A stereo calibration method, storage medium and terminal

Abstract

This invention discloses a stereo calibration method, storage medium, and terminal, belonging to the field of camera calibration technology. It introduces new error factors into the radial and / or tangential distortion parameters of the distortion model to update the distortion parameters. By adding new error factors to the radial and / or tangential distortion parameters of the distortion model, this invention can correct the distortion parameters (radial and tangential distortion parameters), thereby optimizing the distortion parameters and providing a more accurate distortion model that better fits the camera distortion.

Description

Technical Field

[0001] This invention relates to the field of camera calibration technology, and in particular to a stereo calibration method, storage medium, and terminal. Background Technology

[0002] In image measurement and machine vision applications, to determine the 3D geometric position of a point on the surface of a spatial object and its corresponding point in the image, a geometric model of camera imaging must be established. These geometric model parameters are the camera parameters. Under most conditions, these parameters must be obtained through experimentation and calculation; this process of solving for the parameters is called camera calibration. Whether in image measurement or machine vision applications, camera parameter calibration is a crucial step, as the accuracy of the calibration results and the stability of the algorithm directly affect the accuracy of the camera's output. Therefore, proper camera calibration is a prerequisite for subsequent work, and improving calibration accuracy is a key focus of research. Traditional stereo calibration methods are generally based on the "Zhang Zhengyou" calibration method, which simulates the distortion model based on the first few terms of the Taylor series expansion. Since the Taylor series expansion is only an approximate solution, it introduces a certain degree of error into the entire distortion model. Summary of the Invention

[0003] The purpose of this invention is to provide a stereo calibration method, storage medium, and terminal to provide a more accurate distortion model.

[0004] The objective of this invention is achieved through the following technical solution: a stereo calibration method, which includes a distortion correction step:

[0005] New error factors are introduced into the radial and / or tangential distortion parameters of the distortion model to update the distortion parameters.

[0006] In one example, the updated distortion parameters include:

[0007] Preliminary calibration is performed based on the distortion model, and the radial and tangential distortion parameters are solved.

[0008] Introduce new error factors into the radial and / or tangential distortion parameters of the distortion model, and then perform multiple calibrations to solve for the error factors.

[0009] The corresponding radial distortion parameter and / or tangential distortion parameter are replaced by the error factor, the radial distortion parameter and / or tangential distortion parameter corresponding to the error factor, to obtain the final distortion model based on the final radial distortion parameter and / or the final tangential distortion parameter.

[0010] In one example, during the initial calibration based on the distortion model, the distortion model is:

[0011]

[0012] Where x and y are the x and y coordinates of a pixel in the distortion-free normalized pixel plane, respectively; x′ and y′ are the x and y coordinates of a pixel after distortion; and r is the radius of curvature, and r = r 2 =x 2 +y 2 k1 and k2 are radial distortion parameters; p1 and p2 are tangential distortion parameters.

[0013] In one example, when new error factors are introduced into both the radial and tangential distortion parameters of the distortion model, the expression for the distortion model becomes:

[0014]

[0015] Where x and y are the x and y coordinates of a pixel in the distortion-free normalized pixel plane, respectively; x′ and y′ are the x and y coordinates of a pixel after distortion; and r is the radius of curvature, and r = r 2 =x 2 +y 2 k1, k2 are radial distortion parameters; p1, p2 are tangential distortion parameters; Δδ, Δγ, Δα, Δβ are error factors.

[0016] In one example, the calibration method includes:

[0017] Establish a camera imaging model and, based on the mapping relationship between the four coordinate systems, establish the mapping relationship from spatial points to the two-dimensional plane;

[0018] Establish a camera distortion model;

[0019] New error factors are introduced into the radial and / or tangential distortion parameters of the distortion model to update the distortion parameters.

[0020] In one example, the step of introducing new error factors into the radial and / or tangential distortion parameters of the distortion model to update the distortion parameters specifically includes:

[0021] Establish a world coordinate system;

[0022] Call OpenCV's corner detection function to obtain the pixel coordinates of the corner points;

[0023] Call the calibration function;

[0024] Distortion correction is performed to obtain the final distortion model, and then the final distortion parameters are obtained.

