Calibration Method, Device, Electronic Device and Computer Readable Storage Medium for External Parameters of Linear Shape Measuring Machine

Abstract

The present application provides a calibration method, apparatus, and electronic device for external parameters of a linear shape measuring machine applicable to the field of measurement technology. The calibration method for the external parameters of a linear shape measuring machine includes obtaining a plurality of frames of contour point clouds collected by the linear shape measuring machine with respect to a calibration block during the movement of the calibration block along with the measurement table (S101), performing point cloud segmentation on each contour point cloud to obtain surface point clouds belonging to different measurement surfaces (S102), constructing a constraint relationship for each surface point cloud based on each feature point in the surface point cloud and the surface feature of the measurement surface to which the surface point cloud belongs (S103), constructing an objective optimization function for the estimated transformation relationship based on the estimated transformation relationship and the constraint relationships of the surface point clouds (S104), optimizing the objective optimization function, and obtaining, as external parameters between the linear shape measuring machine and the measurement table, the transformation parameters between the optimized linear shape measuring machine coordinate system and the measurement table coordinate system (S105). The present application can achieve calibration of the external parameters between the linear shape measuring machine and the measurement table.

Description

Technical Field

[0001] (Cross - reference to related applications) This application claims priority based on a Chinese patent application with an application number of 202210582401.7 and an invention title of "Calibration Method, Device and Electronic Equipment for External Parameters of a Linear Shape Measuring Machine", which was filed with the China National Intellectual Property Administration on May 26, 2022. Herein, all of its content is incorporated into this application by reference.

[0002] This application relates to the field of measurement technology, and particularly to a calibration method, device and electronic equipment for external parameters of a linear shape measuring machine.

Background Art

[0003] A linear shape measuring machine is a precision instrument for measuring the contour line shape and cross - sectional contour shape of various mechanical parts. Generally, when measuring a measurement object with a linear shape measuring machine, the measurement object is placed on a measurement table, and the measurement table can usually move the measurement object by rotation or parallel movement. The linear shape measuring machine continuously collects the contour point cloud of the measurement object during the movement of the measurement object, and then determines the contour information of the measurement object based on the contour point cloud collected by the linear shape measuring machine.

[0004] In the above process, it is necessary to use the contour point cloud collected by the linear shape measuring machine to determine the contour information of the measurement object. In the process of determining the contour information of the measurement object, it is necessary to use the external parameters between the linear shape measuring machine and the measurement table. Therefore, before determining the contour information, it is necessary to calibrate the external parameters between the linear shape measuring machine and the measurement table so as to determine the external parameters between the linear shape measuring machine and the measurement table. For this reason, how to calibrate the external parameters between the linear shape measuring machine and the measurement table has become an urgent technical problem to be solved.

Summary of the Invention

[0005] An embodiment of the present application aims to provide a calibration method, device, and electronic device for external parameters of a linear shape measuring machine to realize the calibration of the external parameters of the linear shape measuring machine. The specific technical solution is as follows.

[0006] In a first aspect, an embodiment of the present application provides a calibration method for external parameters of a linear shape measuring machine. The calibration method for external parameters of the linear shape measuring machine includes obtaining a plurality of frames of contour point clouds collected by the linear shape measuring machine for the calibration block during the movement of the calibration block by the measuring table; performing point cloud segmentation on each contour point cloud to obtain surface point clouds belonging to different measurement surfaces; for each surface point cloud, constructing a constraint relationship of the surface point cloud based on each feature point in the surface point cloud and the surface feature of the measurement surface to which the surface point cloud belongs, where the surface feature of each measurement surface is a feature indicating the geometric attribute of the measurement surface; constructing a target optimization function for the inferred transformation relationship based on the inferred transformation relationship between the linear shape measuring machine coordinate system and the measuring table coordinate system and the constraint relationship of each surface point cloud; optimizing the target optimization function, and obtaining the transformation parameters between the optimized linear shape measuring machine coordinate system and the measuring table coordinate system as the external parameters between the linear shape measuring machine and the measuring table.

[0007] Preferably, performing point cloud segmentation on each contour point cloud to obtain surface point clouds belonging to different measurement surfaces includes performing point cloud segmentation on each contour point cloud based on a segmentation policy corresponding to the type of the calibration block to obtain surface point clouds belonging to different measurement surfaces.

[0008] Preferably, based on a segmentation policy corresponding to the type of the calibration block, point cloud segmentation is performed on the contour point cloud to obtain surface point clouds belonging to different measurement planes. When the calibration block is a cone, for each feature point in the contour point cloud, curve fitting and line fitting are performed, and each feature point corresponding to the curve obtained by fitting is defined as a conical surface point cloud, and the feature points corresponding to the line obtained by fitting are defined as a bottom surface point cloud. When the calibration block is a sphere, for each feature point in the contour point cloud, circle fitting and line fitting are performed, and each feature point corresponding to the curve obtained by fitting is defined as a spherical surface point cloud, and each feature point corresponding to the line obtained by fitting is defined as a bottom surface point cloud.

[0009] Preferably, before constructing an objective optimization function for the estimated transformation relationship based on the estimated transformation relationship between the linear shape measuring machine coordinate system and the measurement table coordinate system and the constraint relationship of each surface point cloud, the calibration method for the external parameters of the linear shape measuring machine further includes determining the coordinate transformation relationship between the linear shape measuring machine coordinate system and the measurement table coordinate system as the estimated transformation relationship based on the obtained multiple frames of contour point clouds.

[0010] Preferably, determining the coordinate transformation relationship between the linear shape measuring machine coordinate system and the measurement table coordinate system as the estimated transformation relationship based on the obtained multiple frames of contour point clouds includes determining the initial transformation parameters between the linear shape measuring machine coordinate system and the measurement table coordinate system based on the multiple frames of contour point clouds, and obtaining the estimated transformation relationship by using the initial transformation parameters as the estimated values of the parameters in the transformation formula between the linear shape measuring machine coordinate system and the measurement table coordinate system.

[0011] Preferably, the initial conversion parameters include initial rotation parameters. Based on the contour point clouds of the multiple frames, determining the initial conversion parameters between the linear shape measurement machine coordinate system and the measurement table coordinate system includes: determining the slope of the fitting line corresponding to each bottom surface feature point in the contour point clouds of the multiple frames, and determining the first rotation angle of the linear shape measurement machine coordinate system with respect to the measurement table coordinate system based on the slope, where the bottom surface feature points are the feature points belonging to the measurement table; determining the feature point with the maximum height in the contour point clouds of the multiple frames as the maximum feature point, calculating the ratio between the height of the maximum feature point and the actual height of the calibration block, and determining the second rotation angle of the linear shape measurement machine coordinate system with respect to the measurement table coordinate system based on the ratio; and determining the initial rotation parameters based on the first rotation angle and the second rotation angle.

[0012] Preferably, the initial conversion parameters include initial translation parameters. Based on the contour point clouds of the multiple frames, determining the initial conversion parameters between the linear shape measurement machine coordinate system and the measurement table coordinate system includes: for the contour point clouds of any two adjacent frames, determining the first height difference of the feature point with the maximum height in the contour point clouds of the two frames; determining the curved surface arc length corresponding to the first height difference and the horizontal displacement within each sampling interval of the calibration block based on the surface features of the calibration block; determining the first displacement in the first direction of the linear shape measurement machine coordinate system with respect to the measurement table coordinate system based on the curved surface arc length and the horizontal displacement; and determining the second displacement in the second direction of the linear shape measurement machine coordinate system with respect to the measurement table coordinate system based on the curved surface arc length and the first height difference, where the first direction and the second direction are orthogonal; and determining the initial translation parameters based on the first displacement and the second displacement.

