Optical slice plane calibration method based on regional multi-modal mapping

Abstract

The invention provides an optical slice plane calibration method based on regional multi-modal mapping, and relates to the field of optical plane calibration, and the method comprises the steps: obtaining an inner corner point of an optical slice plane in a pixel coordinate system of a CCD imaging surface; dimensionality reduction is carried out by constructing world coordinates of inner corner points on an optical slice plane; world coordinate values of the inner corner points are calculated; extracting sub-pixel point coordinates of inner angular points on the CCD imaging surface; carrying out region division on a CCD imaging surface, and calculating the distance from the coordinates of the sub-pixel points to an imaging center by combining the coordinates of the sub-pixel points; according to the distance and the world coordinate values of the interior corner points, carrying out region distribution on the interior corner point group; constructing a multi-modal flexible mapping model based on region allocation; establishing a flexible mapping relation from a distorted CCD imaging surface to an optical slice plane in different areas through a multi-mode flexible mapping model; and high-precision calibration of the optical slice plane is realized through the flexible mapping relation. According to the technical scheme, the light plane calibration precision is improved.

Description

Technical Field

[0001] This application relates to the field of optical plane calibration, and in particular to an optical slice plane calibration method based on region multimodal mapping. Background Technology

[0002] Optical sectioning visual measurement technology is a typical non-contact 3D measurement method. Its core idea is to use a laser or other light source to create an optical section that illuminates the surface of the object being measured. An imaging system then captures the cross-sectional contour of the optical section on the object's surface. Combined with a camera imaging model and calibration methods, high-precision reconstruction of the object's 3D shape is achieved. Optical sectioning visual measurement exhibits unique advantages in fields such as industrial manufacturing, quality inspection, life sciences, and cultural relic preservation. For example, in the inspection of mechanical parts, it can quickly and non-destructively acquire 3D information of complex surfaces; in biomedicine, it can be used for 3D imaging of skeletal structures; and in cultural relic preservation, it enables fine scanning and digital archiving of fragile artifact surfaces.

[0003] The optical slicing measurement system consists of a camera and an optical slicing emitter, which are rigidly connected and fixed in relative position. Camera module imaging system calibration is well-established; this mainly involves calculating the intrinsic and extrinsic parameter matrices and distortion parameters of the imaging system, with the Zhang Zhengyou calibration method being a classic example. However, optical plane calibration typically relies on camera module calibration. Using the intrinsic and extrinsic parameter matrices and distortion parameters obtained from camera calibration, the optical plane equation in the camera coordinate system is calculated, achieving the transformation between the imaging system's pixel coordinates and physical space coordinates. Traditional optical plane calibration methods include wire-drawing calibration, serrated target calibration, and mechanical adjustment. However, these methods are limited by the precision of the instruments and the need for high-precision motion control platforms, and the calibration process is cumbersome, preventing the achievement of high accuracy.

[0004] In his presentation "A flexible new technique for camera calibration" at the conference "TPAMI, 22(11):1330-1334, 2000", Zhang Z proposed a method for calibrating camera internal parameters based on a coplanar calibration reference, which solves the problem of on-site calibration of camera internal parameters. The process requires the calculation of internal and external parameter matrices and camera distortion coefficients.

[0005] The calibration of optical slices requires the internal and external parameters of the calibrated imaging system. Through linear mapping transformation, the pixel coordinates in the imaging system are converted to physical space coordinates, and the equation of the optical plane of the optical slice in physical space is calculated. Chen Tianfei et al. proposed a two-step calibration method by controlling the sensor to perform several translational movements. This method is flexible and fast, but errors caused by translational movements still exist, reducing the robustness of the measurement system. Xie et al. proposed a new method for simultaneously calibrating the internal and external parameters of the structured light sensor. Based on the precise position before and after movement using a coordinate measuring machine (CMM), the three-dimensional coordinates of the collinear point between the laser plane and the target plane are obtained. The external and internal parameters are calculated in the CMM's coordinate system. This method has a measurement error between 0.013mm and 0.014mm, but the calibration process requires precise positioning using a CMM, increasing the calibration cost. Furthermore, both of the above optical slice calibration methods first require separate calibration of the camera, inevitably involving the calculation of camera distortion parameters, increasing the computational load. Then, based on the camera parameters after distortion correction, coordinate mapping from the imaging plane to the optical slice plane is performed.