[0025] In one example, the method further includes:

[0026] The calibration scenario is divided into multiple areas, so that each area completely covers the calibration board;

[0027] In each region, multiple calibration images with different poses are collected, and calibration processing is performed based on the calibration images to obtain the final distortion parameters.

[0028] In one example, when dividing the calibration scene, the calibration scene is divided into nine regions using a 3x3 grid.

[0029] It should be further noted that the technical features corresponding to the above examples can be combined or substituted to form new technical solutions.

[0030] The present invention also includes a storage medium storing computer instructions thereon, which, when executed, perform the steps of the stereo calibration method formed by any or more of the above examples.

[0031] The present invention also includes a terminal comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, and the processor executes the steps of the stereo calibration method formed by any or more of the above examples when executing the computer instructions.

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] 1. In one example, the present invention adds a new error factor to the radial distortion parameters and / or tangential distortion parameters of the distortion model, which can correct the distortion parameters (radial distortion parameters and tangential distortion parameters) and thus optimize the distortion parameters, thereby providing a more accurate distortion model that better fits the camera distortion.

[0034] 2. In one example, the error factor introduced in this invention is used to iteratively calculate the distortion parameters. That is, after the initial calibration is completed, the distortion parameters obtained from the initial calibration are iteratively calculated to achieve the purpose of parameter optimization.

[0035] 3. In one example, the present invention divides the calibration scene into multiple regions, and each region can completely cover the calibration board. Based on this, calibration images of different postures are collected, which can fully calibrate in all directions, thereby improving the calibration accuracy. Attached Figure Description

[0036] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, which are used to provide a further understanding of the present application and constitute a part of the present application. The same reference numerals are used in these drawings to denote the same or similar parts. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application.

[0037] Figure 1 A diagram showing the transformation relationship between the world coordinate system and the camera coordinate system;

[0038] Figure 2 A diagram showing the transformation relationship between the camera coordinate system and the image coordinate system;

[0039] Figure 3 A diagram showing the transformation relationships between the four coordinate systems;

[0040] Figure 4 This is a normalized planar schematic diagram;

[0041] Figure 5 This is a schematic diagram of two types of distortion in radial distortion;

[0042] Figure 6 This is a schematic diagram of tangential distortion;

[0043] Figure 7 This is a flowchart of distortion correction in one example of the present invention;

[0044] Figure 8 The calibration experiment diagrams are for the traditional maximum likelihood estimation method and the method with error factor distortion model.

[0045] Figure 9 A diagram showing the division of the calibration area for the "nine-square grid" calibration method;

[0046] Figure 10 The following are attitude simulation diagrams corresponding to the nine-square grid method, the five-position calibration method, and the random calibration method;

[0047] Figure 11 The calibration error diagrams are shown for the nine-square grid method, the five-position calibration method, and the random calibration method.

[0048] Figure 12 The calibration results are shown for the nine-square grid method, the five-position calibration method, and the random calibration method.

[0049] Figure 13 The images show the calibration attitude simulations using the traditional method and the improved nine-square grid method based on the error factor of this invention, respectively.

[0050] Figure 14 The calibration error diagrams are shown for the traditional method and the improved method of adding error factors and nine squares according to the present invention. Detailed Implementation

[0051] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] In the description of this invention, it should be noted that the directions or positional relationships indicated by terms such as "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer" are based on the directions or positional relationships shown in the accompanying drawings. They are used only for the convenience of describing this invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention. Furthermore, the use of ordinal numbers (e.g., "first and second," "first to fourth," etc.) is for distinguishing objects and is not limited to this order, and should not be construed as indicating or implying relative importance.

[0053] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0054] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0055] Before proceeding with calibration, the camera imaging model will be explained. Specifically, from the perspective of 3D reconstruction, it will be explained how to map a 2D photograph from the camera to 3D space. The camera model formula is as follows:

[0056]

[0057] In the camera model, spatial points are captured by the camera and eventually transformed into points on the screen. This process involves transformations from the world coordinate system (3D) to the camera coordinate system (3D), the image coordinate system (2D), and the pixel coordinate system. The definitions of each coordinate system are as follows.