[0013] Preferably, optimizing the target optimization function and obtaining the conversion parameters between the optimized linear shape measurement machine coordinate system and the measurement table coordinate system as the external parameters between the linear shape measurement machine and the measurement table includes iteratively optimizing the conversion parameters in the target optimization function until the residual of the target optimization function is smaller than a preset threshold, setting the conversion parameters in the target optimization function as the external parameters between the linear shape measurement machine and the measurement table when the residual of the target optimization function is smaller than the preset threshold, obtaining a plurality of frames of contour point clouds collected by the linear shape measurement machine while the calibration block is moving along with the measurement table, performing feature point segmentation on each feature point in the contour point cloud of each frame to obtain a set of multiple contour feature points, where each feature point in each set of contour feature points belongs to the same surface, constructing a constraint relationship corresponding to each set of contour feature points based on each feature point in each set of contour feature points and the surface feature corresponding to each set of contour feature points, where the surface feature corresponding to each set of contour feature points is the surface feature of the surface to which each feature point in each set of contour feature points belongs, constructing a target optimization function based on the inferred coordinate system conversion relationship and the constraint relationship corresponding to each set of contour feature points, where the coordinate system conversion relationship is the conversion relationship between the linear shape measurement machine coordinate system and the measurement table coordinate system, and optimizing the target optimization function to obtain the conversion parameters between the optimized linear shape measurement machine coordinate system and the measurement table coordinate system as the external parameters of the linear shape measurement machine.

[0014] Preferably, performing feature point segmentation on each feature point in the contour point cloud of each frame to obtain a set of multiple contour feature points includes performing feature point segmentation on each feature point in the contour point cloud of each frame based on a segmentation policy corresponding to the type of the calibration block to obtain a set of multiple contour feature points.

[0015] Preferably, based on a segmentation policy corresponding to the type of the calibration block, performing feature point segmentation on each feature point in the contour point group of the frame to obtain a set of a plurality of contour feature points includes, when the calibration block is a cone, performing curve fitting and straight line fitting on each feature point in the contour point group of the frame, determining a set of conical surface feature points based on the feature points corresponding to the curve obtained by fitting, and determining a set of bottom surface feature points based on the feature points corresponding to the straight line obtained by fitting; and when the calibration block is a sphere, performing circle fitting and straight line fitting on each feature point in the contour point group of the frame, determining a set of spherical surface feature points based on the feature points corresponding to the curve obtained by fitting, and determining a set of bottom surface feature points based on the feature points corresponding to the straight line obtained by fitting.

[0016] Preferably, before constructing an objective optimization function based on the estimated coordinate system transformation relationship and the constraint relationship corresponding to each set of contour feature points, the calibration method for the external parameters of the linear shape measuring machine further includes estimating initial transformation parameters between the linear shape measuring machine coordinate system and the measurement table coordinate system based on the obtained contour point group, and obtaining an estimated coordinate system transformation relationship by using the initial transformation parameters as initial values for the transformation relationship transformation between the linear shape measuring machine coordinate system and the measurement table coordinate system.

[0017] Preferably, the initial conversion parameters include initial rotation parameters. Based on the obtained contour point cloud, inferring the initial conversion parameters between the linear shape measurement machine coordinate system and the measurement table coordinate system involves determining the slope of the fitting line corresponding to each bottom surface feature point in the obtained contour point cloud, and determining the first rotation angle of the linear shape measurement machine coordinate system with respect to the measurement table coordinate system based on the slope, where the bottom surface feature points are feature points belonging to the measurement table. Determining the feature point with the maximum height in each contour point cloud as the maximum feature point, calculating the ratio of the height of the maximum feature point to the corresponding height of the calibration block, and determining the second rotation angle of the linear shape measurement machine coordinate system with respect to the measurement table coordinate system based on the ratio. And determining the initial rotation parameters based on the first rotation angle and the second rotation angle.

[0018] Preferably, the initial conversion parameters include initial translation parameters. Based on the obtained contour point cloud, inferring the initial conversion parameters between the linear shape measurement machine coordinate system and the measurement table coordinate system involves determining the first height difference of the feature point with the maximum height in the contour point clouds of any two adjacent frames for the two adjacent frames of contour point clouds, determining the curved surface arc length corresponding to the first height difference and the horizontal displacement within the sampling interval of the calibration block based on the surface features of the calibration block, determining the first displacement in the first direction of the linear shape measurement machine coordinate system with respect to the measurement table coordinate system based on the curved surface arc length and the horizontal displacement, and determining the second displacement in the second direction of the linear shape measurement machine coordinate system with respect to the measurement table coordinate system based on the curved surface arc length and the first height difference, where the first direction and the second direction are orthogonal, and determining the initial translation parameters based on the first displacement and the second displacement.

[0019] Preferably, optimizing the target optimization function and obtaining, as the external parameters of the linearity shape measuring instrument, the conversion parameters between the optimized linearity shape measuring instrument coordinate system and the measurement table coordinate system includes iteratively optimizing the conversion parameters between the linearity shape measuring instrument coordinate system and the measurement table coordinate system until the residual of the target optimization function becomes smaller than a preset threshold value.

[0020] Preferably, the target optimization function further includes the sampling interval of the linearity shape measuring instrument, and the calibration method for the external parameters of the linearity shape measuring instrument is as follows: It further includes obtaining the optimized sampling interval by optimizing the target optimization function.

[0021] As a second aspect, the embodiments of the present application provide a calibration device for external parameters of a linearity shape measuring instrument. The calibration device for external parameters of the linearity shape measuring instrument includes: obtaining a plurality of frames of contour point clouds collected by the linearity shape measuring instrument for the calibration block during the movement of the calibration block by the measurement table, a point cloud acquisition module; performing point cloud segmentation on each contour point cloud to obtain surface point clouds belonging to different measurement surfaces, a point cloud segmentation module; for each surface point cloud, constructing a constraint relationship of the surface point cloud based on each feature point in the surface point cloud and the surface feature of the measurement surface to which the surface point cloud belongs, a relationship construction module; constructing a target optimization function for the inferred conversion relationship based on the inferred conversion relationship between the linearity shape measuring instrument coordinate system and the measurement table coordinate system and the constraint relationship of each surface point cloud, a function construction module; optimizing the target optimization function and obtaining, as the external parameters between the linearity shape measuring instrument and the measurement table, the conversion parameters between the optimized linearity shape measuring instrument coordinate system and the measurement table coordinate system, a function optimization module.

[0022] As a third aspect, the embodiment of the present application provides an electronic device including a processor, a communication interface, a memory, and a communication bus, where the processor, the communication interface, and the memory communicate with each other through the communication bus, the memory is used to store a computer program, and when the processor executes the program stored in the memory, it is used to implement the steps of the calibration method for the external parameters of the linearity shape measuring instrument described in the first aspect.

[0023] As a fourth aspect, the embodiment of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the calibration method for the external parameters of the linearity shape measuring instrument described in the first aspect.

[0024] Beneficial effects of the embodiments of the present application: In the calibration method of the external parameters of the linearity shape measuring instrument provided by the embodiment of the present application, during the movement of the calibration block along with the measuring table, the linearity shape measuring instrument collects a plurality of frames of contour point clouds for the calibration block, performs point cloud segmentation on each contour point cloud, obtains surface point clouds belonging to different measurement surfaces, and for each surface point cloud, based on each feature point in the surface point cloud and the surface feature of the measurement surface to which the surface point cloud belongs, constructs the constraint relationship of the surface point cloud. Based on the inferred conversion relationship between the linearity shape measuring instrument coordinate system and the measuring table coordinate system and the constraint relationship of each surface point cloud, constructs a target optimization function for the inferred conversion relationship, optimizes the target optimization function, and can obtain the conversion parameters between the optimized linearity shape measuring instrument coordinate system and the measuring table coordinate system as the external parameters between the linearity shape measuring instrument and the measuring table. Since the linearity shape measuring instrument collects a plurality of frames of contour point clouds of the calibration block, and the measurement surface to which the contour point cloud belongs can divide the contour point cloud of each frame into surface point clouds of different measurement surfaces, and the geometric model of the calibration block is known, that is, the surface features of each measurement surface of the calibration block are known, based on the surface features of each measurement surface, constructs the constraint relationship of the surface point cloud belonging to the measurement surface, constructs the target optimization function, and optimizes the conversion parameters between the linearity shape measuring instrument coordinate system and the measuring table coordinate system, and can obtain the external parameters between the linearity shape measuring instrument and the measuring table. From this, it can be seen that the embodiment of the present application realizes the calibration of the external parameters of the linearity shape measuring instrument, that is, realizes the calibration of the external parameters between the linearity shape measuring instrument and the measuring table.

[0025] Of course, in implementing any product or method of the present application, it is not necessary to achieve all of the above advantages simultaneously.

Brief Description of the Drawings

[0026] To more clearly explain the embodiments of the present application and the technical solutions of the prior art, the drawings used in the embodiments or the prior art will be briefly described below. The drawings described below are merely those according to some embodiments of the present application, and it is obvious that for those skilled in the art, other embodiments can be obtained based on these drawings without creative efforts.

