Hole group axis parallelism error detection method and device and storage medium

Abstract

The invention discloses a hole group axis parallelism error detection method and device and a storage medium, and relates to the field of structured light vision measurement. The method comprises the following steps: acquiring first point cloud data of a reference hole for positioning and acquiring second point cloud data of a detected hole to be detected by adopting a line laser and camera equipment; on the basis of the first point cloud data and the second point cloud data, ellipse centers corresponding to intersection lines of the reference hole and the measured hole are obtained through a fitting algorithm; calculating a reference axis according to the ellipse center of each intersection line of the reference hole; establishing an evaluation plane perpendicular to the reference axis according to the reference axis and the ellipse center of the measured hole; projecting the center of the ellipse of each intersection line of the measured hole to an evaluation plane to obtain a minimum containing circle containing all projection points; and measuring the diameter of the minimum containing circle, comparing the diameter with a preset parallelism tolerance, and judging whether the parallelism of the measured hole is qualified or not according to a comparison result. According to the invention, the efficiency of measuring the parallelism of the part is improved.

Description

Technical Field

[0001] The present invention relates to the field of machine vision, and in particular to a method, a device and a storage medium for detecting parallelism errors of hole group axes. Background Art

[0002] The hole parallelism of parts plays a vital role in the process of mechanical manufacturing and assembly, especially in the field of precision instruments and high-end equipment manufacturing, where error control directly affects the quality of the final product. Traditional hole parallelism measurement methods usually use contact measuring tools such as micrometers and gauge blocks for measurement. Although these methods can provide a certain degree of measurement accuracy, they are all contact measurement methods, and the operation process is cumbersome, time-consuming, and dependent on the operator's experience. It is difficult to adapt to the needs of modern industry for efficient, accurate, and batch measurement.

[0003] With the rapid development of optical technology and computer vision technology, non-contact visual measurement methods have gradually become an important means of industrial measurement. They can not only significantly improve the speed and efficiency of measurement, but also realize comprehensive detection of all product dimensions during the production process through online detection. Among them, line structured light visual measurement technology, as an active measurement method, is an efficient three-dimensional data acquisition method. It projects a laser beam onto the surface of an object and uses a camera to capture the distortion information of the line structured light, thereby achieving high-precision three-dimensional contour measurement. In the prior art, there are few studies on the automated measurement of hole parallelism errors, and most methods are limited to two-dimensional measurement and cannot accurately obtain the spatial information of the hole positions. In addition, traditional measurement methods are difficult to ensure the consistency and reliability of measurement results when facing parts with complex shapes. Therefore, there is an urgent need for a high-precision measurement method that can overcome the above problems to meet the needs of modern industry for part hole parallelism error control. Summary of the invention

[0004] In order to solve the technical problems of complex operation, low efficiency and low precision brought about by the above-mentioned traditional measurement methods, improve the automated measurement capability of hole positions, and ensure high-precision control of hole position parallelism during the production process, the present invention provides a method, device and storage medium for detecting parallelism errors of hole group axes.

[0005] According to one aspect of the present invention, there is provided a method for detecting parallelism errors of hole group axes, comprising:

[0006] A line laser and a camera are used to obtain first point cloud data of a reference hole for positioning, and second point cloud data of a hole to be tested, wherein the reference hole is used to evaluate the parallelism of the hole to be tested relative to the reference hole, the line laser is used to emit line structured light, and by moving the line laser multiple times, intersection lines formed by the light plane of the line structured light and the reference hole and the hole to be tested are obtained, and the camera is used to capture the intersection line and convert it into point cloud data;

[0007] Based on the first point cloud data and the second point cloud data, the ellipse centers corresponding to the intersection lines of the reference hole and the measured hole are respectively obtained by an ellipse fitting algorithm;

[0008] According to the ellipse center of each intersection line of the reference hole, the reference axis is calculated by using a nonlinear optimization algorithm;

[0009] According to the reference axis and the centers of the ellipses of the measured holes, an evaluation plane perpendicular to the reference axis is established;

[0010] Projecting the ellipse centers of the intersection lines of the measured holes onto the evaluation plane, and obtaining the minimum enclosing circle that encloses all the projection points on the projection plane;

[0011] The diameter of the minimum containing circle is measured and compared with a preset parallelism tolerance, and whether the parallelism of the measured hole is qualified is determined based on the comparison result.

