[0079] [Description of Reference Numerals]

[0080] 101. Terminal;

[0081] 102. Server;

[0082] 1101. Get module;

[0083] 1102. Imaging modeling module;

[0084] 1103. Parameter calculation module;

[0085] 1104. Distortion modeling module;

[0086] 1105. Distortion fitting module;

[0087] 1106. Objective function establishment module;

[0088] 1107. Optimization module;

[0089] 1108, calibration module;

[0090] 1201, receiving module;

[0091] 1202, positioning module;

[0092] 1302. Computer equipment;

[0093] 1304, processor;

[0094] 1306. Memory;

[0095] 1308, driving mechanism;

[0096] 1310, input / output interface;

[0097] 1312. Input devices;

[0098] 1314. Output device;

[0099] 1316. Presentation equipment;

[0100] 1318. Graphical User Interface;

[0101] 1320, network interface;

[0102] 1322, communication link;

[0103] 1324. Communication bus. DETAILED DESCRIPTION

[0104] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings of some embodiments of this specification. Obviously, the embodiments described are only some of the embodiments of this specification, not all of them. All other embodiments obtained by those skilled in the art based on some of the embodiments in this specification without creative work should fall within the scope of protection of this specification.

[0105] It should be noted that the terms "first", "second", etc. in the specification and claims of this document and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of this document described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment. It should be noted that the acquisition, storage, use, processing, etc. of data in the technical solution of this application comply with the relevant provisions of relevant laws and regulations.

[0106] like Figure 1 The figure shows a schematic diagram of a system for implementing a camera calibration method according to an embodiment of the present invention. The system may include a terminal 101 and a server 102. The terminal 101 stores multiple images captured by the camera to be calibrated. The terminal 101 and the server 102 communicate via a network. The network may include a local area network (LAN), a wide area network (WAN), the Internet, or a combination thereof, and may be connected to a website, a user device (e.g., a computing device), and a back-end system. A staff member may send a camera calibration request to the server 102 via the terminal 101. Upon receiving the camera calibration request, the server 102 retrieves data from a database for computational processing to obtain a camera calibration result. The server 102 then sends the camera calibration result to the terminal 101, allowing the staff member to process business operations based on the camera calibration result.

[0107] In the embodiments of this specification, the server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN, Content Delivery Network), and big data and artificial intelligence platforms.

[0108] In an optional embodiment, the terminal 101 may include, but is not limited to, electronic devices such as self-service terminal devices, desktop computers, tablet computers, laptop computers, smart wearable devices, etc., which can be connected to the camera to be calibrated. Optionally, the operating system running on the electronic device may include, but is not limited to, Android, iOS, Linux, Windows, etc. Of course, the terminal 101 is not limited to the above-mentioned electronic devices with a certain entity, and it can also be software running on the above-mentioned electronic devices.

[0109] In addition, it should be noted that Figure 1 What is shown is only an application environment provided by the present disclosure. In actual application, multiple terminals 101 may be included, and this specification does not limit this.

[0110] Figure 2 This is a flowchart of a camera calibration method provided by an embodiment of the present invention. This specification provides the method operation steps described in the embodiment or flowchart, but based on conventional or non-creative work, more or fewer operation steps may be included. The order of steps listed in the embodiment is only one way of executing the steps among many steps, and does not represent the only execution order. When the actual system or device product is executed, it can be executed in the order or in parallel according to the method shown in the embodiment or the accompanying drawings. Specifically, Figure 2 As shown, applying the above-mentioned server side, the method may include:

[0111] S201: Acquire multiple environment images generated by the camera to be calibrated;

[0112] S202: establishing a distortion-free imaging model according to a perspective imaging process of a checkerboard calibration plate in the environment image;

[0113] S203: Calculating the intrinsic parameter matrix and the extrinsic parameter matrix of the distortion-free imaging model;

[0114] S204: Establishing a distortion fitting model of the environment image;

[0115] S205: Determine distortion parameters corresponding to the distortion fitting model using a nonlinear fitting algorithm for the environmental image before and after distortion;

[0116] S206: Using the distortion parameters, the internal parameter matrix, and the external parameter matrix as decision variables, establishing an objective function of the distortion fitting model under regularity constraints;

[0117] S207: Optimizing the objective function using a nonlinear optimization algorithm to optimize the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix;

[0118] S208: Complete camera calibration based on the optimized intrinsic parameter matrix, extrinsic parameter matrix and distortion parameters.