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Embodiment for Carrying out the Invention

[0027] Hereinafter, in order to make the object, technical solution and advantages of the present application clearer, embodiments will be given and the present application will be described in detail with reference to the drawings. Of course, the described embodiments are only a part of the embodiments of the present application, not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor shall fall within the protection scope of the present application.

[0028] In order to realize the calibration of the external parameters of a linear shape measuring machine, the embodiments of the present application provide a calibration method, device and electronic device for the external parameters of a linear shape measuring machine.

[0029] It should be noted that the embodiments of the present application can be applied to electronic devices such as personal computers, servers, smart phones and other devices with data processing capabilities. In addition, the calibration method for the external parameters of the linear shape measuring machine provided in the embodiments of the present application may be realized by software, hardware or a combination thereof.

[0030] The calibration method for the external parameters of the linear shape measuring machine provided in the embodiments of the present application is obtaining a plurality of frames of contour point clouds collected by the linear shape measuring machine for the calibration block during the movement of the calibration block along with the measuring table; performing point cloud segmentation on each contour point cloud to obtain surface point clouds belonging to different measurement surfaces; for each surface point cloud, constructing a constraint relationship of the surface point cloud based on each feature point in the surface point cloud and the surface feature of the measurement surface to which the surface point cloud belongs, where the surface feature of each measurement surface is a feature indicating the geometric attributes of the measurement surface; constructing an objective optimization function for the estimated transformation relationship based on the estimated transformation relationship between the linear shape measuring machine coordinate system and the measuring table coordinate system and the constraint relationships of each surface point cloud; Optimizing the target optimization function and obtaining, as external parameters between the linearity shape measuring machine coordinate system and the measuring table coordinate system, the conversion parameters between them.

[0031] In the technical solution provided in the above embodiment of the present application, a plurality of frames of contour point clouds of a calibration block are collected by a linearity shape measuring machine, and the contour point clouds of each frame can be divided into surface point clouds of different measuring surfaces by the measuring surface to which the contour point clouds belong. Also, the geometric model of the calibration block is known, that is, the surface features of each measuring surface of the calibration block are known. Therefore, based on the surface features of each measuring surface, the constraint relationship of the surface point cloud belonging to the measuring surface is constructed, the target optimization function is constructed, and the conversion parameters between the linearity shape measuring machine coordinate system and the measuring table coordinate system are optimized to obtain the external parameters between the linearity shape measuring machine and the measuring table. Thus, it is found that the embodiment of the present application can realize the calibration of the external parameters of the linearity shape measuring machine.

[0032] Hereinafter, with reference to the drawings, the calibration method for the external parameters of the linearity shape measuring machine provided in the embodiment of the present application will be described in detail.

[0033] As shown in FIG. 1, the calibration method for the external parameters of the linearity shape measuring machine provided in the embodiment of the present application may include the following steps.

[0034] S101, while the calibration block is being moved by the measuring table, obtain a plurality of frames of contour point clouds collected by the linearity shape measuring machine for the calibration block. The calibration block is an object having a specific geometric shape such as a cone, a sphere, a crater, a frustum, etc. When it is necessary to calibrate the external parameters of the linear shape measuring machine, since the linear shape measuring machine can only collect one linear contour point group at a time, in order to obtain the complete contour information of the calibration block, it is necessary to move the linear shape measuring machine and the calibration block relative to each other. Thereby, throughout the process of the movement of the linear shape measuring machine, data collection is continuously performed on the calibration block to obtain the complete contour information of the calibration block.

[0035] In the embodiment of the present application, by arranging the calibration block on a measuring table that can rotate or translate in parallel, the linear shape measuring machine can continuously perform data collection on the calibration block while the calibration block is moving along with the measuring table. When the measuring table rotates, the calibration block can perform circular motion along with the rotation of the measuring table. When the measuring table performs translational motion, the calibration block can translate linearly along with the measuring table.

[0036] Figure 2 is a schematic diagram of the acquisition by the linearity shape measuring machine provided in the embodiment of the present application. In the figure, the bottom disk is the measurement table, the cone on the bottom disk is the calibration block, the cube represents the linearity shape measuring machine, the triangle under the cube is the data sampling surface of the linearity shape measuring machine, that is, the laser plane of the linearity shape measuring machine. The measurement table rotates around the central axis, and the cone moves in a circular motion as the measurement table rotates. When the cone passes through the measurement area of the linearity shape measuring machine during the movement, that is, when the cone contacts the data sampling surface of the linearity shape measuring machine, the linearity shape measuring machine can collect the contour point cloud of the cone at a certain sampling frequency. The cone can be used by the linearity shape measuring machine to collect multiple frames of contour point clouds of the cone from the time it enters the data sampling surface until it leaves the data sampling surface. The data sampling surface is the optical plane where the linearity shape measuring machine emits laser light. The coordinate system Oobj in Figure 2 is the calibration block coordinate system, the coordinate system Osys is the measurement table coordinate system, the coordinate system Osnr is the projection coordinate system of the linearity shape measuring machine, and the directions of the X-axis, Y-axis, and Z-axis of the projection coordinate system of the linearity shape measuring machine coincide with the linearity shape measuring machine coordinate system (not shown in Figure 2), and its origin is the projection point of the center point of the linearity shape measuring machine on the measurement table.

[0037] The above linearity shape measuring machine can measure the depth data in a straight line by emitting laser light. Figure 3 is a schematic diagram of the contour point cloud collected by the linearity shape measuring machine provided in the embodiment of the present application. In the figure, the horizontal coordinate is the X-axis of the linearity shape measuring machine coordinate system, and the vertical coordinate is the Z-axis of the linearity shape measuring machine coordinate system. The contour point cloud of each frame collected by the linearity shape measuring machine is a set of feature points. For the feature points in any contour point cloud, since the coordinates of the feature points are (X, 0, Z), for the linearity shape measuring machine, it can be seen that for each collected feature point, the data is only in the X-axis direction and Z-axis direction of the linearity shape measuring machine coordinate system, and is 0 in the Y-axis direction of the linearity shape measuring machine coordinate system.

[0038] S102. Perform point cloud segmentation on each contour point cloud to obtain the surface point clouds belonging to different measurement surfaces. In order to obtain the constraint relationships corresponding to the characteristic points of each contour point cloud, in the embodiments of the present application, point cloud segmentation is performed on each contour point cloud, and surface point clouds belonging to different measurement planes can be obtained.

[0039] Since the linearity shape measuring machine measures depth data in a straight line by emitting a laser, it means that each frame of contour point cloud collected by the linearity shape measuring machine usually contains characteristic points belonging to different measurement planes. The measurement plane consists of the geometric surface of the calibration block and the measurement table. Taking the contour point cloud shown in FIG. 3 as an example, the contour point cloud generally consists of a left straight line segment, an intermediate curve segment, and a right straight line segment. The characteristic points corresponding to the left straight line segment and the right straight line segment are the characteristic points generated by measuring the measurement table when the linearity shape measuring machine measures, and the characteristic points corresponding to the intermediate curve segment are the characteristic points generated by measuring the geometric surface of the calibration block when the linearity shape measuring machine measures.

[0040] In order to correctly construct the constraint relationship, in the embodiments of the present application, it is necessary to perform point cloud segmentation on each contour point cloud to obtain surface point clouds belonging to different measurement planes.

[0041] When different calibration blocks are measured, the obtained contour point clouds usually include surface point clouds of different measurement planes. For example, the contour point cloud obtained by measuring a conical calibration block includes a conical point cloud and a bottom surface point cloud. The conical point cloud includes the characteristic points of the cone, and the bottom surface point cloud includes the characteristic points of the measurement table. The contour point cloud obtained by measuring a spherical calibration block includes a spherical point cloud and a bottom surface point cloud. The spherical point cloud includes the characteristic points of the sphere, and the bottom surface point cloud includes the characteristic points of the measurement table.

[0042] Preferably, for each contour point cloud, based on the segmentation policy corresponding to the type of the calibration block, point cloud segmentation is performed on the contour point cloud to obtain surface point clouds belonging to different measurement planes.

[0043] As described above, when measuring different calibration blocks, the obtained contour point cloud usually includes the surface point clouds of different measurement surfaces. Therefore, it is necessary to divide the contour point cloud according to different division policies.