[0006] In summary, most current optical slicing vision measurement systems undergo two-step calibration: camera calibration and optical plane calibration. These two steps require obtaining the camera's intrinsic and extrinsic parameters, as well as the structural parameters of the optical slice. The intrinsic and extrinsic parameter matrices participate in the continuous transformation from pixel coordinates to physical space coordinates, which also affects the final measurement results. Furthermore, the extraction of the optical fringe center point is subject to computational errors caused by image processing algorithms. Therefore, to address the problems of insufficient calibration points, poor robustness, and low calibration accuracy in current calibration methods, and the need for separate calibration of imaging units within the measurement system, this paper proposes an optical slice plane calibration method based on regional multimodal flexible mapping. Summary of the Invention

[0007] The purpose of this invention is to provide a light slice plane calibration method based on regional multimodal mapping to address the problems of insufficient calibration points, poor robustness, low calibration accuracy, and the need for separate calibration of imaging units in the measurement system in current calibration methods.

[0008] The above-mentioned objective of this application is achieved through the following technical solution: S1: The spatial position and spatial angle between the fixed camera and the light slice emitter are rigidly connected; S2: Adjust the optical slice plane and the calibration plate plane to coincide in physical space; S3: Capture a high-definition checkerboard plane using the imaging unit CCD camera, and obtain the inner corner points of the light slice plane in the pixel coordinate system of the CCD imaging surface; S4: Dimensionality reduction is achieved by constructing the world coordinates of the interior corner points on the light slice plane; S5: Calculate the world coordinates of the interior corner points; S6: Extract the sub-pixel coordinates of the inner corner points on the CCD imaging surface; S7: Divide the CCD imaging surface into regions, and calculate the distance from the sub-pixel coordinates to the imaging center by combining the sub-pixel coordinates; allocate regions to the inner corner point group by using the distance and the world coordinate values ​​of the inner corner points; S8: Construct a multimodal flexible mapping model based on region allocation; establish a flexible mapping relationship between the distorted CCD imaging surface and the optical slicing plane in different regions through the multimodal flexible mapping model; achieve high-precision calibration of the optical slicing plane through the flexible mapping relationship.

[0009] Optionally, step S4 includes: S41: Set the world coordinate system on the checkerboard plane; define The plane coincides with the chessboard plane. The axis is parallel to the normal vector of the checkerboard plane. ; S42: Obtain the light slice plane in the world coordinate system interior corner points in .

[0010] Optionally, step S5 includes: S51: Definition Point For the chessboard grid calibration board Column, No. Interior corner points on rows; Definition point For the CCD imaging plane and the inner corner point The corresponding interior corner points of the chessboard square; point With point There is a one-to-one correspondence, which determines several groups of interior corner points; S52: Define the light slice plane midpoint of coordinate system Let the origin of the light slice plane be denoted by , and let the horizontal direction to the right be denoted by . Positive direction, vertically downwards is positive direction; S53: Interior corner point of the checkerboard plane Its coordinates in the world coordinate system are _____. The calculation is as follows:

[0011]

[0012] in, and These are the interior corners. In the world coordinate system middle , Coordinate values ​​in the direction; The width of the chessboard square.

[0013] Optionally, step S6 includes: S61: Uses the OpenCV interior corner extraction algorithm to detect the interior corners of the chessboard. S62: Optimize the inner corner coordinates at the sub-pixel level to obtain the inner corner points. In the CCD imaging surface where distortion exists Sub-pixel coordinates .