[0119] The embodiments of the present specification first automatically obtain multiple environmental images generated by the camera to be calibrated, and then establish a distortion-free imaging model based on the perspective imaging process of the checkerboard calibration plate in the environmental image to determine the intrinsic parameter matrix corresponding to the camera and the extrinsic parameter matrix corresponding to the environmental image under the distortion-free condition, thereby establishing a distortion fitting model of the environmental image, and using a nonlinear fitting algorithm to determine the corresponding distortion parameters. However, since the obtained distortion parameters, intrinsic parameter matrix, and extrinsic parameter matrix are obtained by fitting using an approximate method, an objective function of the distortion fitting model under regularization constraints is established, and the objective function is optimized using a nonlinear optimization algorithm, thereby optimizing the distortion parameters, intrinsic parameter matrix, and extrinsic parameter matrix to ensure that the camera calibration accuracy is improved while ensuring the camera calibration efficiency.

[0120] It can be understood that in some embodiments, a spatial rectangular coordinate system is established with the earth plane as the XY plane; the origin of the real-space rectangular coordinate system is determined, and the longitude and latitude of the origin are measured using a GPS locator; a target reference point is selected, and the longitude and latitude of the target reference point are measured using a GPS locator; the due east direction is determined using the GPS locator, and the deflection angle between the due east direction and the positive direction of the X-axis is measured; for one or more cameras to be calibrated in the real-space rectangular coordinate system, a checkerboard calibration plate is placed in real space, and the world coordinate system is fixed on the checkerboard so that W=0 in the physical coordinates (U, V, W) of any point on the checkerboard, and the camera to be calibrated is used to shoot it at different angles to obtain multiple environmental images generated by the camera to be calibrated, thereby establishing a distortion-free imaging model and a distortion fitting model based on the imaging process, and establishing a corresponding objective function for optimizing the parameters of the distortion fitting model, which is an approximate fitting model, thereby improving the accuracy of the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix, so as to improve the accuracy of the camera calibration.

[0121] It should be noted that, in some embodiments, due to the complex perspective process of the fisheye camera and its application limitations, the camera to be calibrated is an ordinary camera, not a fisheye camera.

[0122] Refer to the attached Figure 3In some embodiments, establishing a distortion-free imaging model based on the perspective imaging process of the checkerboard calibration plate in the environment image may include:

[0123] S301: determining a perspective imaging relationship of the checkerboard calibration plate from the world coordinate system to the camera coordinate system and the pixel coordinate system in sequence according to two sets of coordinate information of the checkerboard calibration plate in the world coordinate system and the pixel coordinate system and a perspective imaging process;

[0124] S302: performing a product operation on a mapping relationship of the checkerboard calibration plate from the world coordinate system to the camera coordinate system and a mapping relationship of the checkerboard calibration plate from the camera coordinate system to the pixel coordinate system to obtain a product operation matrix;

[0125] S303: Mapping the checkerboard calibration plate from the world coordinate system to the pixel coordinate system according to the product operation matrix to establish the distortion-free imaging model.

[0126] It can be understood that in some embodiments, referring to the attached Figure 4 In the camera imaging system, there are four coordinate systems: world coordinate system, camera coordinate system, image coordinate system, and pixel coordinate system. The world coordinate system is the coordinate system in the real world. The environmental objects are first radially mapped from the world coordinate system to the camera coordinate system through rigid body transformation, and then mapped from the camera coordinate system to the image coordinate system through perspective projection, and then mapped from the image coordinate system to the pixel coordinate system through affine transformation. Among them, the product of the affine transformation matrix and the perspective projection matrix is the intrinsic parameter matrix, and the rigid body transformation matrix is the extrinsic parameter matrix. Therefore, when establishing an undistorted imaging model, it is only necessary to determine the mapping relationship of the checkerboard calibration plate from the world coordinate system to the camera coordinate system, and the mapping relationship of the checkerboard calibration plate from the camera coordinate system to the pixel coordinate system. There is no need to consider the image coordinate system, so that the undistorted imaging model can be established quickly and accurately.