[0044] FIG. 4 is a contour schematic diagram of each type of calibration block provided in the embodiment of the present application. In the figure, the straight line segment is a position where the optical plane of the linearity shape measuring machine (i.e., the laser plane of the linearity shape measuring machine) can intersect with the calibration block. The frustum calibration block intersects with the laser plane of the linearity shape measuring machine at different angles and positions, and there are at most 8 types of surface contours that can appear. That is, there are at most 8 types of surface contours of the contour point cloud that can be collected by the linearity shape measuring machine. The crater calibration block has 5 types of surface contours, the conical calibration block has 3 types of surface contours, and the spherical calibration block has 2 types of surface contours.

[0045] For point cloud division, the simpler the intersecting contour is, the easier the point cloud division becomes, and the higher the robustness to various materials and environments is.

[0046] In one embodiment, when the calibration block is a cone, for each feature point in the contour point cloud, curve fitting and straight line fitting are performed. Each feature point corresponding to the curve obtained by fitting is determined as the conical surface point cloud, and the feature point corresponding to the straight line obtained by fitting is determined as the bottom surface point cloud. When the calibration block is a cone, it means that the obtained contour point group includes the characteristic points of the cone and the characteristic points of the measuring table. By performing curve fitting on the characteristic points of the cone, a conic curve can be obtained, and by performing line fitting on the characteristic points of the measuring table, a straight line can be obtained. Therefore, after the contour point group is obtained, through curve fitting and line fitting, from each characteristic point included in the contour point group, each characteristic point corresponding to the curve obtained by fitting is determined as a conical surface point group, and the characteristic points corresponding to the straight line obtained by fitting can be determined as a bottom surface point group.

[0047] In one embodiment, when the calibration block is a sphere, for each characteristic point in the contour point group, circle fitting and line fitting are performed, and each characteristic point corresponding to the curve obtained by fitting is used as a spherical surface point group, and each characteristic point corresponding to the straight line obtained by fitting is used as a bottom surface point group.

[0048] When the calibration block is a sphere, it means that the obtained contour point group includes the characteristic points of the sphere and the characteristic points of the measuring table. By performing curve fitting on the characteristic points of the sphere, a spherical surface curve can be obtained, and by performing line fitting on the characteristic points of the measuring table, a straight line can be obtained. Therefore, after the contour point group is obtained, through curve fitting and line fitting, from each characteristic point included in the contour point group, each characteristic point corresponding to the curve obtained by fitting is determined as a spherical surface point group, and each characteristic point corresponding to the straight line obtained by fitting is used as a bottom surface point group.

[0049] In one embodiment, when the calibration block is a frustum of a cone, line fitting is performed on each characteristic point in the contour point group to obtain a plurality of fitting straight lines. The plurality of fitting straight lines are partitioned to obtain an end straight line including endpoints and a middle straight line not including endpoints. Each characteristic point corresponding to the middle straight line is used as an upper surface point group, and each characteristic point corresponding to the end straight line is used as a bottom surface point group.

[0050] When the calibration block is a frustum, it means that the obtained contour point cloud includes the feature points of the frustum and the measurement table. The feature points of the frustum and the measurement table can both be obtained as fitting lines by straight line segments. Among the multiple fitting lines, since the end point line including the end points belongs to the line of the measurement table, each feature point corresponding to the end point line is used as the bottom surface point cloud. Since the middle line without end points belongs to the line of the frustum, each feature point corresponding to the middle line is used as the upper surface point cloud.

[0051] S103. For each surface point cloud, based on each feature point in the surface point cloud and the surface feature of the measurement surface to which the surface point cloud belongs, construct the constraint relationship of the surface point cloud. The surface feature of each measurement surface is a feature indicating the geometric attribute of the measurement surface. After dividing the contour point cloud to obtain the surface point cloud, the constraint relationship of the surface point cloud may be constructed. Each feature point in each surface point cloud corresponds to the feature point on the corresponding measurement surface, and since the surface feature of each measurement surface can be determined based on the geometric model of the calibration block, the constraint relationship of the surface point cloud can be constructed based on each feature point in the surface point cloud and the surface feature of the measurement surface to which the surface point cloud belongs.

[0052] Exemplarily, the constructed constraint relationship is represented by the following formula.

[0053]

Equation

[0054] The specific constraint relationship depends on the type of calibration block. Different calibration blocks have different measurement surfaces, and different measurement surfaces have different surface features.

[0055] When the measurement surface is a plane, the constraint relationship between the surface features of the plane and the corresponding surface point cloud is expressed by the following formula.

[0056]

Number

[0057] When the feature point of the surface point cloud corresponding to the measurement surface is a conical surface, the constraint relationship between the surface features of the conical surface and the corresponding surface point cloud is expressed by the following formula.

[0058]

Number

[0059] When the feature point of the surface point cloud corresponding to the measurement surface is a spherical surface, the constraint relationship between the surface features of the spherical surface and the corresponding surface point cloud is expressed by the following formula.

[0060]

Number

[0061] In each of the above constraint relationships, the coordinates of each feature point are the coordinates in the calibration block coordinate system O of the corresponding feature point. However, since the coordinates of each feature point in the surface point cloud are the coordinates in the linearity shape measuring machine coordinate system S, it is necessary to convert the linearity shape measuring machine coordinate system S into the coordinates in the calibration block coordinate system O.

[0062] Exemplarily, referring to FIG. 2, the calibration block coordinate system is denoted as O, that is, Oobj in FIG. 2, the measurement pedestal coordinate system is denoted as M, that is, Osys in FIG. 2, the linearity shape measuring machine coordinate system is S, and the projection coordinate system fixedly connected relative to the linearity shape measuring machine coordinate system is M', that is, Osnr in FIG. 2. However, the directions of the X-axis, Y-axis, and Z-axis of the coordinate system M' are the same as those of the linearity shape measuring machine coordinate system S, and its origin is the projection point of the center point of the linearity shape measuring machine on the measurement pedestal.

[0063] The conversion relationship for changing the linearity shape measuring machine coordinate system into the calibration block coordinate system is represented by the following formula.

[0064]

Equation

[0065] When the measurement pedestal rotates, the above conversion relationship is expanded as follows.

[0066]

Equation

[0067] The above conversion relationship includes the external parameters between the coordinate system of the linearity shape measuring machine base and the measurement table coordinate system, and is the object to be calibrated.

[0068] For each surface point group, based on the above conversion relationship, it is converted into each feature point in the calibration block coordinate system and substituted into the above-mentioned respective constraint relationships, so as to obtain the constraint relationship of each surface point group.

[0069] S104. Based on the estimated conversion relationship between the coordinate system of the linearity shape measuring machine base and the measurement table coordinate system and the constraint relationship of each surface point group, construct an objective optimization function for the estimated conversion relationship. Since the estimated conversion relationship includes the estimated values of the external parameters between the coordinate system of the linearity shape measuring machine base and the measurement table coordinate system, the estimated values can be substituted into the constraint relationships of the above-mentioned respective surface point groups to obtain the constraint relationships including the estimated values. The above-mentioned estimated values are obtained by estimating each collected contour point group, and the specific implementation forms will be described in detail in the subsequent embodiments. Of course, the estimated values may also be null.

[0070] After obtaining the constraint relationship including the estimated value, an objective optimization function may be constructed based on each preset constraint relationship. Preferably, based on the constraint relationship of each surface point group, the residual of each surface point group that can be constructed is represented by the following formula.

[0071]

Number

[0072] Furthermore, the constructed target optimization function is represented by the following formula.

[0073]

Number

[0074] The target optimization function is used to calculate the minimum value of the sum of the residuals corresponding to each surface point group.

[0075] S105. Optimize the target optimization function, and obtain the transformation parameter between the optimized linear shape measuring machine coordinate system and the measuring table coordinate system as the external parameter between the linear shape measuring machine and the measuring table.

[0076] After obtaining the target optimization function, the conversion parameters in the target optimization function may be iteratively optimized. After each iteration, it may be determined whether the residual of the target optimization function is smaller than a preset threshold. If the residual of the target optimization function is smaller than the preset threshold, it means that each parameter in the currently iterated target optimization function meets the conditions. In this case, when the residual of the target optimization function is smaller than the preset threshold, the conversion parameters in the target optimization function can be used as the external parameters between the linearity shape measuring instrument and the measuring table. If the residual of the target optimization function is greater than or equal to the preset threshold, the iteration continues until the residual of the target optimization function is smaller than the preset threshold. The above preset threshold may be determined according to needs and experience. The residual of the target optimization function is the sum of the residuals corresponding to each surface point cloud.