[0012] Optionally, based on the first point cloud data and the second point cloud data, obtaining the ellipse centers corresponding to the intersection lines of the reference hole and the measured hole respectively by an ellipse fitting algorithm includes:

[0013] For each intersection line in the first point cloud data and the second point cloud data, the point cloud data of the intersection line is converted into two-dimensional coordinates in the local coordinate system through coordinate transformation. The coordinate transformation formula is as follows:

[0014]

[0015] Among them, R1 is the rotation transformation matrix, T is the translation matrix, (X A , Y A , Z A ) is the coordinate value of each point cloud data before coordinate transformation, (X F , Y F , Z F ) is the coordinate value of each point cloud data in the local coordinate system after coordinate transformation;

[0016] According to the point cloud coordinates of each intersection line after coordinate transformation, the least squares method is used to fit the ellipse equation:

[0017]

[0018] Among them, A, B, C, D, and E are the coefficients of the ellipse equation;

[0019] The coordinates of the center point of the ellipse are obtained according to the ellipse equation obtained by fitting

[0020]

[0021] The coordinates of the ellipse center point are converted from the local coordinate system back to the camera coordinate system through inverse transformation. The transformation formula is as follows:

[0022]

[0023] in, is the inverse matrix of the rotation matrix R1, T -1 is the inverse matrix of the translation matrix T, are the local coordinates of the ellipse center, The camera coordinates of the center point of the ellipse.

[0024] Optionally, according to the ellipse center of each intersection line of the reference hole, the reference axis is calculated using a nonlinear optimization algorithm, including:

[0025] Set a cylindrical model, where the center point of the cylindrical model is X0, and the coordinates of X0 in the camera coordinate system are (x C0 ,y C0 , z C0 ), the direction vector of the central axis of the cylinder is (m,n,p), the radius of the cylinder is r, and the equation of the cylinder model is expressed as:

[0026] (X C -x C0 ) 2 +(Y C -y C0 ) 2 +(Z C -z C0 ) 2 -[m(X C -x C0 )+n(Y C -y C0 )+p(Z C -z C0 )] 2 =r 2 ;

[0027] In the formula, (X C ,Y C ,Z C ) represents the coordinate value of any point on the cylindrical surface;

[0028] For the i-th point (XCi ,Y Ci ,Z Ci ), whose distance to the central axis of the cylinder is r i for:

[0029]

[0030] According to the distance from the point to the central axis of the cylinder and the radius of the cylinder, the objective function of nonlinear optimization is established:

[0031]

[0032] Wherein, N is the number of center points of the fitted reference hole ellipse, and the objective function aims to minimize the sum of the squares of the differences between the cylinder radius and the distances from all fitting points to the center axis of the cylinder;

[0033] The objective function is processed by the Levenberg–Marquardt algorithm to obtain the center axis direction, center point coordinates and radius of the optimized cylinder;

[0034] The optimized center axis of the cylinder is the reference axis, the direction vector of the center axis of the cylinder is the direction vector of the reference axis, and the coordinates of the center point of the cylinder are the coordinates of the reference point of the reference axis.

[0035] Optionally, setting a cylindrical model includes:

[0036] Assume the coordinates of the reference axis in the camera coordinates are:

[0037]

[0038] In the formula, (X D ,Y D ,Z D ) represents the coordinate value of any point on the reference axis;

[0039] Change the above formula and use Z D X D , Y D Parameterized representation:

[0040]

[0041] in,

[0042] Parameterized representation of X D ,Y D The equation can be regarded as the equation of two space planes, which intersect at the reference axis. The equations of the two planes are obtained by the least squares method, and the overdetermined equation system is obtained:

[0043]

[0044] Combine the above overdetermined equations to solve the parameters of the plane equation:

[0045]

[0046] Among them, (x Ci ,y Ci ,z Ci ) is the camera coordinate of the center point of the ellipse of the intersection line corresponding to each scan of the reference hole;

[0047] By formula Determine the z-axis coordinate of a center point on the reference axis C0 ;

[0048] According to the obtained parameters k1, b1, k2, b2 and z C0 , calculate the x-axis coordinate x of the center point on the reference axis C0 and the y-axis coordinate y C0 ;

[0049] Assign a value to the p component in the direction vector of the reference axis, and use k1 and k2 to solve the m and n components on the direction vector;

[0050] The obtained ((x C0 ,y C0 , z C0 )) is used as the center point coordinates of the cylindrical model, (m, n, p) is used as the direction vector of the central axis of the cylinder, and half of the short axis length of each intersection line of the reference hole obtained by elliptical fitting is used as the initial value of the cylinder radius r to construct an initial cylindrical model, which is used for subsequent nonlinear optimization.