[0014] Optionally, step S7 includes: S71: Divide the CCD imaging surface into equidistant regions centered at the center point; the resulting regions have radii of... circle The area and the ring width are ring The area and the ring width are ring The region, among which, , , This represents the maximum distance between a sub-pixel in the imaging plane and the imaging center. S72: Calculate the sub-pixel coordinates of each interior corner point in the CCD imaging plane. Distance to the center of the image :

[0015] in, The horizontal width of the imaging plane. The vertical width of the imaging plane; S73: The distance from the inner corner point to the image center in each CCD imaging plane. Substitute these values ​​into the following formula to assign regions to the inner corner point group:

[0016] in, , and They are area, Region and The set of pixel coordinates of feature points in a region; , and respectively area, Region and The set of world coordinates of feature points in a region.

[0017] Optionally, step S8 includes: S81: Construct a high-order matrix collaborative model to realize a flexible mapping relationship between interior corner point groups, i.e. The function is as follows:

[0018] in, For model order parameters; For the total mapping function; For sub-pixel coordinates and The mapping function; For sub-pixel coordinates and The mapping function; In the above formula , and and The specific functional relationships are as follows:

[0019] in, and They are and The mapping coefficients, , ; Indicates the first Subpixel coordinates of Power of; Represent a variable, starting from 0 and ending at 0. Finish; Represents a variable; Indicates the first The nth power of the sub-pixel coordinate value v; S82: For regions with different degrees of distortion, different order matrix collaborative models are used to construct flexible mapping relationships for the inner corner point groups in each region; make To determine the order of the matrix cooperative model used in the region where the current inner corner point group is located, as follows:

[0020] in This represents the distance from the sub-pixel coordinate to the image center; S83: The following mapping formula is obtained:

[0021] in, , The minimum order of the matrix collaborative model used; , and They represent area, Region and The interior corner point group of the region; , and express area, Region and Mapping functions within the region; when hour, The mapping relationship expansion is as follows: and and First relational formula:

[0022] and and The second relational formula:

[0023] in and They are respectively represented as The area is and The mapping coefficients; Indicates the first The t-1 power of the sub-pixel coordinate value u; make , , , , The first and second relation formulas simplify to:

[0024]

[0025] Multiply both sides of the equation by ,as follows:

[0026] Due to the matrix If the matrix is ​​invertible, then the matrix... The calculation formula is as follows:

[0027] Similarly, matrix The calculation is as follows:

[0028] S84: Obtain the mapping coefficients for each region; Based on the mapping coefficients, the mapping relationship is obtained. middle , mapping coefficients and ,in, ,Finish Regional calibration, the mapping coefficients are denoted as ; when When, solve for the mapping coefficients. and ,in, ,Finish Region calibration, the mapping coefficients are denoted as ; when When, solve for the mapping coefficients. and ,in, ,Finish Region calibration, the mapping coefficients are denoted as ; S85: Construct a multimodal flexible mapping model based on region allocation; respectively... , and Mapping coefficients of the three regions , and Substituting these values ​​into the mapping formula, and generalizing the pixel coordinates and world coordinates in the formula, we obtain the flexible mapping relationship:

[0029] in, , The horizontal width of the imaging plane; The vertical width of the imaging plane; , and express area, Region and Flexible mapping relationships over regions; S86: High-precision calibration of the optical slicing plane is achieved through flexible mapping relationships.

[0030] An electronic device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to perform a region multimodal mapping-based optical slice plane calibration method.

[0031] A computer-readable storage medium storing instructions that, when executed, perform a method for optical slice plane calibration based on region multimodal mapping.