[0077] In the above technical solution provided by the embodiments of the present application, a plurality of frames of contour point clouds of the calibration block are collected by a linearity shape measuring instrument, and each frame of contour point cloud can be divided into surface point clouds of different measuring surfaces according to the measuring surface to which the contour point cloud belongs. Also, the geometric model of the calibration block is known, that is, since the surface characteristics of each measuring surface of the calibration block are known, based on the surface characteristics of each measuring surface, a constraint relationship of the surface point cloud belonging to the measuring surface is constructed, a target optimization function is constructed, and the conversion parameters between the linearity shape measuring instrument coordinate system and the measuring table coordinate system are optimized to obtain the external parameters between the linearity shape measuring instrument and the measuring table. Thus, it can be seen that the embodiments of the present application have realized the calibration of the external parameters of the linearity shape measuring instrument.

[0078] Another method for calibrating the external parameters of a linearity shape measuring instrument further provided by the embodiments of the present application is that after step S104, Based on the obtained plurality of frames of contour point clouds, determining the coordinate system conversion relationship between the linearity shape measuring instrument coordinate system and the measuring table coordinate system as an estimated conversion relationship.

[0079] After obtaining the contour point groups of multiple frames, based on the obtained contour point groups of multiple frames, first, the coordinate transformation relationship between the linearity shape measuring machine coordinate system and the measuring table coordinate system may be estimated as an estimated transformation relationship. In the estimated transformation relationship, the external parameters between the linearity shape measuring machine coordinate system and the measuring table coordinate system are estimated values and are not accurate. However, performing the optimization of the subsequent target optimization function based on the estimated values can improve the optimization efficiency and also improve the accuracy of the optimization.

[0080] In one embodiment, as shown in FIG. 5, determining the coordinate transformation relationship between the linearity shape measuring machine coordinate system and the measuring table coordinate system as an estimated transformation relationship based on the obtained contour point groups of multiple frames may include steps S501 to S502.

[0081] S501. Based on the contour point groups of multiple frames, determine the initial transformation parameters between the linearity shape measuring machine coordinate system and the measuring table coordinate system. In the embodiments of the present application, the measuring table coordinate system has the center of the measuring table as the origin, the direction perpendicular to the measuring table and upward as the Z-axis direction, the direction pointing to the linearity shape measuring machine as the X-axis direction, and the Y-axis direction in the measuring table coordinate system is determined based on the Z-axis and X-axis directions. The calibration block coordinate system has the geometric center of the calibration block as the origin, and the directions of its X-axis, Y-axis, and Z-axis are the same as those of the measuring table coordinate system. The linearity shape measuring machine coordinate system has the geometric center of the linearity shape measuring machine as the origin, the direction of its X-axis is parallel to the X-axis direction of the measuring table coordinate system, and the positive direction of the X-axis of the linearity shape measuring machine coordinate system may be determined based on the contour point group.

[0082] Preferably, in one embodiment, FIG. 6 is a schematic diagram for determining the positive direction of the X-axis provided by the embodiments of the present application.

[0083] In FIG. 6, the outermost semi-circular arc is the locus line along which the calibration block moves in a circular motion. In the figure, the circle is the plan view of the calibration block of a cone or a sphere. The plurality of line segments diverging outward from the center point of the semi-circular arc are the lines connecting the apex of the calibration block at each sampling time and the center point of the measurement plane. The line segment with an arrow is the position where the linearity shape measuring instrument samples the calibration block at each sampling time. The origin at the line segment with an arrow is the position where the characteristic point of the maximum value in the contour point group obtained by sampling with the linearity shape measuring instrument is located at each sampling time.

[0084] In this form, the coordinates of the characteristic point of the maximum value in the contour point group of each frame can be calculated, and the curvature of the line connecting the characteristic points of the maximum value in the contour point group of each frame can be determined, and the curvature direction can be determined as the positive direction of the X-axis of the linearity shape measuring instrument coordinate system. For example, when the curvature direction is the negative direction, the positive direction of the X-axis of the linearity shape measuring instrument coordinate system points to the opposite of the center of the measurement table. Conversely, when the curvature direction is the positive direction, the positive direction of the X-axis of the linearity shape measuring instrument coordinate system points to the center point of the measurement table. The positive direction of the X-axis of the linearity shape measuring instrument coordinate system determined in this way can be used to judge the direction of the subsequent Euler angles.

[0085] The initial conversion parameters between the linearity shape measuring instrument coordinate system and the measurement table coordinate system may include initial rotation parameters and / or initial translation parameters. Each rotation parameter indicates the angle by which each direction axis should rotate when converting the linearity shape measuring instrument coordinate system to the measurement table coordinate system. Each translation parameter indicates the moving distance of each origin along each direction axis when converting the linearity shape measuring instrument coordinate system to the measurement table coordinate system. For the sake of easy understanding, the process of determining the initial conversion parameters will be described in detail in the subsequent embodiments.

[0086] S502. Obtain an estimated conversion relationship by using the initial conversion parameters as the estimated values of the parameters in the conversion formula between the linearity shape measuring instrument coordinate system and the measurement table coordinate system.

[0087] After obtaining the initial conversion parameters between the linearity shape measurement machine coordinate system and the measurement table coordinate system, an estimated conversion relationship can be obtained by using the initial conversion parameters as the estimated values of the parameters in the conversion formula between the linearity shape measurement machine coordinate system and the measurement table coordinate system.

[0088] According to the above technical solution provided by the embodiment of the present application, calibration of the external parameters between the linearity shape measurement machine and the measurement table can be realized. At the same time, the estimated conversion relationship between the linearity shape measurement machine coordinate system and the measurement table coordinate system can be estimated from the contour point clouds of multiple frames, and the optimization efficiency and accuracy for the target optimization function can be improved.

[0089] Preferably, in one embodiment, the initial conversion parameters include initial rotation parameters. In this case, based on the embodiment shown in FIG. 5, as shown in FIG. 7, the embodiment of the present application further provides a calibration method for the external parameters of another linearity shape measurement machine, and step S501 may include steps S701 to S703.

[0090] S701, Determine the slope of the fitting line corresponding to each bottom surface feature point in the contour point clouds of multiple frames, and determine the first rotation angle of the linearity shape measurement machine coordinate system with respect to the measurement table coordinate system based on the slope. The bottom surface feature points are feature points belonging to the measurement table. As shown in FIG. 3, the slope of the fitting line corresponding to the bottom surface point cloud may be calculated and denoted as ry. Then, ry may be converted into an angle value and used as the first rotation angle of the linearity shape measurement machine coordinate system with respect to the measurement table coordinate system.

[0091] S702, Determine the feature point with the maximum height in the contour point clouds of multiple frames as the maximum feature point, calculate the ratio between the height of the maximum feature point and the actual height of the calibration block, and determine the second rotation angle of the linearity shape measurement machine coordinate system with respect to the measurement table coordinate system based on the ratio. Preferably, the second rotation angle may be calculated using the maximum vertex and denoted as rx. Each conic curve of the contour point group of each frame has a vertex, and the distance from the vertex to the bottom straight line is the height of the vertex obtained by measurement. Among all the contour point groups, the vertex with the maximum height is the position of the cone top. Therefore, by calculating the ratio of this height to the actual height of the cone, rx can be obtained. Then, rx can be converted into an angular value to be the second rotation angle of the linearity shape measuring machine coordinate system with respect to the measuring pedestal coordinate system.

[0092] S703. Determine the initial rotation parameters based on the first rotation angle and the second rotation angle.

[0093] In this step, after obtaining the first rotation angle and the second rotation angle, the first rotation angle and the second rotation angle may be used as the initial rotation parameters.

[0094] According to the above technical solution provided by the embodiments of the present application, calibration of the external parameters between the linearity shape measuring machine and the measuring pedestal can be realized. At the same time, since the initial rotation parameters between the linearity shape measuring machine coordinate system and the measuring pedestal coordinate system can be determined by the contour point groups of multiple frames, a basis for improving the optimization efficiency and accuracy for the target optimization function is realized.