[0051] Optionally, establishing an evaluation plane perpendicular to the reference axis according to the reference axis and the ellipse centers of the measured holes comprises:

[0052] According to the coordinate values ​​of the ellipse center points of each intersection line of the measured hole, the numerical mean values ​​of the coordinates of all the ellipse center points of the measured hole in the three dimensions of X, Y, and Z are calculated to obtain the mean point;

[0053] According to the coordinates of the mean point and the direction vector of the reference axis, an evaluation plane is constructed, and the equation of the evaluation plane is:

[0054] m(X E -x C )+n(Y E -y C )+p(Z E -z C )=0;

[0055] Among them, (m,n,p) is the direction vector of the reference axis, (x C ,y C , z C ) is the coordinate of the mean point, (X E , Y E , Z E ) are the coordinates of any point on the evaluation plane.

[0056] Optionally, obtaining a minimum enclosing circle enclosing all projection points on the projection plane includes:

[0057] The projection points are sorted according to the order of line laser scanning to form a sequence;

[0058] Take the first two projection points d1 and d2 of the sequence, and use the distance between d1 and d2 as the diameter to construct the initial minimum enclosing circle C2;

[0059] Traverse all the projection points in the sequence in the order of the sequence, add the projection points to the current minimum enclosing circle one by one, and determine whether the added projection points are within the current minimum enclosing circle;

[0060] For the i-th projection point d i , if d i If the circle is within the current minimum enclosing circle, there is no need to update the enclosing circle;

[0061] If d i If it is not within the current minimum enclosing circle, the projection points d1 and d i The distance is the diameter to construct the middle circle C i ;

[0062] Check whether the 2nd to i-1th projection points in the sequence are contained in the middle circle C i If the 2nd to i-1th projection points are contained in the middle circle C i Inside, the middle circle C i Become the new smallest containing circle;

[0063] If there is a projection point d among the 2nd to i-1th projection points j (2≤j≤i-1) is not in the middle circle C i If d1 and d j The distance is the diameter to construct the middle circle C j ;

[0064] Then check whether the 2nd to j-1th projection points in the sequence are included in the middle circle C j If the 2nd to j-1th projection points are contained in the middle circle C j Inside, the middle circle C j Become the new smallest containing circle;

[0065] If there is a projection point d among the 2nd to j-1th projection points k (2≤k≤j-1) is not in the middle circle C j If the projection point d i d j d k Three points form a new circle C i ', construct the new circle C i ' is the smallest enclosing circle that contains the first i points of the sequence.

[0066] Optionally, measuring the diameter of the minimum enclosing circle and comparing it with a preset parallelism tolerance, and judging whether the parallelism of the measured hole is qualified according to the comparison result includes:

[0067] If the diameter of the minimum enclosing circle is greater than the preset parallelism tolerance, it is determined that the measured hole exceeds the prescribed parallelism error range and does not meet the specification requirements;

[0068] If the diameter of the minimum containing circle is less than or equal to the preset parallelism tolerance, it is determined that the parallelism error of the measured hole is within an acceptable range and meets the design requirements.

[0069] Optionally, before respectively obtaining the ellipse centers corresponding to the intersection lines of the reference hole and the measured hole by an ellipse fitting algorithm based on the first point cloud data and the second point cloud data, the method further includes:

[0070] The first point cloud data and the second point cloud data are subjected to noise filtering and error point elimination processing to allow point cloud data within a specific dimensional range to pass through and eliminate point cloud data beyond a preset range.

[0071] According to another aspect of the present invention, there is provided a device for detecting parallelism errors of hole group axes, the device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the steps of the method described above are implemented when the computer program is executed by the processor.

