A calibration method for three-dimensional measurement equipment

Abstract

The present application provides a calibration method for a three-dimensional measuring device, comprising obtaining calibration image data of a calibration object and spatial coordinates of feature points on the calibration object, wherein the spatial coordinates of the feature points on the calibration object are obtained according to the size of the calibration object; according to the calibration image data, the pixel coordinates of the feature points on the calibration object corresponding to the feature points on the imaging surface are determined; the distortion parameters are determined by a distortion model and the pixel coordinates of the feature points on the imaging surface; the distortion-free pixel coordinates corresponding to the pixel coordinates of the feature points on the imaging surface are determined by the distortion model; the transformation matrix of the spatial coordinates and the distortion-free pixel coordinates is determined according to a homography matrix, and the transformation matrix is ​​used to calibrate the coordinates of the object to be measured; the present application determines the transformation matrix used for calibration by obtaining the spatial coordinates of the feature points on the calibration object and the corresponding distortion-free pixel coordinates, thereby improving the calibration accuracy of the three measuring devices.

Description

Technical Field

[0001] The present application relates to the field of machine vision, and in particular to a calibration method for three-dimensional measurement equipment. Background Art

[0002] Three-dimensional measuring equipment has important applications in measurement, positioning, and detection in industrial automation. Based on a line laser camera, three-dimensional measuring equipment maps spatial points on the surface of the object to be measured to pixel points in the camera imaging image, thereby determining the three-dimensional geometric information and grayscale texture information of the spatial points corresponding to the pixel points.

[0003] The calibration of existing 3D measurement equipment mainly includes camera internal and external parameter calibration and light plane calibration. Figure 1 As shown, the calibration of the camera's internal and external parameters is assisted by an optical component 30. Within the clear imaging range of the camera 10, it is necessary to continuously adjust the posture of the calibration plate 20 having feature points and geometric information to perform feature extraction and camera internal and external parameter calibration; after correcting the image distortion by the camera's internal parameters, the light plane is calibrated in combination with the camera's external parameters; wherein the camera's internal parameters include focal length, principal point and lens distortion coefficient, and the camera's external parameters include the rotation matrix and translation matrix of the camera's coordinate system relative to the space coordinate system.

[0004] However, in the existing three-dimensional measurement equipment calibration, it is necessary to first calibrate the internal and external parameters of the camera and obtain the calibration images of the calibration plates with different postures under the optical components. It is difficult to ensure the clarity of the calibration images in each posture, which will reduce the calibration accuracy. Summary of the invention

[0005] The present application provides a calibration method for a three-dimensional measurement device to solve the technical problem in the prior art that when calibrating the internal and external parameters of a camera, it is difficult to obtain calibration images of calibration plates with different postures under optical components, thereby reducing the calibration accuracy.

[0006] In order to achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

[0007] The present application provides a calibration method for a three-dimensional measurement device, the calibration method comprising:

[0008] Acquire calibration image data of the calibration object and spatial coordinates of feature points on the calibration object, wherein the spatial coordinates of the feature points on the calibration object are acquired according to the size of the calibration object;

[0009] Determining pixel coordinates of feature points on the calibration object corresponding to feature points on the imaging surface according to the calibration image data;

[0010] Determine the distortion parameters through the distortion model and the pixel coordinates of the feature points on the imaging surface;

[0011] Determine the distortion-free pixel coordinates corresponding to the pixel coordinates of the feature points on the imaging surface by using the distortion model and the distortion parameters;

[0012] A transformation matrix of the spatial coordinates and the undistorted pixel coordinates is determined according to the homography matrix, and the transformation matrix is ​​used to calibrate the coordinates of the object to be measured.

[0013] In a possible implementation manner, after determining the transformation matrix, the calibration method further includes:

[0014] Obtaining an imaging coordinate diagram of a standard gauge block and distances between adjacent standard surfaces of the standard gauge block;

[0015] The imaging coordinates of the standard gauge block are determined according to the transformation matrix to determine the real coordinates of the standard gauge block;

[0016] Determining the true value of the distance between adjacent standard surfaces of the standard gauge block according to the true coordinates of the standard gauge block;

[0017] A correction coefficient is determined according to the distance between adjacent standard surfaces of the standard gauge block and the true value of the distance, and the correction coefficient is used to correct the longitudinal coordinate of the object to be measured.