[0058] (1) World Coordinate System

[0059] A three-dimensional coordinate system established with a point in the outside world as a reference point to express three-dimensional spatial coordinates, denoted as (X). w ,Y w Z w );

[0060] (2) Camera coordinate system

[0061] A coordinate system is established with the camera's optical center as the origin and the camera's focal length as the Z-axis pointing directly in front of the camera.

[0062] (3) Image coordinate system

[0063] The coordinate system established by projecting the optical center onto the imaging plane, i.e., the center point, is called the image coordinate system;

[0064] (4) Pixel coordinate system

[0065] What the end user sees is the origin of the coordinate system at the top left corner of the image, measured in pixels.

[0066] The following section derives the transformation relationship from the world coordinate system to the pixel coordinate system through formula derivation. This is a necessary process for camera calibration and subsequent stereo matching and 3D reconstruction.

[0067] (1) World coordinate system → Camera coordinate system

[0068] like Figure 1 As shown, we first assume that the coordinates of a point in space in the world coordinate system are P. W =[X W ,Y w Z w ] T This point is transformed to coordinate P in the camera coordinate system. W =[X W ,Y w Z w ] T The transformation is shown in equation (1.1.1). To ensure consistent calculation, the formula is written in homogeneous form.

[0069]

[0070] (2) Camera coordinate system → Image coordinate system

[0071] like Figure 2 As shown, the coordinates of point P in the camera coordinate system are P_i. C =[X C ,Y C Z C ] T The coordinates of this point in the image coordinate system are P. i =[x i ,y i ] T It is a mapping from three-dimensional space to a two-dimensional plane. Based on the equivalent triangle relationship, we can obtain the following relationship:

[0072]

[0073] Written in homogeneous form as follows:

[0074]

[0075] (3) Image coordinate system → pixel coordinate system

[0076] Let u and v denote the column number and row number of the pixel, then the following relationship holds:

[0077] (1) The origin of the image coordinate system is denoted as o(u0,v0) in the pixel coordinate system uv.

[0078] (2) A certain image point p in the image coordinate system is denoted as p(u,v) in the pixel coordinate system.

[0079] Furthermore, by denoteing dx and dy as the physical dimensions of each pixel along the x-axis and y-axis respectively, the following relationship can be obtained:

[0080]

[0081] Rewriting equation (1.1.4) in homogeneous form, we have:

[0082]

[0083] Finally, the transformation relationships between the four coordinate systems are summarized as follows: Figure 3 As shown, there are rigid body transformations involving rotation and translation, similar triangle relationships, and translation scaling with different units.

[0084] The above description of the camera imaging model is only applicable under the condition that the camera has no errors. However, real-world cameras all have a certain degree of distortion. To correct this distortion, a distortion correction model needs to be introduced. The specific processing method is as follows:

[0085] First, the plane needs to be normalized.

[0086] like Figure 4 As shown, the normalized plane refers to normalizing the distance f between the imaging plane and the optical center to 1 in the imaging model. Therefore, the original camera model formula can be rewritten as:

[0087]

[0088] After normalizing the plane, the distortion needs to be added to the model, and the subsequent calibration needs to calibrate these distortions as well.

[0089] Camera distortion mainly includes two types: radial distortion and tangential distortion. Radial distortion occurs because, during camera manufacturing, limitations in technology and processes make it difficult to ensure perfectly uniform lens thickness. When a camera lens is thicker in the center and thinner at the edges, light rays are more severely distorted and deviated as they move away from the lens center, resulting in barrel distortion. Conversely, if the camera lens is thinner in the center and thicker at both ends, it results in pincushion distortion. A diagram illustrating radial distortion is shown below. Figure 5As shown.

[0090] The radial distortion model is simulated based on the first few terms of the Taylor expansion. This model mainly has three radial distortion parameters: k1, k2, and k3. In the normalized plane, its mathematical formula is described as follows:

[0091]

[0092] Where: r is the radius of curvature, and r 2 =x 2 +y 2 x, y are the pixel coordinates in the distortion-free normalized pixel plane; x′, y′ are the pixel coordinates after distortion occurs; usually only the two distortion parameters k1 and k2 are considered, unless it is a lens with more severe distortion, such as a fisheye lens, in which case the distortion parameter k3 will be used.