[0032] The beneficial effects of the technical solution provided in this application are: By dividing the CCD imaging surface into regions and assigning internal corner points to these regions, and constructing a multimodal mapping model for these regions, a flexible mapping between pixel coordinates and world coordinates for internal corner points on the optical plane is established in different regions. This solves the problem of low mapping accuracy caused by large edge distortion of the imaging surface. While improving mapping accuracy, only one flexible transformation is required to directly correct distortion of the imaging unit within the optical slicing measurement system. Simultaneously, coordinate mapping between the imaging surface and the optical slicing plane is achieved. The technical solution of this application eliminates the need for center extraction of light fringes, avoiding the influence of image algorithm errors on the calibration results. It also avoids the cumbersome steps of internal and external parameter matrix calculations, greatly simplifying the calibration process, reducing calibration costs, and improving the calibration accuracy of the optical plane. Attached Figure Description

[0033] The present application will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a schematic diagram of the optical slicing plane calibration in an embodiment of this application; Figure 2 This is a diagram of a flexible mapping model based on region segmentation in an embodiment of this application; Figure 3 This is a flowchart of an embodiment of this application; Figure 4 This is a schematic diagram of the electronic device structure in the embodiments of this application. Detailed Implementation

[0034] To provide a clearer understanding of the technical features, objectives, and effects of this application, the specific embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0035] The embodiments of this application provide a method for optical slice plane calibration based on regional multimodal mapping.

[0036] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating the steps of a light slice plane calibration method based on region multimodal mapping in an embodiment of this application, including: S1: The spatial position and spatial angle between the fixed camera and the light slice emitter are rigidly connected; S2: Adjust the optical slice plane and the calibration plate plane to coincide in physical space; S3: Capture a high-definition checkerboard plane using the imaging unit CCD camera, and obtain the inner corner points of the light slice plane in the pixel coordinate system of the CCD imaging surface; In one embodiment of this application, the interior corner point is a key feature point, such as... Figure 1 As shown.

[0037] S4: Dimensionality reduction is achieved by constructing the world coordinates of the interior corner points on the light slice plane; Step S4 includes: S41: Set the world coordinate system on the checkerboard plane; define The plane coincides with the chessboard plane. The axis is parallel to the normal vector of the checkerboard plane. ; S42: Obtain the light slice plane in the world coordinate system interior corner points in .

[0038] In one embodiment of this application, the mapping relationship between two-dimensional plane points and three-dimensional space points can be reduced by defining the world coordinates of the interior corner points on the light slice plane. Therefore, the mapping relationship in light plane calibration can be converted into pixel coordinate points. with world coordinates The mapping relationship between them.

[0039] S5: Calculate the world coordinates of the interior corner points; Step S5 includes: S51: Definition Point For the chessboard grid calibration board Column, No. Interior corner points on rows; Definition point For the CCD imaging plane and the inner corner point The corresponding interior corner points of the chessboard square; point With point There is a one-to-one correspondence, which determines several groups of interior corner points; S52: Define the light slice plane midpoint of coordinate system Let the origin of the light slice plane be denoted by , and let the horizontal direction to the right be denoted by . Positive direction, vertically downwards is positive direction; In one embodiment of this application, a light slicing plane is defined. midpoint of coordinate system Let the origin of the light slice plane be denoted by , and let the horizontal direction to the right be denoted by . Positive direction, vertically downwards is Positive direction, such as Figure 2 As shown.

[0040] S53: Interior corner point of the checkerboard plane Its coordinates in the world coordinate system are _____. The calculation is as follows:

[0041]

[0042] in, and These are the interior corners. In the world coordinate system middle , Coordinate values ​​in the direction; The width of the chessboard square.

[0043] In one embodiment of this application, since the camera lens is composed of lenses, distortion occurs during CCD imaging, resulting in a nonlinear mapping relationship between the inner corner points. In the CCD imaging plane, the central region experiences less distortion due to its proximity to the lens optical axis, but the distortion increases in the surrounding areas as the distance from the optical axis increases.

[0044] S6: Extract the sub-pixel coordinates of the inner corner points on the CCD imaging surface; Step S6 includes: S61: Uses the OpenCV interior corner extraction algorithm to detect the interior corners of the chessboard. In one embodiment of this application, the OPENCV interior corner extraction algorithm calculates the corner response function by calculating the autocorrelation matrix of the image gradient within a local image window, thereby achieving interior corner localization.