[0018] In a possible implementation, a three-dimensional measurement device includes a camera, a calibration object, and a moving stage; the moving stage moves in a direction perpendicular to a line laser of the camera, and the calibration object is placed on the moving stage.

[0019] Among them, the calibration object includes a platform base, a pad and a calibration plate; the platform base has a groove on one side and a baffle on the other side, and the groove is used to place the pad; the calibration plate is placed obliquely between the baffle of the platform base and the pad, and has an inclination angle θ with the platform base.

[0020] The present application provides calibration plates in different postures for camera imaging through calibration objects and a mobile stage, and the calibration images in different postures are within the clear imaging range of the camera, thereby improving the imaging accuracy.

[0021] The present application provides a calibration method for a three-dimensional measuring device, comprising obtaining calibration image data of a calibration object and spatial coordinates of feature points on the calibration object, wherein the spatial coordinates of the feature points on the calibration object are obtained according to the size of the calibration object; according to the calibration image data, the pixel coordinates of the feature points on the calibration object corresponding to the feature points on the imaging surface are determined; the distortion parameters are determined by a distortion model and the pixel coordinates of the feature points on the imaging surface; the distortion-free pixel coordinates corresponding to the pixel coordinates of the feature points on the imaging surface are determined by the distortion model; the transformation matrix of the spatial coordinates and the distortion-free pixel coordinates is determined according to a homography matrix, and the transformation matrix is ​​used to calibrate the coordinates of the object to be measured; the present application determines the transformation matrix used for calibration by obtaining the spatial coordinates of the feature points on the calibration object and the corresponding distortion-free pixel coordinates, thereby improving the calibration accuracy of the three measuring devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 A schematic diagram of the existing camera internal and external parameter calibration of the three-dimensional measurement equipment of this application;

[0024] Figure 2 This is a schematic diagram of the structure of a three-dimensional measurement device according to an embodiment of the present application;

[0025] Figure 3 This is a schematic diagram of the structure of a calibration object according to an embodiment of the present application;

[0026] Figure 4 A schematic diagram of a calibration plate according to an embodiment of the present application;

[0027] Figure 5 A schematic diagram of a flow chart of a three-dimensional measurement device calibration method according to an embodiment of the present application;

[0028] Figure 6 A schematic diagram of a calibration method for three-dimensional measurement equipment according to an embodiment of the present application;

[0029] Figure 7 This is a schematic diagram of the structure of a standard gauge block according to an embodiment of the present application;

[0030] Among them: 1-camera; 2-calibration object; 21-platform base; 211-groove; 212-baffle; 22-pad; 23-calibration plate; 3-moving platform. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application 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 the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising 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 devices.

[0033] The 3D measurement equipment is based on a line laser camera to obtain high-resolution geometric information and grayscale texture information of the surface of the object being measured; the camera internal and external parameters can be calibrated using Zhang's camera calibration method, using calibration plates with feature points and geometric information such as a plane chessboard or dot grid to obtain images of different postures at multiple positions in the camera imaging area, that is, to collect images containing laser lines and calibration plate features, and further perform feature extraction and camera internal and external parameter calibration on the image. After correcting the image distortion through the camera internal parameters, the light plane is calibrated in combination with the camera external parameters. The process of constantly adjusting the calibration plate posture to calibrate the camera internal and external parameters is cumbersome and difficult to automate.

[0034] For line laser cameras, Schaum's law can be used for camera imaging to make the camera lens focus within the light plane. For calibration plates in different postures, since the camera imaging uses Schaum's law to reduce the clear imaging range of the vertical line laser surface, it is difficult to ensure the imaging clarity of the calibration plate in each posture, reducing the accuracy of calibration.

[0035] To solve the above problems, some embodiments of the present application provide a three-dimensional measurement device, such as Figure 2 As shown, the three-dimensional measurement device may include a line laser-based camera 1, a calibration object 2 and a moving stage 3. The calibration object 2 is placed on the moving stage 3, and the calibration object 2 is adjusted to be within the clear measurement range of the camera 1. When the line laser corresponds to the features in the same row on the calibration plate, the moving stage 3 is controlled to move in a direction perpendicular to the line laser.