[0095] Preferably, in one embodiment, the initial conversion parameters include initial translation parameters. In this case, based on the embodiment shown in FIG. 5, as shown in FIG. 8, the embodiments of the present application further provide a calibration method for the external parameters of another linearity shape measuring machine, and step S502 may include steps S801 to S805.

[0096] S801. For any two adjacent frames of contour point groups, determine the first height difference of the feature point with the maximum height in the two frames of contour point groups. FIG. 9 is a schematic diagram of the height difference provided by the embodiment of the present application. The solid line and the dotted line on the left side in FIG. 9 respectively represent the positions of the calibration block in the plan view during two adjacent measurements in the process of the calibration block moving along with the measurement plane. The right side in FIG. 9 is a schematic diagram of the calibration block. The dotted line in the calibration block represents the center line of the calibration block, and the two solid line segments in the calibration block represent the measurement positions of the linearity shape measuring instrument during two adjacent measurements in the process of the calibration block moving along with the measurement plane. The first height difference is the height difference of the feature points with the largest height difference value in each adjacent contour point group, and is denoted as the first height difference, that is, dz in FIG. 9.

[0097] S802. Based on the surface features of the calibration block, determine the curved surface arc length corresponding to the first height difference and the horizontal displacement within each sampling interval of the calibration block. After determining the first height difference, combine the surface features of the measurement surface of the calibration block to determine the curved surface arc length corresponding to the first height difference, that is, the distance on the surface of the calibration block between the two feature points with the largest height difference value, that is, dx in FIG. 9, and the horizontal displacement within the sampling interval of the calibration block, that is, dy in FIG. 9.

[0098] S803. Based on the curved surface arc length and the horizontal displacement, determine the first displacement in the first direction of the linearity shape measuring instrument coordinate system with respect to the measurement table coordinate system. Calculate the ratio between the curved surface arc length and the horizontal displacement, and obtain the first displacement in the first direction of the linearity shape measuring instrument coordinate system with respect to the measurement table coordinate system. Preferably, the first direction may be the Z-axis direction, that is, calculate the displacement in the Z-axis direction of the linearity shape measuring instrument coordinate system with respect to the measurement table coordinate system as the first displacement.

[0099] S804. Based on the curved surface arc length and the first height difference, determine the second displacement in the second direction of the linearity shape measuring instrument coordinate system with respect to the measurement table coordinate system, and the first direction and the second direction are perpendicular to each other. Calculate the ratio between the curved surface arc length and the first height difference, and further determine the second displacement in the second direction of the linearity shape measuring machine coordinate system with respect to the measurement pedestal coordinate system. Preferably, the second direction may be the X-axis direction, that is, determine the displacement in the X-axis direction of the linearity shape measuring machine coordinate system with respect to the measurement pedestal coordinate system as the second displacement.

[0100] S805. Determine the initial translation parameters based on the first displacement and the second displacement.

[0101] In this step, the first displacement and the second displacement may be used as the initial translation parameters. Further, the initial translation parameters may include ty = 0, that is, the translation distance in the Y-axis direction of the linearity shape measuring machine coordinate system with respect to the measurement pedestal coordinate system may be 0.

[0102] According to the above technical solution provided by the embodiments of the present application, calibration of the external parameters between the linearity shape measuring machine and the measurement pedestal can be realized. At the same time, since the initial translation parameters between the linearity shape measuring machine coordinate system and the measurement pedestal coordinate system can be determined by the contour point clouds of multiple frames, a basis for improving the optimization efficiency and accuracy for the target optimization function is realized.

[0103] Preferably, in other embodiments where the initial conversion parameters include the initial translation parameters, the calibration block may be a truncated cone. In this case, based on the embodiment shown in FIG. 5, as shown in FIG. 10, the embodiments of the present application further provide a method for calibrating the external parameters of other linearity shape measuring machines, and step S502 may include steps S1001 to S1003.

[0104] S1001. Based on the contour point clouds of each frame, determine the abscissa of the specified point on the truncated cone as the third displacement, and the specified point is any point on the truncated cone. In this step, based on the contour point groups of each frame, the abscissa of the specified point on the frustum can be determined as the third displacement. The specified point can be any point on the frustum, for example, the center point of the top surface of the frustum, or any vertex among the four vertices of the top surface, or any vertex among the four vertices of the bottom surface of the frustum. Preferably, the above third displacement may be 0.

[0105] Preferably, when the specified point is the center point of the top surface of the frustum, the contour point group existing at the middle position can be determined from the contour point groups of each frame, and the abscissa of the feature point at the middle position can be determined as the third displacement.

[0106] S1002. Based on the surface point group belonging to the top surface of the frustum and the bottom point group belonging to the measuring table, the height of the frustum can be determined as the fourth displacement. Preferably, the height difference between the top surface of the frustum and the bottom surface of the measuring table can be determined as the height of the frustum, that is, the fourth displacement, by the surface point group belonging to the top surface of the frustum and the bottom point group belonging to the measuring table.

[0107] S1003. Determine the initial translation parameters based on the third displacement and the fourth displacement.

[0108] In this step, the third displacement and the fourth displacement may be used as the initial translation parameters. Further, the initial translation parameters may include ty = 0, that is, the translation distance in the Y-axis direction of the linearity shape measuring machine coordinate system with respect to the measuring table coordinate system may be 0.

[0109] According to the above technical solution provided in the embodiments of the present application, calibration of the external parameters between the linearity shape measuring machine and the measuring table can be realized. At the same time, since the initial translation parameters between the linearity shape measuring machine coordinate system and the measuring table coordinate system can be determined by the contour point groups of multiple frames, a basis for improving the optimization efficiency and accuracy for the target optimization function is realized.

[0110] According to the method provided in the above embodiments of the present application, as shown in FIG. 11, the embodiments of the present application further provide a calibration device for external parameters of a linear shape measuring machine. The calibration device for external parameters of the linear shape measuring machine includes obtaining a plurality of frames of contour point clouds collected by the linear shape measuring machine for the calibration block during the movement of the calibration block by the measuring table, a point cloud acquisition module 1101, and performing point cloud segmentation on each contour point cloud to obtain surface point clouds belonging to different measurement surfaces, a point cloud segmentation module 1102, and constructing a constraint relationship of each surface point cloud based on each feature point in the surface point cloud and the surface feature of the measurement surface to which the surface point cloud belongs, a relationship construction module 1103, and constructing an objective optimization function for the inferred transformation relationship based on the inferred transformation relationship between the linear shape measuring machine coordinate system and the measuring table coordinate system and the constraint relationships of each surface point cloud, a function construction module 1104, and optimizing the objective optimization function to obtain transformation parameters between the optimized linear shape measuring machine coordinate system and the measuring table coordinate system as external parameters between the linear shape measuring machine and the measuring table, including a function optimization module 1105.

[0111] Preferably, the point cloud segmentation module is specifically used to perform point cloud segmentation on each contour point cloud based on a segmentation policy corresponding to the type of the calibration block to obtain surface point clouds belonging to different measurement surfaces.

[0112] Preferably, when the calibration block is a cone, the point cloud segmentation module specifically performs curve fitting and straight line fitting on each feature point in the contour point cloud, determines each feature point corresponding to the curve obtained by fitting as a conical surface point cloud, and determines each feature point corresponding to the straight line obtained by fitting as a bottom surface point cloud; when the calibration block is a sphere, the point cloud segmentation module specifically performs circular fitting and straight line fitting on each feature point in the contour point cloud, determines each feature point corresponding to the curve obtained by fitting as a spherical surface point cloud, and determines each feature point corresponding to the straight line obtained by fitting as a bottom surface point cloud; when the calibration block is a frustum of a cone, the point cloud segmentation module specifically performs straight line fitting on each feature point in the contour point cloud to obtain a plurality of fitting straight lines, divides the plurality of fitting straight lines to obtain an end straight line including endpoints and a middle straight line not including endpoints, determines each feature point corresponding to the middle straight line as an upper surface point cloud, and determines each feature point corresponding to the end straight line as a bottom surface point cloud.