[0072] According to another aspect of the present invention, a storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0073] The present invention collects high-precision point cloud data of the hole to be tested by establishing an integrated system of a line laser and a camera device. During the measurement process, the relative positions of the projection position of the line laser and the hole to be tested and the reference hole are first determined. By adjusting the position of the line laser multiple times, the reference hole and the hole to be tested are scanned respectively. The line structured light technology generates the intersection line between the light plane and the inner surface of the hole position, and the point cloud data of different hole positions are obtained. The intersection line of the light plane and the hole position surface is extracted for these data, and the spatial coordinates of the ellipse center point corresponding to the intersection line are obtained by the ellipse fitting method. After coordinate transformation, these center points are converted to the same coordinate system for processing. During the data processing process, the present invention adopts a nonlinear optimization algorithm to fit the reference axis, thereby ensuring high precision and noise resistance during the measurement process. An evaluation plane is established according to the reference axis, and the center points of each ellipse of the hole to be tested are projected onto the evaluation plane. The diameter of the minimum enclosing circle is solved by the incremental point algorithm, thereby evaluating the parallelism error of the part hole position and completing the parallelism error detection of the part hole group axis.

[0074] The present invention adopts a line laser and a camera device to obtain three-dimensional point cloud data of the reference hole and the measured hole, thereby avoiding deformation or damage of parts that may be caused by contact measurement, and realizes efficient and automatic parallelism detection by processing and analyzing the point cloud data. The present invention proposes a non-contact detection method for hole position parallelism, which solves the problems of measurement errors caused by human influence and inability to detect in real time in contact measurement, and improves the efficiency of part parallelism measurement.

[0075] The present invention adopts a nonlinear optimization algorithm to fit the reference axis, which effectively improves the robustness of the measurement, reduces the influence of external interference on the measurement result, and improves the reliability of parallelism error measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0077] Figure 1 is a flow chart of a method for detecting parallelism errors of hole groups axes in an embodiment of the present invention;

[0078] Figure 2 A schematic diagram of scanning a measured part using a line laser in an embodiment of the present invention;

[0079] Figure 3 Schematic diagram of the spatial position relationship between the local coordinate system and the camera coordinate system in an embodiment of the present invention;

[0080] Figure 4 It is a schematic diagram of the axis of the measured hole;

[0081] Figure 5 It is a schematic diagram of the projection of the center of the intersection line ellipse of the measured hole onto the evaluation plane. DETAILED DESCRIPTION

[0082] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a part of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0083] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0084] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0085] Example 1

[0086] In this embodiment, a method for detecting parallelism errors of hole group axes is provided. The method embodiment provided in this embodiment can be executed in a server, a computer, a mobile phone, or a similar computing device.

[0087] Reference Figure 1 , Figure 1 is a flow chart of a method for detecting parallelism errors of hole groups in an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0088] S1, using a line laser and a camera device to obtain first point cloud data of a reference hole for positioning, and obtaining second point cloud data of a hole to be detected;

[0089] The reference hole is the standard for evaluating the parallelism of the measured hole, and is used to evaluate the parallelism of the measured hole relative to the reference hole. The reference hole and the measured hole can be a hole group on the same part, or they can belong to different parts.

[0090] Build a line laser and camera equipment, use high-resolution camera equipment to capture the images of the reference hole and the hole to be measured on the part, determine the projection position of the line laser and the position information of the hole to be measured and the reference hole, use the line laser as the light source, and project evenly distributed line structured light. These line structured lights will form clear light strips when they intersect the surface of the illuminated object.

[0091] To ensure that the line laser accurately captures the surface features of the measured part, it needs to be precisely calibrated and calibrated to eliminate errors such as optical distortion. This includes synchronizing the line laser with the camera, adjusting the position and angle, and ensuring that the light strip is clearly and consistently imaged on the surface of the part. Calibrate the camera to obtain an image that truly reflects the geometric features of the part. After setting the appropriate exposure time and scanning parameters, the line laser projects the light strip, and the camera captures the image and converts it into point cloud data to complete high-precision measurement.

[0092] Reference Figure 2 , Figure 2 FIG. 1 is a schematic diagram of scanning a measured part using a line laser in an embodiment of the present invention, which describes the position of the line laser and the intersection line formed by the reference hole, the measured hole and the light plane. Figure 2 As shown, Figure 2 The reference hole and the measured hole in the figure are the hole group located in the same part. By moving the line laser multiple times at different positions of the preset path (from position 1 to position i), each movement generates a new light plane. In the figure, τ1 is the light plane of the measured hole illuminated by the line structured light at position 1, π1 is the light plane of the reference hole illuminated by the line structured light at position 1, and τ i is the light plane of the line structured light illuminating the measured hole at position i, π i The line structured light planes emitted by these line lasers intersect with the inner surfaces of the reference hole and the measured hole at different positions, forming intersection lines N1 and N2 after the intersection. i , M1, M i, due to the viewing angle and the shape of the hole, these intersection lines usually appear as ellipses. The data of these intersection lines are then captured and recorded by the camera device, forming part of the point cloud data. As the position of the line laser changes, the accumulated scanning data will reflect the three-dimensional morphology of the hole surface. In this process, the point cloud data is filtered, noise filtering and error point removal are performed, which can be achieved by setting a reasonable threshold. After filtering, point cloud data within a specific dimensional range is allowed to pass, while data that exceeds the preset interval is removed, and erroneous points or abnormal data caused by equipment movement or irregularities on the hole surface are removed, thereby effectively removing background noise and error points to reduce the amount of data and perform preliminary screening. This step provides accurate and purified point cloud data for parallelism error detection based on line structured light vision.