[0036] In one embodiment, a spatial coordinate system O-XZ is established, and a pixel coordinate system o-uv is established on the imaging surface. In the spatial coordinate system, X is perpendicular to the direction of motion, Z is vertically upward, and the origin is set at the calibration plate pattern space point corresponding to the upper left feature point of the calibration data. In the pixel coordinate system established on the imaging surface, u is horizontally to the right, v is vertically downward, and the origin is located at the first pixel in the upper left.

[0037] like Figure 3 and Figure 4 As shown, the calibration object can be a calibration component, and the calibration component can include a platform base 21, a pad 22, and a calibration plate 23. The platform base 21 has a groove 211 on one side and a baffle 212 on the other side. The groove 211 is used to place the pad 22. The calibration plate 23 is tilted between the baffle 212 and the pad 22 of the platform base 21, and can be replaced with pads 22 of different heights according to the requirements of the calibrated three-dimensional measurement equipment. The present application converts the two-dimensional calibration plate 23 into a target with three-dimensional feature points through mechanical automatic control, that is, it has depth direction information, and the calibration image data of the calibration object can be obtained within the clear imaging range of the camera.

[0038] The calibration plate 23 is a flat plate with a characteristic pattern. The pattern may be a dot pattern with white dots on a black background, with the center of the dots used as the feature for calibration. In some embodiments, the pattern of the calibration plate may be a checkerboard, by extracting the checkerboard corners as features for distortion correction and system calibration; or it may be a uniform grid line pattern, by extracting the grid lines and grid intersections as features for distortion correction and system calibration.

[0039] In some embodiments, the spatial coordinates of the feature points may also be obtained through calibration objects with three-dimensional feature information, such as a height block with feature points, a standard sphere, or a sawtooth block.

[0040] The present application provides a three-dimensional measurement device, comprising a camera, a calibration object and a moving stage; the moving stage moves in a direction perpendicular to the camera line laser, and the calibration object is placed on the moving stage; the calibration object comprises a platform base, a cushion block and a calibration plate; one side of the platform base has a groove, and the other side has a baffle, and the groove is used to place the cushion block; the calibration plate is tilted and placed between the baffle and the cushion block of the platform base; the present application provides a calibration plate in different postures for camera imaging through the calibration object and the moving stage, and the calibration images in different postures are within the clear imaging range of the camera, thereby improving the imaging accuracy.

[0041] In combination with the above-mentioned embodiments to provide a three-dimensional measuring device, some embodiments of the present application provide a calibration method for a three-dimensional measuring device, such as Figure 5 As shown, the calibration includes the following steps:

[0042] S101, obtaining calibration image data of a calibration object and spatial coordinates of feature points on the calibration object.

[0043] The calibration image data is obtained by scanning the moving calibration object to obtain multiple groups of image data to be processed; the multiple groups of image data to be processed are preprocessed to obtain calibration image data; wherein the preprocessing algorithm determines a group of calibration image data corresponding to the multiple groups of image data to be processed. The calibration image data includes a coordinate map and a grayscale map, the coordinate map includes pixel coordinates and row coordinate values; the grayscale value includes pixel coordinates and grayscale values; wherein the row coordinate value and the grayscale value are determined by the following specific method:

[0044] The calibration object is placed on the mobile stage, and the calibration object is adjusted to be within the clear measurement range of the camera. When the line laser corresponds to the features in the same row on the calibration plate, the mobile stage is controlled to move in a direction perpendicular to the line laser. During the movement of the mobile stage, the camera scans the calibration plate N times to obtain N groups of line laser light stripe images on the surface of the calibration plate, and each group of light stripe images corresponds to the image of the calibration object at a different position. The light stripe pattern is an image of the same row of features on the calibration plate scanned by the camera based on the line laser. When the calibration plate moves in a direction perpendicular to the line laser according to a preset speed, the camera scans the light stripe image on the calibration plate according to a preset time to obtain N groups of light stripe images, wherein the preset time can be determined according to the preset speed.

[0045] For each group of light streak images, the corresponding row coordinate value and brightness value are determined by the light streak center extraction algorithm. For each group of light streak images, the center row coordinate and brightness of each column are extracted by the light streak center extraction algorithm to obtain the row coordinate value and brightness value of this group of light streak images. Among them, the light streak center extraction algorithm can be a traditional light streak center extraction algorithm, an improved algorithm based on the traditional light streak center extraction algorithm, or a neural network-based light streak center extraction algorithm.