[0127] Refer to the attached Figure 5 In some embodiments, before calculating the intrinsic parameter matrix and the extrinsic parameter matrix of the distortion-free imaging model, the following steps may be further included:

[0128] S501: Acquire a color calibration plate image, pixel coordinates of preset calibration points in the color calibration plate image, and three-dimensional coordinates of the preset calibration points;

[0129] S502: Inputting the color calibration plate image into a pre-trained neural network model to identify and obtain pixel coordinates of a preset area in the color calibration plate image;

[0130] S503: Establishing a coordinate mapping model between the pixel coordinates of the preset calibration point and the three-dimensional coordinates of the preset calibration point;

[0131] S504: Inputting the pixel coordinates of the preset area into the coordinate mapping model to obtain the three-dimensional coordinates of the preset area;

[0132] S505: Determine coordinate information of a plurality of target points in the checkerboard calibration plate at the same position as the color calibration plate in the world coordinate system according to the three-dimensional coordinates of the preset area.

[0133] It can be understood that in some embodiments, after establishing the distortion-free imaging model, the coordinate information of the target point of the checkerboard calibration plate in the environmental image in the world coordinate system and the pixel coordinate system can be substituted into the distortion-free imaging model to solve the intrinsic parameter matrix and the extrinsic parameter matrix. The target point of the checkerboard calibration plate can be a corner point or other point. However, manually measuring the coordinate information of the target point of the checkerboard calibration plate in the world coordinate system has the problems of poor accuracy, long time consumption and low efficiency. Therefore, a pre-trained neural network model can be used, such as the YOLOV5 target detection network. The neural network model is used to identify the pixel coordinates of a preset position in the color calibration plate image. Specifically, the color calibration plate can be a calibration plate of different colors. The calibration plate can include a checkerboard calibration plate, a circular grid calibration plate, and a color calibration plate. As well as other types of calibration plates, the preset area can be a specified position area, such as the designated area in the upper right corner. The pixel coordinates of the preset calibration point in the color calibration plate image and the three-dimensional coordinates of the preset calibration point are a small amount of coordinate information collected and measured in advance to establish a coordinate mapping model between the pixel coordinates of the preset calibration point and the three-dimensional coordinates of the preset calibration point. Using this coordinate mapping model, the three-dimensional coordinates of the preset area in the color calibration plate image (that is, the three-dimensional coordinates in the real world) can be obtained quickly and accurately. Since the position of the checkerboard calibration plate and the color calibration plate is the same, several points can be selected from the preset area in the color calibration plate image as target points in the checkerboard calibration plate, thereby quickly and accurately determining the coordinate information of multiple target points in the checkerboard calibration plate in the world coordinate system.

[0134] Refer to the attached Figure 6 In some embodiments, calculating the intrinsic parameter matrix and the extrinsic parameter matrix of the undistorted imaging model may include:

[0135] S601: Inputting coordinate information of multiple target points in the checkerboard calibration plate in the world coordinate system and coordinate information of the target points in the pixel coordinate system into the undistorted imaging model to obtain a homography matrix;

[0136] S602: Determine the constraint equation corresponding to the symmetric matrix of the intrinsic parameter matrix according to the coordinate information of the target point of the checkerboard calibration plate in the plurality of environment images in the pixel coordinate system;

[0137] S603: Based on the constraint equation, calculate and obtain the symmetric matrix using a fitting algorithm;

[0138] S604: Determine the internal parameter matrix corresponding to the symmetric matrix;

[0139] S605: Taking the product of the inverse matrix of the intrinsic parameter matrix and the homography matrix as the extrinsic parameter matrix.

[0140] It can be understood that in some embodiments, according to the camera imaging principle, when calculating the intrinsic parameter matrix and the extrinsic parameter matrix of the undistorted imaging model, it is first necessary to solve the product of the intrinsic parameter matrix and the extrinsic parameter matrix, that is, the homography matrix, and simplify the undistorted imaging model to obtain:

[0141] Z·P=A·W·R=H·R

[0142] Among them, Z is the scale factor, which is a constant, P is the coordinate information of the target point in the pixel coordinate system, H is the homography matrix, R is the coordinate information of the target point in the world coordinate system, A is the intrinsic parameter matrix, and W is the extrinsic parameter matrix.