[0093] Besides radial distortion, another common type of distortion is tangential distortion. Tangential distortion is caused by a misalignment between the image plane and the lens plane during camera manufacturing, resulting in perspective transformation. Figure 6 As shown.

[0094] The mathematical expression for tangential distortion is given by the following formula. Similar to radial distortion, tangential distortion coefficients p1 and p2 are introduced. This distortion is related to the distance radius r from the image center:

[0095]

[0096] Combining radial and tangential distortions, the overall distortion model expression is as follows:

[0097]

[0098] The above distortion model is based on the traditional calibration method ("Zhang Zhengyou" calibration method). However, for increasingly sophisticated 3D reconstruction, simulating the distortion model based on the first few terms of the Taylor series still introduces some errors, as the Taylor series is only an approximate solution. To address this issue, this invention provides a stereo calibration method aimed at providing a more accurate distortion model. Specifically, this stereo calibration method mainly improves the distortion correction steps, which include:

[0099] New error factors are introduced into the radial and / or tangential distortion parameters of the distortion model to update the distortion parameters. Preferably, new error factors are introduced into both the radial and tangential distortion parameters of the distortion model. In this case, the final distortion parameters are the sum of the error factors and the corresponding radial distortion parameters, and the sum of the error factors and the corresponding tangential distortion parameters. Based on this, the error factors are solved using multiple calibration images to further correct the initially calibrated radial and tangential distortion parameters, thereby optimizing the distortion parameters, providing a more accurate distortion model, and better fitting the camera distortion.

[0100] In one example, such as Figure 7 As shown, updating the distortion parameters includes:

[0101] S1': Based on the distortion model, preliminary calibration is performed, and radial distortion parameters and tangential distortion parameters are solved;

[0102] S2': Introduce new error factors into the radial distortion parameters and / or tangential distortion parameters of the distortion model, and then perform multiple calibrations to solve for the error factors; preferably, new error factors are introduced into both the radial distortion parameters and the tangential distortion parameters of the distortion model.

[0103] S3': Replace the corresponding radial distortion parameter and / or the corresponding tangential distortion parameter with the error factor, the radial distortion parameter and / or the tangential distortion parameter corresponding to the error factor, to obtain the final distortion model based on the final radial distortion parameter and / or the final tangential distortion parameter.

[0104] Specifically, the distortion model in step S1' is shown in Equation 0.0.9 above. At this time, the radial distortion parameters k1, k2 and the tangential distortion parameters p1, p2 can be solved based on the preliminary calibration image.

[0105] In this example, an error factor is introduced to iteratively calculate the distortion parameters obtained after the initial calibration. That is, after the initial calibration is completed, the distortion parameters obtained from the initial calibration are iteratively calculated again to achieve parameter optimization. It should be further noted that the method of correcting distortion parameters by adding an error factor in this invention is simpler and significantly reduces computational difficulty compared to existing methods such as maximum likelihood estimation. Furthermore, the coefficients corresponding to the radial distortion parameters and tangential distortion parameters in the distortion model have different power series (e.g., r...). 2 r 4 The method described in this invention (x and y) is a non-linear fit, not a linear fit. Therefore, compared to updating the distortion parameters by taking the mean value after multiple calibrations (linear fit), the method of this invention has higher correction accuracy.

[0106] In one example, when new error factors are introduced into both the radial and tangential distortion parameters of the distortion model, the final expression for the distortion model is:

[0107]

[0108] Where Δδ, Δγ, Δα, and Δβ are error factors. At this point, the final radial distortion parameters are k1+Δδ and k2+Δγ; the final tangential distortion parameters are p1+Δα and p2+Δβ.

[0109] In one example, the calibration method specifically includes the following steps:

[0110] S11: Establish a camera imaging model and, based on the mapping relationship between the four coordinate systems, establish a mapping relationship from spatial points to a two-dimensional plane;

[0111] S12: Establish a camera distortion model;

[0112] S13: Introduce new error factors into the radial and / or tangential distortion parameters of the distortion model to update the distortion parameters.