[0045] S62: Optimize the inner corner coordinates at the sub-pixel level to obtain the inner corner points. In the CCD imaging surface where distortion exists Sub-pixel coordinates .

[0046] S7: Divide the CCD imaging surface into regions, and calculate the distance from the sub-pixel coordinates to the imaging center by combining the sub-pixel coordinates; allocate regions to the inner corner point group by using the distance and the world coordinate values ​​of the inner corner points; Step S7 includes: S71: Divide the CCD imaging surface into equidistant regions centered at the center point; the resulting regions have radii of... circle The area and the ring width are ring The area and the ring width are ring The region, among which, , , This represents the maximum distance between a sub-pixel in the imaging plane and the imaging center. In one embodiment of this application, a flexible mapping model based on region segmentation, such as Figure 2 As shown.

[0047] S72: Calculate the sub-pixel coordinates of each interior corner point in the CCD imaging plane. Distance to the center of the image :

[0048] in, The horizontal width of the imaging plane. The vertical width of the imaging plane; S73: The distance from the inner corner point to the image center in each CCD imaging plane. Substitute these values ​​into the following formula to assign regions to the inner corner point group:

[0049] in, , and They are area, Region and The set of pixel coordinates of feature points in a region; , and respectively area, Region and The set of world coordinates of feature points in a region.

[0050] S8: Construct a multimodal flexible mapping model based on region allocation; establish a flexible mapping relationship between the distorted CCD imaging surface and the optical slicing plane in different regions through the multimodal flexible mapping model; achieve high-precision calibration of the optical slicing plane through the flexible mapping relationship.

[0051] This application provides an embodiment as follows: constructing a multimodal flexible mapping model based on region allocation, and establishing CCD imaging surfaces with distortion in different regions. To the light slice plane Flexible mapping relationship To optimize the mapping accuracy between coordinate points and realize the light slicing plane. High-precision calibration, such as Figure 2 As shown.

[0052] Step S8 includes: S81: Construct a high-order matrix collaborative model to realize a flexible mapping relationship between interior corner point groups, i.e. The function is as follows:

[0053] in, For model order parameters; For the total mapping function; For sub-pixel coordinates and The mapping function; For sub-pixel coordinates and The mapping function; In the above formula , and and The specific functional relationships are as follows:

[0054] in, and They are and The mapping coefficients, , ; Indicates the first Subpixel coordinates of Power of; Represent a variable, starting from 0 and ending at 0. Finish; Represents a variable; Indicates the first The nth power of the sub-pixel coordinate value v; This application provides an embodiment as follows: after dividing the CCD imaging surface into regions, the degree of distortion gradually increases from the central region to the outer region. Therefore, for regions with different degrees of distortion, different order matrix collaborative models are used to construct a flexible mapping relationship for the inner corner point group in each region. This solves the problem of overfitting of high-order mapping models to corner points in the central region and reduces mapping errors; it also avoids insufficient corner point mapping accuracy in the outer region due to the model order being too low.

[0055] S82: For regions with different degrees of distortion, different order matrix collaborative models are used to construct flexible mapping relationships for the inner corner point groups in each region; make To determine the order of the matrix cooperative model used in the region where the current inner corner point group is located, as follows:

[0056] in This represents the distance from the sub-pixel coordinate to the image center; S83: The following mapping formula is obtained:

[0057] in, , The minimum order of the matrix collaborative model used; , and They represent area, Region and The interior corner point group of the region; , and express area, Region and Mapping functions within the region; when hour, The mapping relationship expansion is as follows: and and First relational formula:

[0058] and and The second relational formula:

[0059] in and They are respectively represented as The area is and The mapping coefficients; Indicates the first The t-1 power of the sub-pixel coordinate value u; make , , , , The first and second relation formulas simplify to:

[0060]

[0061] Multiply both sides of the equation by ,as follows:

[0062] Due to the matrix If the matrix is ​​invertible, then the matrix... The calculation formula is as follows:

[0063] Similarly, matrix The calculation is as follows:

[0064] S84: Obtain the mapping coefficients for each region; Based on the mapping coefficients, the mapping relationship is obtained. middle , mapping coefficients and ,in, ,Finish Regional calibration, the mapping coefficients are denoted as ; when When, solve for the mapping coefficients. and ,in, ,Finish Region calibration, the mapping coefficients are denoted as ; when When, solve for the mapping coefficients. and ,in, ,Finish Region calibration, the mapping coefficients are denoted as ; S85: Construct a multimodal flexible mapping model based on region allocation; respectively... , and Mapping coefficients of the three regions , and Substituting these values ​​into the mapping formula, and generalizing the pixel coordinates and world coordinates in the formula, we obtain the flexible mapping relationship:

[0065] in, , The horizontal width of the imaging plane; The vertical width of the imaging plane; , and express area, Region and Flexible mapping relationships over regions; S86: High-precision calibration of the optical slicing plane is achieved through flexible mapping relationships.

[0066] This application provides an embodiment as follows, which ultimately completes the overall calibration of the optical plane. The calibration method flowchart is shown below. Figure 3 As shown.

[0067] This application also discloses an electronic device. (See reference...) Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0068] The communication bus 502 is used to enable communication between these components.

[0069] The user interface 503 may include a display screen, and optionally, the user interface 503 may also include a standard wired interface or a wireless interface.

[0070] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0071] This application also discloses a computer-readable storage medium storing multiple instructions adapted for loading by a processor to execute the above-described method for optical slice plane calibration based on region multimodal mapping.

[0072] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure.

[0073] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for optical slice plane calibration based on region multimodal mapping, characterized in that, The method includes the following steps: S1: The spatial position and spatial angle between the fixed camera and the light slice emitter are rigidly connected; S2: Adjust the optical slice plane and the calibration plate plane to coincide in physical space; S3: Capture a high-definition checkerboard plane using the imaging unit CCD camera, and obtain the inner corner points of the light slice plane in the pixel coordinate system of the CCD imaging surface; S4: Dimensionality reduction is achieved by constructing the world coordinates of the interior corner points on the light slice plane; S5: Calculate the world coordinates of the interior corner points; S6: Extract the sub-pixel coordinates of the inner corner points on the CCD imaging surface; S7: Divide the CCD imaging surface into regions, and calculate the distance from the sub-pixel coordinates to the imaging center by combining the sub-pixel coordinates; allocate regions to the inner corner point group by using the distance and the world coordinate values ​​of the inner corner points; S8: Construct a multimodal flexible mapping model based on region allocation; establish a flexible mapping relationship between the distorted CCD imaging surface and the optical slicing plane in different regions through the multimodal flexible mapping model; achieve high-precision calibration of the optical slicing plane through the flexible mapping relationship.

2. The optical slice plane calibration method based on region multimodal mapping as described in claim 1, characterized in that, Step S4 includes: S41: Set the world coordinate system on the checkerboard plane; define The plane coincides with the chessboard plane. The axis is parallel to the normal vector of the checkerboard plane. ; S42: Obtain the light slice plane in the world coordinate system interior corner points in .

3. The optical slice plane calibration method based on region multimodal mapping as described in claim 2, characterized in that, Step S5 includes: S51: Definition Point For the chessboard grid calibration board Column, No. Interior corner points on rows; Definition point For the CCD imaging plane and the inner corner point The corresponding interior corner points of the chessboard square; point With point There is a one-to-one correspondence, which determines several groups of interior corner points; S52: Define the light slice plane midpoint of coordinate system Let the origin of the light slice plane be denoted by , and let the horizontal direction to the right be denoted by . Positive direction, vertically downwards is positive direction; S53: Interior corner point of the checkerboard plane Its coordinates in the world coordinate system are _____. The calculation is as follows: in, and These are the interior corners. In the world coordinate system middle , Coordinate values ​​in the direction; The width of the chessboard square.