[0046] The N groups of brightness values ​​are converted into corresponding N groups of grayscale values. Therefore, the coordinate map in the calibration image data includes N groups of row coordinate values, and the grayscale map in the calibration image data includes N groups of grayscale values.

[0047] The spatial coordinates of the feature points on the calibration object are determined according to the size of the calibration object. The calibration object may be a calibration component, which may include a platform base, a pad, and a calibration plate. The platform base has a groove on one side and a baffle on the other side. The groove is used to place the pad. The calibration plate is tilted between the baffle and the pad of the platform base. The pad can be replaced with pads of different heights according to the requirements of the calibrated three-dimensional measurement equipment, so that the two-dimensional calibration plate is converted into a target with three-dimensional feature points and depth direction information.

[0048] The calibration plate is a flat plate with a characteristic pattern. The pattern can be a dot diagram with white dots on a black background. The center of the dot is used as the feature used for calibration. The spacing of the dot center row direction is Δp. h , the spacing between the center columns of dots is Δp v As shown in the figure, it is a schematic diagram of the size of the calibration component. According to the size of the calibration component, the inclination angle θ of the calibration plate is determined:

[0049]

[0050] Where h is the height of the pad, d is the depth of the groove, and W is the distance between the platform base baffle and the groove. The spatial coordinates P(X P , Z P ), the spatial coordinates of the feature points are calculated according to the following formula:

[0051]

[0052] In some embodiments, the spatial coordinates of feature points on the calibration object can be determined based on the three-dimensional feature information of the calibration object. For example, a height block, a standard sphere, or a sawtooth block with feature points can determine the spatial coordinates of the feature points based on their geometric structure.

[0053] S102: Determine, according to the calibration image data, the pixel coordinates of the imaging surface feature points corresponding to the spatial feature point coordinates.

[0054] Determine the spatial coordinates P(X P , Z P ) corresponds to the pixel coordinates p(u p ,v p ). First, obtain the spatial feature points and extract the feature coordinates in the grayscale image as the grayscale feature point p in (u in ,v in ),exist Figure 2 In the calibration plate shown, the center of the circle is used as a feature to extract feature coordinates, and the dots in the grayscale image are detected by a dot detection algorithm to extract the center coordinates.

[0055] According to the feature points of the calibration object corresponding to the feature point coordinates in the grayscale image, determine the column coordinates u of the feature points on the imaging surface p ; Determine the coordinates in the coordinate map according to the coordinates of the feature points in the grayscale map; Interpolate the neighborhood coordinates of the coordinate values ​​through an interpolation algorithm f inter , determine the row coordinates v of the feature points on the imaging surface p ; The pixel coordinates of the feature points corresponding to the imaging surface, that is, the feature points in the jth row and the ith column in the imaging surface are expressed as:

[0056]

[0057] S103 , determining distortion parameters through the distortion model and the pixel coordinates of the feature points on the imaging surface.

[0058] The distortion parameters include the principal point (u c , v c ), radial distortion coefficients k1, k2 and Sham angle τ x , τ y .

[0059] Among them, the principal point is determined by the cross ratio invariance, including determining the cross ratio CR of the imaging surface feature point in n directions with the neighboring feature points p , and the cross ratio CR of the feature points on the corresponding calibration object T ; Determine the deviation E based on the N feature points on the imaging surface p :

[0060]

[0061] In the formula, w n is the direction distance weight, n is the number of directions, and N is the number of feature points.

[0062] By analyzing the error distribution curve of the cross ratio deviation of all feature points, since the distortion around the principal point is the smallest, the principal point (u c , v c ), that is, the principal point is the imaging surface feature point corresponding to the minimum deviation.

[0063] The radial distortion coefficient and the Sham angle are determined by the collinearity of the feature points, including determining the ideal feature point p according to the collinearity of the feature points in the row or column. ij * ; According to the distance from the ideal feature point to the fitting straight line, establish the target optimization function:

[0064] F(k1,k2,τ x ,τ y )=∑∑|p ij * -L m | 2

[0065] Where k1 and k2 are radial distortion coefficients, τ x , τ y For Ras Sham, p ij * is the ideal feature point, L m is the fitted straight line.

[0066] Through the optimization algorithm, the target optimization function is nonlinearly optimized to obtain the radial distortion coefficient and the Sham angle.