[0113] In one example, the specific steps for updating the distortion parameters by introducing new error factors into the radial and / or tangential distortion parameters of the distortion model are as follows:

[0114] S131: Establish a world coordinate system with the top left corner of the chessboard as the origin;

[0115] S132: Call OpenCV's corner detection function to obtain the pixel coordinates of the corner points;

[0116] S133: Call the calibration function cv2.calibrateCamera;

[0117] S134: Call the distortion correction function cv2.undistort to perform distortion correction processing (i.e. steps S1'-S3'), solve the final distortion model, and then obtain the final distortion parameters.

[0118] In one example, calling OpenCV's corner detection function to obtain the pixel coordinates of corners includes:

[0119] The first detection function, cv2.findChessboardCorners, is called to perform initial corner detection.

[0120] Call the second detection function cv2.cornerSubPix to obtain the sub-pixel coordinates of the corner point.

[0121] To illustrate the technical advantages of the method of this invention, five calibration experiments were conducted using the traditional maximum likelihood estimation method and the distortion model method with added error factors, as follows: Figure 8 As shown in the figure, the experimental results indicate that the method of the present invention has smaller reprojection error and the advantages of simpler calculation and better accuracy.

[0122] Few studies have addressed the placement of the calibration plate; however, the placement of the calibration plate can actually affect the accuracy of subsequent calibrations. To minimize this problem, this invention proposes a calibration method for setting the position of the calibration plate, comprising the following steps:

[0123] S31: Divide the calibration scene into at least six areas, so that each area completely covers the calibration board;

[0124] S32: Within each region, acquire multiple calibration images in different poses, and perform calibration processing based on these images to obtain the final distortion parameters. Specifically, within each region, acquire at least three calibration images in different poses to calculate the camera's intrinsic and extrinsic parameters.

[0125] In this example, the calibration scene is divided into multiple regions, and each region can completely cover the calibration board. Based on this, calibration images of different poses are collected, which can fully calibrate in all directions, effectively avoid camera distortion errors, and thus improve calibration accuracy.

[0126] Preferably, the calibration scene is divided into nine regions using a 3x3 grid, i.e., the "3x3 grid" calibration method. The calibration regions are divided as follows: Figure 9 As shown, the calibration plate will be placed in the 3x3 grid positions of the image. Furthermore, the calibration device is a planar black and white checkerboard, the parameters of which are shown in Table 1.

[0127] Table 1 Calibration Plate Parameters

[0128]

[0129] To illustrate the beneficial effects of the nine-grid calibration method of this invention, three comparative experiments will be conducted below. The first experiment used the proposed nine-grid calibration method, collecting 27 images (3 images per location) from nine locations. The second experiment collected 15 images (3 images per location) from five locations. The third method involved collecting 27 images from random locations. All three experiments used the MATLAB calibration toolbox for calibration. Figure 10 (a)-(c) are attitude simulation diagrams from three calibrations, where the units for the x-axis, y-axis, and z-axis are all millimeters; Figure 11 The error plots for the three calibrations are shown in Table 2. Figure 12 As shown:

[0130] Table 2 Statistical table of three calibration results

[0131]

[0132] Based on the above experiments, it can be seen that the nine-grid calibration method of the present invention can further reduce the average error and avoid the problem of reduced calibration accuracy caused by camera distortion error.

[0133] Furthermore, to illustrate the technical effects of the distortion parameter correction method with added error factor and the nine-square grid calibration method of the present invention, a calibration experiment was conducted. The calibration experiment was performed twice, using both the traditional method and the improved nine-square grid method with added error factor proposed in this invention. Figure 13 As shown, where Figure 13 (a) is the calibration method of the present invention. Figure 13 (b) is the traditional calibration method. Figure 13 The units for the x, y, and z axes are all millimeters; the calibration results are for example... Figure 14 As shown, the horizontal axis represents the image, and the vertical axis represents the reprojection error, where... Figure 14 (a) is the calibration method of the present invention, and its reprojection error is 0.0520; Figure 14 (b) shows the traditional calibration method, with a reprojection error of 0.0576. The comparison results demonstrate that the improved calibration method achieves a 9.7% increase in accuracy compared to the traditional method, effectively improving the calibration results.