[0143] Furthermore, in some embodiments, the scale factor can be eliminated and the homography matrix can be obtained through the coordinate information of at least 4 target points in the pixel coordinate system and the coordinate information in the world coordinate system. The reason why at least 4 target points are needed is that in the calculation process, each target point is represented by a three-dimensional vector, which can provide 2 constraint equations, and H after simplification is a homogeneous matrix containing 8 independent unknown elements, so the number of target points is at least 4.

[0144] Furthermore, in some embodiments, it is known that A·W=H, and it is necessary to solve the internal parameter matrix A, where W=(R1, R2, T), T is the translation vector, R=(R1, R2), R is the rotation matrix, R1 and R2 are the two columns of the rotation matrix respectively, and R1 and R2 are orthogonal to each other. It can be seen that R1 T R2=0,R1 T R1=R2 T R2 = 1, and H = A(R1, R2, T), the constraint equation corresponding to the symmetric matrix of the internal parameter matrix can be obtained. Specifically, the symmetric matrix of the internal parameter matrix is recorded as B = A -T A -1 , the constraint equations include: H1 T ·B·H2=0,H1 T ·B·H1=H2 T·B·H2=1, where H1 and H2 are the first two column vectors of H respectively. Since the camera intrinsic parameters are parameters that describe the inherent properties of the camera itself, including parameters such as focal length and pixel spacing, they are usually represented by the intrinsic parameter matrix. The camera extrinsic parameters are parameters that describe the position and orientation of the camera in the three-dimensional scene, usually represented by the rotation matrix and translation vector. For the same camera to be calibrated, its intrinsic parameter matrix depends on its internal parameters. No matter how the positional relationship between the checkerboard calibration plate and the camera to be calibrated changes, the intrinsic parameter matrix remains unchanged, while the extrinsic parameter matrix reflects the positional relationship between the checkerboard calibration plate and the camera to be calibrated. For different pictures, if the positional relationship between the checkerboard calibration plate and the camera to be calibrated has changed, the extrinsic parameter matrix corresponding to each picture is different. Therefore, when using the fitting algorithm to solve the symmetric matrix under the constraint equation, the coordinate information of the target point corresponding to the number of different environment images greater than the preset threshold in the world coordinate system and the pixel coordinate system is required. Usually, the preset threshold is more than 10 to ensure sufficiently accurate fitting accuracy. After solving the symmetric matrix B, B=A -T A -1 Perform the operation to obtain the internal parameter matrix A, and since W=A -1 H, so the extrinsic parameter matrix W can be calculated, thereby quickly and accurately obtaining the intrinsic parameter matrix and extrinsic parameter matrix of the distortion-free imaging model.

[0145] Refer to the attached Figure 7 In some embodiments, determining the distortion parameters corresponding to the distortion fitting model using a nonlinear fitting algorithm may include:

[0146] S701: Inputting coordinate information of multiple target points in the checkerboard calibration plate in the pixel coordinate system into the distortion fitting model to obtain multiple constraint equations constructed by distortion parameters;

[0147] S702: Fitting a plurality of constraint equations constructed by distortion parameters using a nonlinear fitting algorithm, and obtaining the distortion parameters according to the fitting results.

[0148] It can be understood that in some embodiments, the distortion fitting model maps the undistorted image coordinates to the distorted image coordinates through the distortion parameters. The distortion parameters can be regarded as unknowns. A nonlinear fitting algorithm is used to fit multiple constraint equations constructed by the distortion parameters, and the distortion parameters are obtained according to the fitting results. However, the conventional distortion fitting model only considers the radial distortion with a greater impact, and there is a defect that the distortion fitting model has a low modeling accuracy, and there is room for further optimization.

[0149] Refer to the attached Figure 8 In some embodiments, using the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix as decision variables to establish the objective function of the distortion fitting model under regularization constraints may include:

[0150] S801: Using the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix as decision variables, determining the degree of difference between the environment image before and after the distortion under the current decision variables and regularization terms corresponding to the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix;

[0151] S802: Perform weighted summation on the difference degree and the regularization term to establish the objective function.