[0113] Preferably, the calibration device for the external parameters of the linear shape measuring machine Before executing, by the function construction module, constructing a target optimization function for the estimated transformation relationship based on the estimated transformation relationship between the linear shape measuring machine coordinate system and the measurement table coordinate system and the constraint relationship of each surface point cloud, based on the obtained plurality of frames of contour point clouds, a relationship estimation module is further included to determine the coordinate transformation relationship between the linear shape measuring machine coordinate system and the measurement table coordinate system as the estimated transformation relationship.

[0114] Preferably, the relationship estimation module specifically determines the initial transformation parameters between the linear shape measuring machine coordinate system and the measurement table coordinate system based on the plurality of frames of contour point clouds, and uses the initial transformation parameters as the estimated values of the parameters in the transformation formula between the linear shape measuring machine coordinate system and the measurement table coordinate system to obtain the estimated transformation relationship.

[0115] Preferably, the initial conversion parameter includes an initial rotation parameter, Specifically, the relationship inference module determines the inclination of the fitting line corresponding to each bottom surface feature point in the contour point cloud of the plurality of frames, and determines the first rotation angle of the linearity shape measurement machine coordinate system with respect to the measurement table coordinate system based on the inclination, where the bottom surface feature point is a feature point belonging to the measurement table, determines the feature point with the maximum height in the contour point cloud of the plurality of frames as the maximum feature point, calculates the ratio of the height of the maximum feature point to the actual height of the calibration block, and determines the second rotation angle of the linearity shape measurement machine coordinate system with respect to the measurement table coordinate system based on the ratio, and is used to determine the initial rotation parameter based on the first rotation angle and the second rotation angle.

[0116] Preferably, the initial conversion parameter includes an initial translation parameter, Specifically, the relationship inference module is configured to determine, for any two adjacent frames of contour point groups, a first height difference of a feature point with the maximum height in the contour point groups of the two frames; determine, based on the surface features of the calibration block, a curved surface arc length corresponding to the first height difference and a horizontal displacement within each sampling interval of the calibration block; determine, based on the curved surface arc length and the horizontal displacement, a first displacement in a first direction of the linear shape measuring machine coordinate system with respect to the measurement table coordinate system; determine, based on the curved surface arc length and the first height difference, a second displacement in a second direction of the linear shape measuring machine coordinate system with respect to the measurement table coordinate system, where the first direction and the second direction are orthogonal; and determine the initial translation parameters based on the first displacement and the second displacement. Alternatively, based on the contour point groups of each frame, it is configured to determine the abscissa of a specified point in the frustum as a third displacement, where the specified point is any point in the frustum; determine the height of the frustum as a fourth displacement based on the surface point group belonging to the top surface of the frustum and the bottom surface point group belonging to the measurement table; and determine the initial translation parameters based on the third displacement and the fourth displacement.

[0117] Preferably, the function optimization module is specifically configured to iteratively optimize the transformation parameters in the target optimization function until the residual of the target optimization function is smaller than a preset threshold, and use the transformation parameters in the target optimization function when the residual of the target optimization function is smaller than the preset threshold as the external parameters between the linear shape measuring machine and the measurement table.

[0118] In the above technical solution provided by the embodiments of the present application, a plurality of frames of contour point clouds of a calibration block are collected by a linearity shape measuring machine, and according to the measurement surface to which the contour point cloud belongs, the contour point cloud of each frame can be divided into surface point clouds of different measurement surfaces. Further, the geometric model of the calibration block is known, that is, the surface features of each measurement surface of the calibration block are known. Therefore, based on the surface features of each measurement surface, a constraint relationship of the surface point cloud belonging to the measurement surface is constructed, a target optimization function is constructed, and the conversion parameters between the linearity shape measuring machine coordinate system and the measurement pedestal coordinate system are optimized to obtain the external parameters between the linearity shape measuring machine and the measurement pedestal. Thus, it can be seen that the embodiments of the present application have realized the calibration of the external parameters of the linearity shape measuring machine.

[0119] As shown in FIG. 12, an embodiment of the present application is an electronic device including a processor 1201, a communication interface 1202, a memory 1203, and a communication bus 1204, where the processor 1201, the communication interface 1202, and the memory 1203 communicate with each other through the communication bus 1204. The memory 1203 is used to store a computer program. When the processor 1201 executes the program stored in the memory 1203, it is used to implement the steps of the method provided by the above embodiments of the present application.

[0120] The communication bus mentioned in the above electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. This communication bus can be divided into an address bus, a data bus, a control bus, etc. In the figure, for the convenience of illustration, it is shown by a thick line, but it does not mean that there is only one bus or one type of bus.

[0121] The communication interface is used for communication between the above electronic device and other devices.

[0122] The memory may include a Random Access Memory (RAM), may include a Non-Volatile Memory (NVM), for example, at least one magnetic disk memory. Preferably, the memory may further be a storage device separated from at least one of the aforementioned processors.

[0123] The above-mentioned processor may be a general-purpose processor including a Central Processing Unit (CPU), a Network Processor (NP), etc., or may be a general-purpose processor including a Digital Signal Processing (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other rewritable logic devices, discrete gate or transistor logic devices, discrete hardware assemblies.

[0124] In a further embodiment according to the present application, when executed by a processor, there is further provided a computer-readable storage medium storing a computer program for realizing the steps of the calibration method of the external parameters of any one of the above linearity shape measuring machines in the processor.

[0125] In a further embodiment according to the present application, there is further provided a computer program product including a command, which when executed by a computer, causes the computer to realize the calibration method of the external parameters of any one of the linearity shape measuring machines in the above embodiments.

[0126] In the above embodiments, all or part of them can be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of them can be implemented in the form of a computer program product. The computer program product includes one or more computer commands. When the computer loads and executes the computer program commands, all or part of them implement the processes or functions described in the embodiments of the present application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer commands are stored in a computer-readable storage medium or can be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer commands can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium readable and writable by a computer, or a data storage device including a server, a data center, etc. in which one or more available media are integrated. The available medium may be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a solid state disk (SSD), etc.

[0127] In addition, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not claim or imply that there is any actual relationship or order between these entities or operations. Also, the terms "comprising", "including" or any other variation thereof mean non-exclusive "including". Accordingly, a process, method, product or device comprising a series of elements does not include only those elements, but also includes other elements not expressly listed, or also includes elements specific to such a process, method, product or device. In the absence of further limitations, an element limited by the description "comprising one..." does not exclude the presence of other identical elements in a process, method, product, or device comprising the described element.

[0128] Each embodiment in this specification has been described as being related, but for parts that are the same or similar between each embodiment, cross-reference may be made to each other. What has been described in detail in each embodiment is the part that is different from other embodiments. In particular, for embodiments of devices, apparatuses, and systems, since they are basically similar to embodiments of methods, they have been briefly described. For related parts, reference may be made to the description of the embodiments of the method.

Claims

1. A method for calibrating external parameters of a linearity shape measuring machine, comprising: obtaining a plurality of frames of contour point clouds collected by the linearity shape measuring machine for the calibration block during the movement of the calibration block by the measuring table; performing point cloud segmentation on each contour point cloud to obtain surface point clouds belonging to different measurement surfaces; constructing a constraint relationship of each surface point cloud based on each feature point in the surface point cloud and the surface feature of the measurement surface to which the surface point cloud belongs, wherein the surface feature of each measurement surface is a feature indicating the geometric attribute of the measurement surface; constructing an objective optimization function for the estimated transformation relationship based on the estimated transformation relationship between the linearity shape measuring machine coordinate system and the measuring table coordinate system and the constraint relationship of each surface point cloud; optimizing the objective optimization function, and using the transformation parameters between the optimized linearity shape measuring machine coordinate system and the measuring table coordinate system as the external parameters between the linearity shape measuring machine and the measuring table. A method for calibrating external parameters of a linearity shape measuring machine, characterized by the above.

2. Performing point cloud segmentation on each of the contour point clouds to obtain surface point clouds belonging to different measurement surfaces includes: performing point cloud segmentation on each contour point cloud based on a segmentation policy corresponding to the type of the calibration block to obtain surface point clouds belonging to different measurement surfaces, characterized by the above. The method for calibrating external parameters of a linearity shape measuring machine according to Claim 1.