[0134] This application also includes a storage medium having the same inventive concept as the stereo calibration method comprising any or more of the above examples, wherein computer instructions are stored thereon, which, when executed, perform the steps of the stereo calibration method described above.

[0135] Based on this understanding, the technical solution of this embodiment, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0136] This application also includes a terminal having the same inventive concept as the stereo calibration method comprising any or more of the above examples, including a memory and a processor. The memory stores computer instructions executable on the processor, and the processor executes the steps of the stereo calibration method described above when executing the computer instructions. The processor may be a single-core or multi-core central processing unit or a specific integrated circuit, or one or more integrated circuits configured to implement the present invention.

[0137] In the embodiments provided by the present invention, each functional unit can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0138] The above detailed embodiments are a description of the present invention. It should not be considered that the specific embodiments of the present invention are limited to these descriptions. For those skilled in the art, several simple deductions and substitutions can be made without departing from the concept of the present invention, and all of these should be considered to fall within the protection scope of the present invention.

Claims

1. A stereo calibration method, characterized in that: It includes distortion correction steps: New error factors are introduced into the radial and / or tangential distortion parameters of the distortion model to update the distortion parameters; The updated distortion parameters include: Preliminary calibration is performed based on the distortion model, and the radial and tangential distortion parameters are solved. Introduce new error factors into the radial and / or tangential distortion parameters of the distortion model, and then perform multiple calibrations to solve for the error factors. The corresponding radial distortion parameter and / or the corresponding tangential distortion parameter are replaced by the error factor, the radial distortion parameter and / or the tangential distortion parameter corresponding to the error factor, to obtain the final distortion model based on the final radial distortion parameter and / or the final tangential distortion parameter. When new error factors are introduced into both the radial and tangential distortion parameters of the distortion model, the expression for the distortion model becomes: Where x and y are the x and y coordinates of a pixel in the distortion-free normalized pixel plane, respectively; x′ and y′ are the x and y coordinates of a pixel after distortion; and r is the radius of curvature, and r = r 2 =x 2 +y 2 k1, k2 are radial distortion parameters; p1, p2 are tangential distortion parameters; Δδ, Δγ, Δα, Δβ are error factors; The method further includes: The calibration scenario is divided into multiple areas, so that each area completely covers the calibration board; In each region, multiple calibration images with different poses are collected, and calibration processing is performed based on the calibration images to obtain the final distortion parameters.

2. The stereo calibration method according to claim 1, characterized in that: When performing preliminary calibration based on the distortion model, the distortion model is as follows: Where x and y are the x and y coordinates of a pixel in the distortion-free normalized pixel plane, respectively; x′ and y′ are the x and y coordinates of a pixel after distortion; and r is the radius of curvature, and r = r 2 =x 2 +y 2 k1 and k2 are radial distortion parameters; p1 and p2 are tangential distortion parameters.

3. The stereo calibration method according to claim 1, characterized in that: The method includes: Establish a camera imaging model and, based on the mapping relationship between the four coordinate systems, establish the mapping relationship from spatial points to the two-dimensional plane; Establish a camera distortion model; New error factors are introduced into the radial and / or tangential distortion parameters of the distortion model to update the distortion parameters.

4. The stereo calibration method according to claim 3, characterized in that: The method of introducing new error factors into the radial and / or tangential distortion parameters of the distortion model to update the distortion parameters specifically includes: Establish a world coordinate system; Call OpenCV's corner detection function to obtain the pixel coordinates of the corner points; Call the calibration function; Distortion correction is performed to obtain the final distortion model, and then the final distortion parameters are obtained.

5. The stereo calibration method according to claim 1, characterized in that: When dividing the calibration scene, the calibration scene is divided into nine areas using a nine-square grid.

6. A storage medium storing computer instructions thereon, characterized in that: The computer instructions are executed by the processor to perform the steps of the stereo calibration method according to any one of claims 1-5.

7. A terminal comprising a memory and a processor, wherein the memory stores computer instructions executable on the processor, characterized in that: When the processor executes the computer instructions, it performs the steps of the stereo calibration method according to any one of claims 1-5.

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