[0152] It can be understood that, in some embodiments, the distortion fitting model under the multivariate distortion parameter can further improve the modeling accuracy of the distortion fitting model, which can be expressed as:

[0153]

[0154] Among them, (x d ,y d ) is the coordinate of the image after distortion, (x, y) is the coordinate of the image before distortion, r is the distance from the image pixel to the image center, k1, k2, k3, p1, p2, s1, s2, s3, s4 are distortion parameters.

[0155] Furthermore, in some embodiments, the objective function can be expressed as:

[0156] L=ω1×argmin w MSE(F(x d ,y d ), (x, y))+ω2×||w||

[0157] Among them, L represents the objective function, ω1 represents the first weight, argmin w Indicates that MSE(F(x d ,y d ),(x,y)) reaches the minimum value of the distortion parameter, MSE represents the mean square error, which is used to measure the difference between the environment image before and after distortion, F represents the distortion fitting model under the multivariate distortion parameter, ω2 represents the second weight, ||w|| represents the L2 norm, (x d ,y d ) is the coordinate of the image after distortion, and (x, y) is the coordinate of the image before distortion.

[0158] Refer to the attached Figure 9 In some embodiments, optimizing the objective function using a nonlinear optimization algorithm to optimize the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix may include:

[0159] S901: Use the current decision variable as the current iteration point;

[0160] S902: Constructing a similar function to the target function in the neighborhood of the current iteration point;

[0161] S903: Calculate the minimum value corresponding to the similarity function in the neighborhood, and use the minimum point corresponding to the minimum value as the next iteration point;

[0162] S904: Calculating the similarity between the target function and the similarity function according to the current iteration point and the next iteration point;

[0163] S905: If the similarity is greater than a first threshold, taking the next iteration point as the current iteration point;

[0164] S906: If the similarity is not greater than the first threshold, determining an iteration step according to the similarity, iterating the current iteration point using the iteration step, and using the iteration result as the current iteration point;

[0165] S907: Repeat the above steps of updating the current iteration point until the objective function converges.

[0166] It can be understood that in some embodiments, after the objective function is established, the distortion fitting model established in advance performs a distortion fitting operation, and initialized distortion parameters, an internal parameter matrix, and an external parameter matrix are obtained. However, the distortion fitting operation has a certain distortion fitting error. In order to further ensure the accuracy of the distortion parameters, the internal parameter matrix, and the external parameter matrix, while taking into account the optimization speed, the optimization process can be started around the initial value of the current decision variable. Specifically, the current decision variable is first used as the current iteration point. At the same time, in order to avoid local convergence and reduce the sensitivity to the initial value, a similar function is constructed in the neighborhood of the objective function in the current iteration point. It should be noted that the similarity function is composed of simple functions. Simple functions do not refer to simple functions, but correspond to concepts in real variable function theory. Then, the minimum value corresponding to the similarity function in the neighborhood is calculated, and the minimum point corresponding to the minimum value is used as the next iteration point to achieve updated iteration. In order to evaluate the effectiveness of the iteration step, the similarity between the objective function and the similarity function is calculated using the following formula:

[0167]

[0168] Among them, σ i is the similarity degree at the i-th iteration, ||L i || 2 -||L(x i +d i )|| 2 is the actual reduction of the objective function at the i-th iteration, ||L i || 2 -||Li +J i d i || 2 is the predicted reduction of the objective function at the i-th iteration, L i is the objective function value before the i-th iteration, L(x i +d i ) is the objective function value after the i-th iteration, J i d i is the predicted iterative change at the current iteration step size at the i-th iteration, d i is the iteration step size at the i-th iteration.

[0169] Furthermore, in some embodiments, if the degree of similarity is greater than a first threshold, it means that the degree of approximation between the similarity function and the objective function is high, and the current iteration is valid; otherwise, it means that the degree of approximation between the similarity function and the objective function is low, and the current iteration is not accepted, and the iteration step is adjusted according to the degree of similarity; if the degree of similarity is less than a second threshold, the iteration step is increased; if the degree of similarity is greater than the second threshold (and less than the first threshold), the iteration step is reduced to obtain an updated iteration step; thereafter, the current iteration point is iterated using the updated iteration step, and the steps of updating the current iteration point are repeated until the objective function converges, thereby quickly and accurately finding the optimal solution within the credible neighborhood to obtain the optimized distortion parameters, intrinsic parameter matrix, and extrinsic parameter matrix.