[0067] S104: Determine the distortion-free pixel coordinates corresponding to the pixel coordinates of the feature points on the imaging surface through a distortion model.

[0068] The distortion parameters in the distortion model include principal points, radial distortion coefficients and Sham angles. The distortion parameters are calibrated by analyzing the distribution law of the feature points on the imaging surface. The feature points on the calibration plate are evenly distributed in space at equal intervals, the feature points on the imaging surface and the corresponding spatial feature points satisfy the cross ratio invariance, and the feature points in the same row or column are all collinear to determine the distortion parameters, and then the pixel coordinates of the feature points on the imaging surface corresponding to the undistorted pixel coordinates are determined by the distortion model, including:

[0069] S1041, determine the coordinates of the standard image plane distortion point, the standard image plane distortion point p′ xy The coordinates (x p ′,y p ') is calculated according to the following formula:

[0070]

[0071] Where s1 is the scale factor, T S According to the rotation matrix R(τ x ,τ y ) determines the conversion relationship, τ x , τ y Ras Sham, c , v c is the coordinate of the principal point; u p , v p is the pixel coordinate of the feature point on the imaging surface, x p ′,y p ′ is the coordinate of the standard image distortion point.

[0072] S1042, determining the coordinates of the standard image plane undistorted point, wherein the standard image plane undistorted point The coordinates (x p * ,y p * ) is calculated according to the following formula:

[0073] x p * =x p ′+δ u (x p ′,y p ′)

[0074] y p * =yp ′+δ v (x p ′,y p ′)

[0075] In the formula, x p * ,y p * is the coordinate of the point without distortion on the standard image plane, δ u and δ v The model parameters include radial distortion coefficients k1 and k2.

[0076] S1043, determining the pixel coordinates of the undistorted point on the imaging surface, wherein the pixel coordinates of the undistorted point on the imaging surface are calculated according to the following formula:

[0077]

[0078] In the formula, is the pixel coordinate of the point without distortion on the imaging surface.

[0079] S105. Determine a transformation matrix of the spatial coordinates and the undistorted pixel coordinates according to the homography matrix.

[0080] According to multiple sets of corresponding spatial coordinates and undistorted pixel coordinates, and the homography matrix of the spatial coordinates and the undistorted pixel coordinates, a transformation model of the spatial coordinates and the undistorted pixel coordinates is established as follows:

[0081]

[0082] Where s2 is the scale factor, H 3×3 is the transformation matrix.

[0083] Through multiple sets of corresponding spatial coordinates and undistorted pixel coordinates, the transformation matrix H is determined by an optimization algorithm. 3×3 , used to calibrate the coordinates of the object to be measured, wherein the optimization algorithm can be a least squares algorithm.

[0084] The transformation matrix H obtained by the above method 3×3 In actual measurement, the pixel coordinates obtained from the imaging surface can be used to reconstruct the real spatial coordinates (X, Z) in three dimensions, which can be calculated according to the following formula:

[0085]

[0086]

[0087] Where u and v are the pixel coordinates of the imaging surface.

[0088] The present application provides a calibration method for a three-dimensional measuring device, comprising determining calibration image data of a calibration object and spatial coordinates of feature points on the calibration object, wherein the calibration image data is determined by scanning the moving calibration object, and the spatial coordinates of the feature points on the calibration object are determined according to the size of the calibration object; according to the calibration image data, the pixel coordinates of the feature points on the calibration object corresponding to the feature points on the imaging surface are determined; the distortion-free pixel coordinates corresponding to the pixel coordinates of the feature points on the imaging surface are determined by a distortion model; the transformation matrix of the spatial coordinates and the distortion-free pixel coordinates is determined according to a homography matrix, and the transformation matrix is ​​used to calibrate the coordinates of the object to be measured; the present application determines the transformation matrix used for calibration by acquiring the spatial coordinates of the feature points on the calibration object and the corresponding distortion-free pixel coordinates, thereby improving the calibration accuracy of the three measuring devices.

[0089] Based on the calibration method of a three-dimensional measurement device provided above, in some embodiments of the present application, after determining the transformation matrix, such as Figure 6 As shown, a method of using high-precision standard gauge blocks to improve calibration accuracy is also provided:

[0090] S106, obtaining an imaging coordinate diagram of the standard gauge block and distances between adjacent standard surfaces of the standard gauge block.