[0093] S2, based on the first point cloud data and the second point cloud data, respectively obtaining the ellipse centers corresponding to the intersection lines of the reference hole and the measured hole by an ellipse fitting algorithm;

[0094] Reference Figure 2 , Figure 2 O1, O i are the reference hole intersection lines M1 and M i The corresponding ellipse centers, o1, o i N1 and N2 are the intersection lines of the measured hole. i The corresponding ellipse center. The ellipse fitting algorithm is a mathematical method used to fit an ellipse shape that best fits a set of discrete points (in this case, the point cloud data of the intersection line). The optimal ellipse parameters, such as the center position, the length of the major and minor axes, the rotation angle, etc., are found by minimizing the sum of the squares of the distances from the points to the fitted ellipse.

[0095] Furthermore, step S2 in this embodiment includes:

[0096] S21, for each intersection line in the first point cloud data and the second point cloud data, convert the point cloud data of the intersection line into two-dimensional coordinates in the local coordinate system through coordinate transformation, and the coordinate transformation formula is as follows:

[0097]

[0098] Among them, R1 is the rotation transformation matrix, which is used to rotate the point from the original coordinate system to the local coordinate system. It is usually determined by three rotation angles (rotation angles around the X axis, Y axis, and Z axis). T is the translation matrix, (X A , Y A , Z A ) is the coordinate value of each point cloud data before coordinate transformation, (X F , Y F , Z F) is the coordinate value of each point cloud data in the local coordinate system after coordinate transformation;

[0099] The intersection line is composed of three-dimensional coordinate data points in space. The process of fitting an ellipse using spatial coordinates is relatively complicated. Therefore, in this embodiment, the coordinates of one dimension of the points on the intersection line are unified through coordinate transformation, and the three-dimensional spatial coordinates are converted into two-dimensional coordinates in the local coordinate system. This transformation usually involves rotation and translation operations to ensure the correct alignment between the transformed local coordinate system and the camera coordinate system. Figure 3 , Figure 3 Schematic diagram of the spatial position relationship between the local coordinate system and the camera coordinate system in an embodiment of the present invention. Figure 3 As shown, in this example, the local coordinate system ( F -X F Y F Z F ) and the origin of the camera coordinate system (O C -X C Y C Z C ) coincide with the origin of the coordinates of the local coordinate system (O ), but the directions are different. Therefore, in this example, the translation matrix T is a zero matrix, and only the rotation matrix R1 needs to be considered. R1 is used to align the directions of the two coordinate systems. F -X F Y F Z F ) and the camera coordinate system (O C -X C Y C Z C ), calculate the normal vector of the light plane and the rotation angle of the axis to be aligned, construct the corresponding rotation matrix R1, use the rotation matrix R1 to rotate the original point cloud data, and the coordinate plane X of the local coordinate system after the conversion F O F Y F Parallel to the light plane π, coordinate axis O F Z F The direction vector is the normal vector of the light plane π After the coordinate transformation, the X F O F Y F The plane is parallel to the light plane, and all light strip points on the intersection line are in the Z coordinate system of the local coordinate system. F are the same, and the ellipse fitting process only involves two-dimensional coordinates, so Z can be ignored F Coordinates, only keep X F and Y F Coordinates, the remaining two coordinate values ​​of the light bar point (X F , Y F) is substituted into the general formula of ellipse to perform ellipse fitting.