[0170] Based on the same inventive concept, some embodiments of this specification also provide a target positioning method, referring to the attached Figure 10 , the method comprising:

[0171] S1001: Receive an image to be positioned; the image to be positioned is generated by a camera calibrated using the method described in any of the above embodiments;

[0172] S1002: Input the image to be positioned into a pre-trained target positioning model to obtain a positioning result.

[0173] It should be noted that although the operations of the method of the present invention are described in a specific order in the above embodiments and drawings, this does not require or imply that these operations must be performed in this specific order, or that all illustrated operations must be performed to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0174] Corresponding to the above-mentioned camera calibration method, some embodiments of this specification also provide a camera calibration device, referring to Figure 11 As shown, in some embodiments, the apparatus may include:

[0175] An acquisition module 1101 is configured to acquire multiple environment images generated by the camera to be calibrated;

[0176] An imaging modeling module 1102 is configured to establish a distortion-free imaging model based on a perspective imaging process of a checkerboard calibration plate in the environment image;

[0177] A parameter calculation module 1103 is used to calculate the intrinsic parameter matrix and the extrinsic parameter matrix of the distortion-free imaging model;

[0178] The distortion modeling module 1104 is used to establish a distortion fitting model of the environment image;

[0179] The distortion fitting module 1105 is used to determine the distortion parameters corresponding to the distortion fitting model using a nonlinear fitting algorithm for the environmental image before and after distortion;

[0180] An objective function establishment module 1106 is configured to establish an objective function of the distortion fitting model under regularity constraints by using the distortion parameters, the internal parameter matrix, and the external parameter matrix as decision variables;

[0181] An optimization module 1107 is configured to optimize the objective function using a nonlinear optimization algorithm to optimize the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix;

[0182] The calibration module 1108 is used to complete the camera calibration according to the optimized intrinsic parameter matrix, extrinsic parameter matrix and distortion parameters.

[0183] Based on the same inventive concept, corresponding to the above-mentioned target positioning method, some embodiments of this specification further provide a target positioning device, referring to Figure 12 As shown, in some embodiments, the apparatus may include:

[0184] The receiving module 1201 is configured to receive an image to be positioned; the image to be positioned is generated by a camera calibrated using the method described in any of the above embodiments;

[0185] The positioning module 1202 is used to input the image to be positioned into a pre-trained target positioning model to obtain a positioning result.

[0186] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0187] It should be noted that in the embodiments of this specification, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user and fully authorized by all parties.

[0188] It should be noted that the computer program product described in this specification (this application) is a software product that mainly implements the method described in this specification (this application) through a computer program.

[0189] The embodiment of this specification also provides a computer device. Figure 13 As shown, in some embodiments of the present specification, the computer device 1302 may include one or more processors 1304, such as one or more central processing units (CPUs) or graphics processing units (GPUs), and each processing unit may implement one or more hardware threads. The computer device 1302 may also include any memory 1306, which is used to store any type of information such as code, settings, data, etc. In a specific embodiment, the computer program on the memory 1306 and executable on the processor 1304, when the computer program is executed by the processor 1304, can execute the instructions of the method described in any of the above embodiments. Non-limitingly, for example, the memory 1306 may include any one or more combinations of the following: any type of RAM, any type of ROM, a flash memory device, a hard disk, an optical disk, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent a fixed or removable component of the computer device 1302. In one embodiment, when the processor 1304 executes the associated instructions stored in any memory or combination of memories, the computer device 1302 can perform any operation of the associated instructions. The computer device 1302 also includes one or more drive mechanisms 1308 for interacting with any memory, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0190] The computer device 1302 may also include an input / output interface 1310 (I / O) for receiving various inputs (via input devices 1312) and for providing various outputs (via output devices 1314). A specific output mechanism may include a presentation device 1316 and an associated graphical user interface 1318 (GUI). In other embodiments, the input / output interface 1310 (I / O), input devices 1312, and output devices 1314 may not be included, and the computer device 1302 may simply function as a computer device in a network. The computer device 1302 may also include one or more network interfaces 1320 for exchanging data with other devices via one or more communication links 1322. One or more communication buses 1324 couple the components described above together.