[0091] like Figure 7 The standard gauge block 4 shown in the figure has each step surface as a high-precision plane, and the height difference between adjacent step surfaces is known, that is, the distance d between the two standard surfaces on the standard gauge block mn .

[0092] In some embodiments, standard gauge blocks of other structures may also be used to calibrate the data.

[0093] S107, the imaging coordinates of the standard gauge block are determined according to the transformation matrix to obtain the real coordinates of the standard gauge block.

[0094] In some embodiments, the imaging coordinates (u t , v t ), calculate the corresponding real space coordinates (X, Z); combine the movement information of the mobile platform, including the preset speed, etc., calculate the coordinate Y in the movement direction, and obtain the real point cloud data (X, Y, Z) of the standard gauge block.

[0095] S108. Determine the true value of the distance between adjacent standard surfaces of the standard gauge block according to the true coordinates of the standard gauge block.

[0096] The true value D of the distance between the two standard surfaces of the standard gauge block is obtained through the real point cloud data mn .

[0097] S109, determining a correction coefficient according to the distance between adjacent standard surfaces of the standard gauge block and the true value of the distance.

[0098] The correction coefficient is used to correct the longitudinal coordinate of the object to be measured. Obtain all the combinations of two standard surfaces on the standard gauge block and the distance d between the corresponding two standard surfaces mn , the true value of the distance between the two standard surfaces D mn , the formula for calculating the correction coefficient γ is as follows:

[0099]

[0100] Where N is the number of combinations of two standard surfaces.

[0101] Correct the spatial coordinates of the three-dimensional reconstruction after the three-dimensional measurement equipment is calibrated. The corrected coordinate Z * =γ·Z. In this application, the Z-direction coordinate calibration is required to be high, so a standard gauge block in the height direction is used for analysis.

[0102] The present application uses high-precision standard gauge blocks to perform data correction on the spatial coordinates of the three-dimensional reconstruction after the above calibration, so as to further improve the calibration accuracy.

[0103] The above content is only for explaining the technical idea of ​​the present application and cannot be used to limit the protection scope of the present application. Any changes made on the basis of the technical solution in accordance with the technical idea proposed in the present application shall fall within the protection scope of the claims of the present application.

[0104] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some embodiments currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0105] Similarly, it should be noted that in order to simplify the description disclosed in this application and thus help understand one or more embodiments, in the above description of the embodiments of this application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0106] Each patent, patent application, patent application disclosure, and other materials, such as articles, books, instructions, publications, documents, etc., cited in this application are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the content of this application, documents that limit the broadest scope of the claims of this application (currently or later attached to this application) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the attached materials of this application are inconsistent or conflicting with the content described in this application, the descriptions, definitions, and / or use of terms in this application shall prevail.

Claims

1. A calibration method for a three-dimensional measuring device, characterized in that: The calibration method comprises: Acquire calibration image data of the calibration object and spatial coordinates of feature points on the calibration object, wherein the spatial coordinates of the feature points on the calibration object are acquired according to the size of the calibration object; Determining pixel coordinates of feature points on the calibration object corresponding to feature points on the imaging surface according to the calibration image data; Determine the distortion parameters through the distortion model and the pixel coordinates of the feature points on the imaging surface; Determine the distortion-free pixel coordinates corresponding to the pixel coordinates of the feature points on the imaging surface by using the distortion model and the distortion parameters; Determine a transformation matrix of the spatial coordinates and the undistorted pixel coordinates according to a homography matrix, wherein the transformation matrix is ​​used to calibrate the coordinates of the object to be measured; After determining the transformation matrix, the calibration method further includes: Obtaining an imaging coordinate diagram of a standard gauge block and distances between adjacent standard surfaces of the standard gauge block; The imaging coordinates of the standard gauge block are determined according to the transformation matrix to determine the real coordinates of the standard gauge block; Determining the true value of the distance between adjacent standard surfaces of the standard gauge block according to the true coordinates of the standard gauge block; A correction coefficient is determined according to the distance between adjacent standard surfaces of the standard gauge block and the true value of the distance, and the correction coefficient is used to correct the longitudinal coordinate of the object to be measured.

2. A calibration method for a three-dimensional measuring device according to claim 1, characterized in that: The distortion parameters include the principal point (u c , v c ), radial distortion coefficients k1, k2 and Sham angle τ x , τ y .