[0100] S22, according to the point cloud coordinates of each intersection line after coordinate transformation, the ellipse equation is fitted using the least square method:

[0101]

[0102] Among them, A, B, C, D, and E are the coefficients of the ellipse equation;

[0103] A set of coefficients A, B, C, D, and E are found by least squares fitting, so that the error between the ellipse equation defined by these coefficients and the given point cloud data is minimized;

[0104] S23, obtain the coordinates of the center point of the ellipse according to the ellipse equation obtained by fitting

[0105]

[0106] In order to fit the ellipse in the two-dimensional coordinate system and find the center point of the ellipse, step S21 converts the point cloud data from the camera coordinates to the local coordinates. Therefore, the center point coordinates of the ellipse obtained in step S23 are

[0107] is the coordinate value in the local coordinate system (where The z value of the intersection line is converted to the local coordinate system), and the subsequent nonlinear optimization, the fitting of the reference axis and the construction of the cylindrical model are based on the camera coordinate system. Therefore, it is necessary to convert the obtained ellipse center point coordinates from the local coordinate system back to the camera coordinate system;

[0108] S24, convert the coordinates of the ellipse center point from the local coordinate system back to the camera coordinate system through inverse transformation, and the transformation formula is as follows:

[0109]

[0110] in, is the inverse matrix of the rotation matrix R1, T -1 is the inverse matrix of the translation matrix T, are the local coordinates of the ellipse center, The camera coordinates of the center point of the ellipse.

[0111] Z can be ignored when fitting the parameters of an ellipse using the ellipse fitting algorithm. F coordinates, this is because the light bar point on the intersection line after coordinate transformation is in the Z F are all the same, that is, on a specific Z plane, so we can only focus on the points on the XY plane and use X F and Y Fcoordinates to fit the ellipse, but when representing the three-dimensional spatial position of the center point of the ellipse, Z F The coordinate cannot be ignored. It is related to X F , Y F Together they represent the position of the ellipse center on different cross sections.

[0112] S3, according to the ellipse center of each intersection line of the reference hole, the reference axis is calculated by using a nonlinear optimization algorithm;

[0113] The reference axis represents the ideal center line of the reference hole and is a reference for evaluating the parallelism between the measured hole and the reference hole. In some embodiments, the least square method can be used, such as using the points on each intersection line to obtain a center point corresponding to the position of the light plane by ellipse fitting. In theory, the center point is a point on the axis of the reference hole. The optimal straight line is determined by minimizing the sum of the squares of the errors between these center points and the fitting straight line. This straight line is the reference axis of the reference hole.

[0114] Due to possible measurement errors, data noise and uncertainties in ellipse fitting, a nonlinear optimization algorithm (such as the Levenberg-Marquardt algorithm, a genetic algorithm or a particle swarm optimization algorithm, etc.) is used in this embodiment to fit the reference axis. The algorithm will iteratively adjust the parameters of the straight line to minimize the objective function, thereby finding the straight line parameters with the smallest sum of the squares of the distances from each point to the fitting straight line.

[0115] Furthermore, in this embodiment, step S3 includes:

[0116] S31, setting a cylindrical model, wherein the center point of the cylindrical model is X0, and the coordinates of X0 in the camera coordinate system are (x C0 ,y C0 , z C0 ), the direction vector of the central axis of the cylinder is (m,n,p), the radius of the cylinder is r, and the equation of the cylinder model is expressed as:

[0117] (X C -x C0 ) 2 +(Y C -y C0 ) 2 +(Z C -z C0 ) 2 -[m(X C -x C0 )+n(Y C -y C0 )+p(Z C -z C0 )] 2 =r 2 (1)

[0118] In the formula, (X C ,Y C ,Z C ) represents the coordinate value of any point on the cylindrical surface;

[0119] S32, for the i-th point (X Ci ,Y Ci ,Z Ci ), whose distance to the central axis of the cylinder is r i for:

[0120]

[0121] S33, based on the distance from the point to the central axis of the cylinder and the radius of the cylinder, establish the objective function of nonlinear optimization:

[0122]

[0123] Where N is the number of center points of the fitted reference hole ellipse, and the objective function aims to minimize the radius r of the cylinder and the distance r from all fitting points to the center axis of the cylinder: i The sum of the squares of the differences;

[0124] S34, processing the objective function by using the Levenberg–Marquardt algorithm to obtain the center axis direction, center point coordinates and radius of the optimized cylinder;

[0125] The Levenberg–Marquardt algorithm is an optimization algorithm for nonlinear least squares problems. It uses the Levenberg–Marquardt algorithm to process the objective function and minimizes the objective function by iteratively adjusting the cylinder parameters. The output of the algorithm is the optimized cylinder parameters, including the center axis direction, center point coordinates, and radius.