[0191] The communication link 1322 can be implemented in any manner, for example, through a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 1322 can include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0192] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), computer-readable storage media, and computer program products of some embodiments of the present specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processor to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processor generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0193] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processor to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, the instruction device being implemented in the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0194] These computer program instructions can also be loaded onto a computer or other programmable data processor so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0195] In a typical configuration, a computer device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0196] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0197] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computer device. As defined in this specification, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0198] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0199] Embodiments of this specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. Embodiments of this specification may also be practiced in distributed computing environments where tasks are performed by remote processors connected via a communications network. In distributed computing environments, program modules may be located in local and remote computer storage media, including storage devices.

[0200] It should also be understood that in the embodiments of this specification, the term "and / or" is merely a description of the association relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0201] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0202] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiments of this specification. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0203] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A camera calibration method, characterized in that: The method comprises: Obtain multiple environment images generated by the camera to be calibrated; Establishing a distortion-free imaging model according to a perspective imaging process of a checkerboard calibration plate in the environmental image; Acquire a color calibration plate image, pixel coordinates of preset calibration points in the color calibration plate image, and three-dimensional coordinates of the preset calibration points; Inputting the color calibration plate image into a pre-trained neural network model to identify the pixel coordinates of a preset area in the color calibration plate image; Establishing a coordinate mapping model between the pixel coordinates of the preset calibration points and the three-dimensional coordinates of the preset calibration points; Inputting the pixel coordinates of the preset area into the coordinate mapping model to obtain the three-dimensional coordinates of the preset area; Determining coordinate information of a plurality of target points on the checkerboard calibration plate at the same position as the color calibration plate in a world coordinate system according to the three-dimensional coordinates of the preset area; Calculating the intrinsic parameter matrix and the extrinsic parameter matrix of the undistorted imaging model; Establishing a distortion fitting model of the environmental image; Determining distortion parameters corresponding to the distortion fitting model using a nonlinear fitting algorithm for the environmental image before and after distortion; Taking the distortion parameters, the internal parameter matrix and the external parameter matrix as decision variables, an objective function of the distortion fitting model under regularity constraints is established; Take the current decision variable as the current iteration point; Constructing a similar function to the target function in the neighborhood of the current iteration point; Calculate the minimum value corresponding to the similarity function in the neighborhood, and use the minimum point corresponding to the minimum value as the next iteration point; Calculating the similarity between the target function and the similarity function according to the current iteration point and the next iteration point; If the similarity is greater than a first threshold, taking the next iteration point as the current iteration point; If the similarity is not greater than a first threshold, determining an iteration step length according to the similarity, iterating the current iteration point using the iteration step length, and using an iteration result as the current iteration point; Repeat the above steps of updating the current iteration point until the objective function converges to optimize the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix; The camera calibration is completed based on the optimized intrinsic parameter matrix, extrinsic parameter matrix and distortion parameters.

2. The method according to claim 1, characterized in that Establishing a distortion-free imaging model according to a perspective imaging process of a checkerboard calibration plate in the environment image includes: Determine, based on two sets of coordinate information of the checkerboard calibration plate in the world coordinate system and the pixel coordinate system and the perspective imaging process, a perspective imaging relationship of the checkerboard calibration plate mapped from the world coordinate system to the camera coordinate system and the pixel coordinate system in sequence; Performing a product operation on a mapping relationship of the checkerboard calibration plate from the world coordinate system to the camera coordinate system and a mapping relationship of the checkerboard calibration plate from the camera coordinate system to the pixel coordinate system to obtain a product operation matrix; The checkerboard calibration plate is mapped from the world coordinate system to the pixel coordinate system according to the product operation matrix to establish the distortion-free imaging model.

3. The method according to claim 2, characterized in that Calculating the intrinsic parameter matrix and the extrinsic parameter matrix of the distortion-free imaging model includes: Inputting coordinate information of multiple target points in the checkerboard calibration plate in the world coordinate system and coordinate information of the target points in the pixel coordinate system into the undistorted imaging model to obtain a homography matrix; Determining the constraint equation corresponding to the symmetric matrix of the intrinsic parameter matrix according to the coordinate information of the target point of the checkerboard calibration plate in the plurality of environmental images in the pixel coordinate system; Based on the constraint equation, the symmetric matrix is calculated using a fitting algorithm; Determine the internal parameter matrix corresponding to the symmetric matrix; The product of the inverse matrix of the intrinsic parameter matrix and the homography matrix is used as the extrinsic parameter matrix.