3. A calibration method for a three-dimensional measuring device according to claim 2, characterized in that: The principal point is determined by cross-ratio invariance, including: Determine the cross ratio CR of the imaging surface feature point in n directions with the neighborhood feature point p , and the cross ratio CR of the corresponding feature points on the calibration object T ; Determine the deviation E based on the N feature points on the imaging surface p : In the formula, w n is the direction distance weight, n is the number of directions, and N is the number of feature points; Determine the principal point (u c , v c ), the principal point is the imaging surface feature point corresponding to the minimum deviation.

4. The calibration method of a three-dimensional measuring device according to claim 2, characterized in that: The radial distortion coefficient and the Sham angle are determined by the collinearity of feature points, including: Determine the ideal feature point p based on the collinearity of the feature points of the row or column ij * ; According to the distance from the ideal feature point to the fitting line, the target optimization function is established: F(k1,k2,τ x ,t y )=∑∑|p ij * -L m | 2 Where k1 and k2 are radial distortion coefficients, τ x , τ y For Ras Sham, p ij * is the ideal feature point, L m is the fitting straight line; Nonlinear optimization is performed on the target optimization function to determine the radial distortion coefficient and the Sham angle.

5. The calibration method of a three-dimensional measuring device according to claim 1, characterized in that: The step of determining the distortion-free pixel coordinates corresponding to the pixel coordinates of the feature points on the imaging surface by using the distortion model and the distortion parameters includes: Determine the coordinates of the standard image plane distortion point, which are calculated according to the following formula: Where s1 is the scale factor, T S According to the rotation matrix R(τ x ,τ y ) determines the conversion relationship, τ x , τ y Ras Sham, c , v c is the coordinate of the principal point; u p , v p is the pixel coordinate of the feature point on the imaging surface, x p ′,y p ′ is the coordinate of the standard image distortion point; Determine the coordinates of the standard image plane distortion-free point, which are calculated according to the following formula: x p * =x p ′+δ u (x p ′,y p ′) and p * =and p ′+δ v (x p ',and p ′) In the formula, x p * ,y p * is the coordinate of the point without distortion on the standard image plane, δ u and δ v The model parameters include radial distortion coefficients k1 and k2; Determine the pixel coordinates of the undistorted point on the imaging surface, and the pixel coordinates of the undistorted point on the imaging surface are calculated according to the following formula: In the formula, is the pixel coordinate of the point without distortion on the imaging surface.

6. A calibration method for a three-dimensional measuring device according to claim 1, characterized in that: The calibration image data includes a coordinate map and a grayscale map, the coordinate map includes pixel coordinates and row coordinate values, the grayscale map includes pixel coordinates and grayscale values, and the determination of the row coordinate values ​​and grayscale values ​​includes: Acquire multiple groups of light fringe images of the calibration object, each group of light fringe images corresponding to an image of the calibration object at a different position; For each group of light fringe images, the corresponding row coordinate value and grayscale value are determined by the light fringe center extraction algorithm.

7. A calibration method for a three-dimensional measuring device according to claim 6, characterized in that: Determining the pixel coordinates of the feature points corresponding to the feature points on the imaging surface includes: According to the feature points of the calibration object corresponding to the feature point coordinates in the grayscale image, determine the column coordinates u of the feature points on the imaging surface p ; Determine the coordinates in the coordinate map according to the coordinates of the feature points in the grayscale map; The neighborhood coordinates of the coordinate value are interpolated by an algorithm to determine the row coordinates v of the feature point on the imaging surface. p .

8. The calibration method of a three-dimensional measuring device according to claim 1, characterized in that: The acquisition of the calibration image data includes: By scanning the moving calibration object, a plurality of sets of image data to be processed are obtained; The calibration image data is obtained by preprocessing the multiple groups of image data to be processed.

9. The calibration method of a three-dimensional measuring device according to claim 1, characterized in that: The calibration object comprises a platform base (21), a cushion block (22) and a calibration plate (23); One side of the platform base has a groove (211) and the other side has a baffle (212), the groove (211) being used to place the cushion block (22); the calibration plate (23) is obliquely arranged on the baffle (212) and the cushion block (22), and forms an inclination angle θ with the platform base (21); The spatial coordinates of the feature point on the calibration object are determined according to the inclination angle θ.

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