4. The optical slice plane calibration method based on region multimodal mapping as described in claim 3, characterized in that, Step S6 includes: S61: Uses the OpenCV interior corner extraction algorithm to detect the interior corners of the chessboard. S62: Optimize the inner corner coordinates at the sub-pixel level to obtain the inner corner points. In the CCD imaging surface where distortion exists Sub-pixel coordinates .

5. The optical slice plane calibration method based on region multimodal mapping as described in claim 4, characterized in that, Step S7 includes: S71: Divide the CCD imaging surface into equidistant regions centered at the center point; the resulting regions have radii of... circle The area and the ring width are ring The area and the ring width are ring The region, among which, , , This represents the maximum distance between a sub-pixel in the imaging plane and the imaging center. S72: Calculate the sub-pixel coordinates of each interior corner point in the CCD imaging plane. Distance to the center of the image : in, The horizontal width of the imaging plane. The vertical width of the imaging plane; S73: The distance from the inner corner point to the image center in each CCD imaging plane. Substitute these values ​​into the following formula to assign regions to the inner corner point group: in, , and They are area, Region and The set of pixel coordinates of feature points in a region; , and respectively area, Region and A set of world coordinates of feature points in a region.

6. The optical slice plane calibration method based on region multimodal mapping as described in claim 5, characterized in that, Step S8 includes: S81: Construct a high-order matrix collaborative model to realize a flexible mapping relationship between interior corner point groups, i.e. The function is as follows: in, For model order parameters; For the total mapping function; For sub-pixel coordinates and The mapping function; For sub-pixel coordinates and The mapping function; In the above formula , and and The specific functional relationships are as follows: in, and They are and The mapping coefficients, , ; Indicates the first Subpixel coordinates of Power of; Represent a variable, starting from 0 and ending at 0. Finish; Represents a variable; Indicates the first The nth power of the sub-pixel coordinate value v; S82: For regions with different degrees of distortion, different order matrix collaborative models are used to construct flexible mapping relationships for the inner corner point groups in each region; make To determine the order of the matrix cooperative model used in the region where the current inner corner point group is located, the following steps are taken: in This represents the distance from the sub-pixel coordinate to the image center; S83: The following mapping formula is obtained: in, , The minimum order of the matrix collaborative model used; , and They represent area, Region and The interior corner point group of the region; , and express area, Region and Mapping function within the region; when hour, The mapping relationship expansion is as follows: and and First relational formula: and and The second relational formula: in and They are respectively represented as The area is and The mapping coefficients; Indicates the first The t-1 power of the sub-pixel coordinate value u; make , , , , The first and second relation formulas simplify to: Multiply both sides of the equation by ,as follows: Due to the matrix If the matrix is ​​invertible, then the matrix... The calculation formula is as follows: Similarly, matrix The calculation is as follows: S84: Obtain the mapping coefficients for each region; Based on the mapping coefficients, the mapping relationship is obtained. middle , mapping coefficients and ,in, ,Finish Regional calibration, the mapping coefficients are denoted as ; when When, solve for the mapping coefficients. and ,in, ,Finish Region calibration, the mapping coefficients are denoted as ; when When, solve for the mapping coefficients. and ,in, ,Finish Region calibration, the mapping coefficients are denoted as ; S85: Construct a multimodal flexible mapping model based on region allocation; respectively... , and Mapping coefficients of the three regions , and Substituting these values ​​into the mapping formula, and generalizing the pixel coordinates and world coordinates in the formula, we obtain the flexible mapping relationship: in, , The horizontal width of the imaging plane; The vertical width of the imaging plane; , and express area, Region and Flexible mapping relationships over regions; S86: High-precision calibration of the optical slicing plane is achieved through flexible mapping relationships.

7. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a computer, perform the method as described in any one of claims 1-6.

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