4. The method according to claim 2, characterized in that Determining the distortion parameters corresponding to the distortion fitting model using a nonlinear fitting algorithm includes: Inputting coordinate information of a plurality of target points in the checkerboard calibration plate in the pixel coordinate system into the distortion fitting model to obtain a plurality of constraint equations constructed by distortion parameters; A nonlinear fitting algorithm is used to fit a plurality of constraint equations constructed by the distortion parameters, and the distortion parameters are obtained according to the fitting results.

5. The method according to claim 4, characterized in that The distortion parameters, the internal parameter matrix, and the external parameter matrix are used as decision variables to establish the objective function of the distortion fitting model under regularization constraints, including: Using the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix as decision variables, determining the degree of difference between the environment image before and after the distortion under the current decision variables and the regularization terms corresponding to the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix; The difference degree and the regularization term are weightedly summed to establish the objective function.

6. A target positioning method, characterized in that: The method comprises: Receiving an image to be positioned; the image to be positioned is generated by a camera calibrated using the method according to any one of claims 1 to 5; The image to be positioned is input into a pre-trained target positioning model to obtain a positioning result.

7. A camera calibration device, characterized in that: The device comprises: An acquisition module is used to acquire multiple environment images generated by the camera to be calibrated; An imaging modeling module, configured to establish a distortion-free imaging model based on a perspective imaging process of a checkerboard calibration plate in the environmental image; A parameter calculation module is used to obtain a color calibration plate image, pixel coordinates of preset calibration points in the color calibration plate image, and three-dimensional coordinates of the preset calibration points; Inputting the color calibration plate image into a pre-trained neural network model to identify the pixel coordinates of a preset area in the color calibration plate image; Establishing a coordinate mapping model between the pixel coordinates of the preset calibration points and the three-dimensional coordinates of the preset calibration points; Inputting the pixel coordinates of the preset area into the coordinate mapping model to obtain the three-dimensional coordinates of the preset area; Determining coordinate information of a plurality of target points on the checkerboard calibration plate at the same position as the color calibration plate in a world coordinate system according to the three-dimensional coordinates of the preset area; Calculating the intrinsic parameter matrix and the extrinsic parameter matrix of the undistorted imaging model; A distortion modeling module, used to establish a distortion fitting model of the environment image; A distortion fitting module is used to determine the distortion parameters corresponding to the distortion fitting model using a nonlinear fitting algorithm for the environmental image before and after distortion; An objective function establishment module is used to establish an objective function of the distortion fitting model under regularity constraints by taking the distortion parameters, the internal parameter matrix and the external parameter matrix as decision variables; An optimization module is used to take the current decision variable as the current iteration point; Constructing a similar function to the target function in the neighborhood of the current iteration point; Calculate the minimum value corresponding to the similarity function in the neighborhood, and use the minimum point corresponding to the minimum value as the next iteration point; Calculating the similarity between the target function and the similarity function according to the current iteration point and the next iteration point; If the similarity is greater than a first threshold, taking the next iteration point as the current iteration point; If the similarity is not greater than a first threshold, determining an iteration step length according to the similarity, iterating the current iteration point using the iteration step length, and using an iteration result as the current iteration point; Repeat the above steps of updating the current iteration point until the objective function converges to optimize the distortion parameters, the intrinsic parameter matrix, and the extrinsic parameter matrix; The calibration module is used to complete the camera calibration based on the optimized intrinsic parameter matrix, extrinsic parameter matrix and distortion parameters.

8. A target positioning device, characterized in that: The device comprises: A receiving module, configured to receive an image to be positioned; the image to be positioned is generated by a camera calibrated using the method according to any one of claims 1 to 5; The positioning module is used to input the image to be positioned into a pre-trained target positioning model to obtain a positioning result.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: When the computer program is executed by the processor, the computer program executes the instructions of the method according to any one of claims 1 to 6.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor of a computer device, the computer program executes the instructions of the method according to any one of claims 1 to 6.

11. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the computer program executes instructions of the method according to any one of claims 1 to 6.

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