[0126] S35, the optimized central axis of the cylinder is the reference axis, the direction vector of the central axis of the cylinder is the direction vector of the reference axis, and the coordinates of the center point of the cylinder are the coordinates of the reference point of the reference axis.

[0127] The basic data for fitting the cylinder are the ellipse center points of each intersection line of the reference hole, because these ellipse center points reflect the position of the intersection line and are the ideal point set of the reference hole axis. Fitting the cylinder with the ellipse center points of the reference hole can reduce the error impact of the point cloud data. In theory, the ellipse center connecting all the intersection lines of the reference hole should be a straight line, but due to the existence of errors, some points will deviate. The above steps S31 to S35 can be understood as fitting the cylinder with the coordinates of the ellipse center points of the reference hole to find a minimum enclosing cylinder that encloses the ellipse centers of all the reference holes. The central axis of the cylinder obtained by this fitting is the reference axis.

[0128] The present invention uses the Levenberg-Marquardt algorithm to process the objective function to obtain the optimized axis direction, center point coordinates and radius of the cylinder. However, since the nonlinear optimization algorithm has high requirements on the initialization, if the gap between the initial value and the optimal solution is large, the solution process is prone to fall into the local optimal solution. The scanning method of the present invention for the measured hole can obtain a more accurate nonlinear optimization initialization. The solution algorithm of the method for optimizing the initial value is described below to determine the initial parameters of the reference axis (that is, the initial parameters of the cylindrical model constructed in S31 above), then S31, set a cylindrical model, including:

[0129] S311, assume that the coordinates of the reference axis in the camera coordinates are:

[0130]

[0131] In the formula, (X D ,Y D ,Z D ) represents the coordinate value of any point on the reference axis;

[0132] S312, change the above formula (4) and use Z D X D , Y D Parameterized representation:

[0133]

[0134] in,

[0135] S313, the reference axis can be expressed by the point method or by the intersection of two planes. The parameterized X in equation (5) is D ,Y D The equation can be regarded as the equation of two space planes, k1 and k2 are the slopes of the planes, b1 and b2 are the intercepts of the planes, and the two planes intersect at the reference axis. In other words, the center point of the ellipse of each intersection line of the reference hole (X Ci ,Y Ci,Z Ci ) should be located on the reference axis in theory, so substituting the two plane equations into x Ci =k1z Ci +b1,y Ci =k2z Ci +b2;

[0136] For each point, calculate the residual: x,i =x Ci -(k1z Ci +b1), ε y,i =y Ci -(k2z Ci +b2);

[0137] In order to find the best k1, b1, k2, b2, so that all points satisfy these two equations as much as possible, minimize the residual sum of squares ∑(ε x,i 2 +ε y,i 2 ), for each data point, expand the residual sum of squares, find partial derivatives of k1, b1, k2, and b2 respectively, and get the following overdetermined equations:

[0138]

[0139] S314, for N data points, each data point corresponds to a pair of equations:

[0140]

[0141] Written in matrix form: A·x=b, where

[0142]

[0143] Let the residual sum of squares of the parameters be minimized, that is, min||A·xb|| 2 ; The least squares solution is obtained through the normal equation x=(A T A) -1 A T b, solve the parameters of the plane equation:

[0144]

[0145] Among them, (x Ci ,y Ci ,z Ci ) is the camera coordinate of the center point of the ellipse of the intersection line corresponding to each scan of the reference hole, which can be obtained by fitting the ellipse of the scan points on the intersection line;

[0146] Substituting the coordinates of the center points of each ellipse into equations (7) to (10) can obtain the parameters k1, b1, k2, and b2. In order to obtain the point formula of the reference axis, it is necessary to first give the z value of a center point on the reference axis. C0 ;

[0147] S315, by formula Determine the z-axis coordinate of a center point on the reference axis C0 ;

[0148] S316, based on the obtained parameters k1, b1, k2, b2 and z C0 Substituting into formula (5) we can calculate the x-axis coordinate x of the center point on the reference axis. C0 and the y-axis coordinate y C0 ;

[0149] S317, assign a value to the p component in the direction vector of the reference axis, such as 1, and use k1 and k2 to solve the m and n components on the direction vector, that is, m = k1*p, n = k2p;

[0150] Through the above process, we can obtain the m, n, p, x of the reference axis using the nonlinear optimization method. C0 ,y C0 、z C0 ;

[0151] S318, the obtained (x C0 ,y C0 , z C0 ) as the center point coordinates of the cylindrical model, and (m,n,p) as the direction vector of the central axis of the cylinder;

[0152] S319, taking half of the minor axis length obtained by ellipse fitting of each intersection line of the reference hole as the initial value of the cylinder radius r, and constructing an initial cylinder model, wherein the initial cylinder model is used for subsequent nonlinear optimization.