3. Performing point cloud segmentation on each of the contour point clouds based on a segmentation policy corresponding to the type of the calibration block to obtain surface point clouds belonging to different measurement surfaces includes: when the calibration block is a cone, performing curve fitting and straight line fitting on each feature point in the contour point cloud, determining the feature points corresponding to the curves obtained by fitting as the conical surface point cloud, and the feature points corresponding to the straight lines obtained by fitting as the bottom surface point cloud. When the calibration block is a sphere, for each feature point in the contour point group, perform circle fitting and straight line fitting. Take each feature point corresponding to the curve obtained by fitting as a spherical point group, and take each feature point corresponding to the straight line obtained by fitting as a bottom surface point group. When the calibration block is a truncated cone, perform straight line fitting for each feature point in the contour point group to obtain a plurality of fitting straight lines. Divide the plurality of fitting straight lines into an end straight line including endpoints and a middle straight line not including endpoints. Take each feature point corresponding to the middle straight line as an upper surface point group, and take each feature point corresponding to the end straight line as a bottom surface point group. The method for calibrating the external parameters of the linearity shape measuring machine according to claim 2.

4. Before constructing the target optimization function for the estimated transformation relationship based on the estimated transformation relationship between the linearity shape measuring machine coordinate system and the measurement table coordinate system and the constraint relationship of each surface point group, the method for calibrating the external parameters of the linearity shape measuring machine is as follows: Further including determining the coordinate transformation relationship between the linearity shape measuring machine coordinate system and the measurement table coordinate system as the estimated transformation relationship based on the obtained plurality of frames of contour point groups. The method for calibrating the external parameters of the linearity shape measuring machine according to any one of claims 1 to 3.

5. Determining the coordinate transformation relationship between the linearity shape measuring machine coordinate system and the measurement table coordinate system as the estimated transformation relationship based on the obtained plurality of frames of contour point groups is as follows: Based on the contour point groups of the plurality of frames, determining the initial transformation parameters between the linearity shape measuring machine coordinate system and the measurement table coordinate system. Including obtaining the estimated transformation relationship by using the initial transformation parameters as the estimated values of the parameters in the transformation formula between the linearity shape measuring machine coordinate system and the measurement table coordinate system. The method for calibrating the external parameters of the linearity shape measuring machine according to claim 4.

6. The initial transformation parameters include initial rotation parameters. Based on the contour point groups of the plurality of frames, determining the initial transformation parameters between the linearity shape measuring machine coordinate system and the measurement table coordinate system is as follows: Determine the slope of the fitting line corresponding to each bottom surface feature point in the contour point group of the plurality of frames, and determine the first rotation angle of the linear shape measuring machine coordinate system with respect to the measurement table coordinate system based on the slope, wherein the bottom surface feature point is a feature point belonging to the measurement table, Determine the feature point with the maximum height in the contour point group of the plurality of frames as the maximum feature point, calculate the ratio of the height of the maximum feature point to the actual height of the calibration block, and determine the second rotation angle of the linear shape measuring machine coordinate system with respect to the measurement table coordinate system based on the ratio, Determining the initial rotation parameter based on the first rotation angle and the second rotation angle, A method for calibrating the external parameters of the linear shape measuring machine according to claim 5.

7. The initial transformation parameter includes an initial translation parameter, Based on the contour point group of the plurality of frames, determining the initial transformation parameter between the linear shape measuring machine coordinate system and the measurement table coordinate system is For the contour point groups of any two adjacent frames, determining the first height difference of the feature point with the maximum height in the contour point groups of the two frames; determining the curved surface arc length corresponding to the first height difference and the horizontal displacement within each sampling interval of the calibration block based on the surface features of the calibration block; determining the first displacement in the first direction of the linear shape measuring machine coordinate system with respect to the measurement table coordinate system based on the curved surface arc length and the horizontal displacement; determining the second displacement in the second direction of the linear shape measuring machine coordinate system with respect to the measurement table coordinate system based on the curved surface arc length and the first height difference, wherein the first direction and the second direction are orthogonal; and determining the initial translation parameter based on the first displacement and the second displacement, or Based on the contour point group of each frame, determining the abscissa of a specified point on the frustum as the third displacement, wherein the specified point is any point on the frustum; determining the height of the frustum as the fourth displacement based on the surface point group belonging to the top surface of the frustum and the bottom surface point group belonging to the measurement table; and determining the initial translation parameter based on the third displacement and the fourth displacement, Calibration method for external parameters of the linearity shape measuring machine according to claim 5.

8. Optimizing the target optimization function and obtaining the conversion parameters between the optimized linearity shape measuring machine coordinate system and the measurement table coordinate system as the external parameters between the linearity shape measuring machine and the measurement table, iteratively optimizing the conversion parameters in the target optimization function until the residual of the target optimization function becomes smaller than a preset threshold, including using the conversion parameters in the target optimization function when the residual of the target optimization function is smaller than the preset threshold as the external parameters between the linearity shape measuring machine and the measurement table, and is characterized in that Calibration method for external parameters of the linearity shape measuring machine according to any one of claims 1 to 7.

9. A point cloud acquisition module that obtains a plurality of frames of contour point clouds collected by the linearity shape measuring machine for the calibration block during the movement of the calibration block by the measurement table, A point cloud segmentation module that performs point cloud segmentation on each contour point cloud to obtain surface point clouds belonging to different measurement surfaces, A relationship construction module that constructs a constraint relationship of each surface point cloud based on each feature point in the surface point cloud and the surface feature of the measurement surface to which the surface point cloud belongs, A function construction module that constructs a target optimization function for the estimated conversion relationship based on the estimated conversion relationship between the linearity shape measuring machine coordinate system and the measurement table coordinate system and the constraint relationship of each surface point cloud, including a function optimization module that optimizes the target optimization function and uses the optimized conversion parameters between the linearity shape measuring machine coordinate system and the measurement table coordinate system as the external parameters between the linearity shape measuring machine and the measurement table, and is characterized in that Calibration device for external parameters of the linearity shape measuring machine.

10. The point cloud segmentation module is used to perform point cloud segmentation on each contour point cloud based on a segmentation policy corresponding to the type of the calibration block to obtain surface point clouds belonging to different measurement surfaces, and is characterized in that Calibration device for external parameters of the linearity shape measuring machine according to claim 9.

11. When the calibration block is a cone, the point cloud segmentation module performs curve fitting and linear fitting on each feature point in the contour point cloud, determines each feature point corresponding to the curve obtained by fitting as a conical surface point cloud, and determines the feature points corresponding to the straight line obtained by fitting as a bottom surface point cloud; when the calibration block is a sphere, the point cloud segmentation module performs circular fitting and linear fitting on each feature point in the contour point cloud, determines each feature point corresponding to the curve obtained by fitting as a spherical surface point cloud, and determines each feature point corresponding to the straight line obtained by fitting as a bottom surface point cloud; when the calibration block is a frustum of a cone, the point cloud segmentation module performs linear fitting on each feature point in the contour point cloud to obtain a plurality of fitting straight lines, divides the plurality of fitting straight lines into an end straight line including endpoints and a middle straight line not including endpoints, determines each feature point corresponding to the middle straight line as an upper surface point cloud, and determines each feature point corresponding to the end straight line as a bottom surface point cloud. The calibration device for external parameters of the linearity shape measuring machine according to claim 10.

12. Before executing the construction of the target optimization function for the estimated transformation relationship based on the estimated transformation relationship between the linearity shape measuring machine coordinate system and the measurement table coordinate system and the constraint relationship of each surface point cloud by the function construction module, based on the obtained contour point clouds of multiple frames, a relationship estimation module is further included to determine the coordinate transformation relationship between the linearity shape measuring machine coordinate system and the measurement table coordinate system as the estimated transformation relationship. The calibration device for external parameters of the linearity shape measuring machine according to any one of claims 9 to 11.

13. The relationship estimation module is used to determine the initial transformation parameters between the linearity shape measuring machine coordinate system and the measurement table coordinate system based on the contour point clouds of the multiple frames, and use the initial transformation parameters as the estimated values of the parameters in the transformation formula between the linearity shape measuring machine coordinate system and the measurement table coordinate system to obtain the estimated transformation relationship. The calibration device for external parameters of the linearity shape measuring machine according to claim 12.

14. An electronic device including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus. The memory is used for storing a computer program. When the processor executes the program stored in the memory, it is used to implement the steps of the method for calibrating the external parameters of the linearity shape measuring instrument according to any one of claims 1 to 8. Electronic device.

15. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for calibrating the external parameters of the linearity shape measuring instrument according to any one of claims 1 to 8. Computer-readable storage medium.

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