[0153] S4, establishing an evaluation plane perpendicular to the reference axis according to the reference axis and the centers of the ellipses of the measured holes;

[0154] According to the spatial direction of the axis of the reference hole obtained in step S3 above, the numerical mean of the center points in the three dimensions of X, Y, and Z is calculated according to the coordinate values ​​of the center points of the ellipses of each intersection line of the measured hole, and a mean point is obtained, and its position in space reflects the overall center trend of the measured hole;

[0155] Using the direction vector of the reference hole axis and the coordinates of the mean point, an evaluation plane I perpendicular to the reference axis is constructed. The equation of the evaluation plane I is:

[0156] m(X E -xC )+n(Y E -y C )+p(Z E -z C )=0;

[0157] Among them, (m,n,p) is the direction vector of the reference axis, (x C ,y C , z C ) can be any point in space. In this embodiment, x C ,y C 、z C Select the average value of each scanning section center point in the three-dimensional direction of all the measured holes, (X E , Y E , Z E ) are the coordinates of any point on the evaluation plane.

[0158] S5, projecting the ellipse centers of the intersection lines of the measured holes onto the evaluation plane, and obtaining a minimum enclosing circle enclosing all the projection points on the projection plane;

[0159] According to the spatial equation of the evaluation plane, the ellipse center point of the measured hole in three-dimensional space is projected onto the evaluation plane. The projection operation follows the projection principle in geometry, ensuring that each center point has a corresponding two-dimensional position on the evaluation plane, and using these plane projection points to determine the minimum inclusion circle that includes all projection points on the evaluation plane.

[0160] Reference Figure 4 , Figure 4 It is a schematic diagram of the axis of the measured hole. After obtaining the ellipse centers corresponding to the intersection lines of the measured hole through the ellipse fitting algorithm, the ellipse centers of the measured holes are sorted in ascending order of z value; through mathematical methods or graphic algorithms, the ellipse centers of the measured holes are connected in sequence. Theoretically, this line should be on a straight line (i.e., the ideal axis in the figure) and parallel to the reference axis. However, due to the existence of errors, some points may deviate from the axis (i.e., the actual axis in the figure), so when these points are subsequently projected, a minimum enclosing circle will be formed, resulting in parallelism errors.

[0161] Reference Figure 5 , Figure 5 It is a schematic diagram of the projection of the center of the intersection line ellipse of the measured hole onto the evaluation plane, such as Figure 5 As shown, the projection plane is perpendicular to the reference axis, the reference axis direction vector is (m, n, p), o1 is the center of the ellipse of the intersection line corresponding to the light plane τ1, and the projection point d1 is the projection point of o1 onto the evaluation plane. i (i=1,2,…,6 in the figure) is the light plane τ i The center of the ellipse corresponding to the intersection line, the projection point di for o i Through all the projection points, we can form a minimum enclosing circle containing all the projections of the center points on the evaluation plane, and obtain the diameter of the minimum enclosing circle according to the incremental point method. Among them, the key to the incremental point method is to update the position and size of the circle by gradually adding points in the point set until a minimum enclosing circle that can contain all points is found. This method is relatively simple in calculation, but it may require multiple iterations to ensure that the global optimal solution is found.

[0162] Furthermore, in this example, obtaining the minimum enclosing circle that encloses all projection points on the projection plane includes:

[0163] S51, sorting the projection points according to the line laser scanning order to form a sequence;

[0164] S52, taking the first two projection points d1 and d2 of the sequence, and taking the distance between d1 and d2 as the diameter, constructing an initial minimum enclosing circle C2;

[0165] S53, traversing all the projection points in the sequence in the order of the sequence, adding the projection points to the current minimum enclosing circle in turn, and determining whether the added projection points are within the current minimum enclosing circle;

[0166] When judging whether a point is inside a circle, we can use the method of comparing the distance from the point to the center of the circle with the radius of the circle. If the distance from the point to the center of the circle is less than the radius of the circle, the point is inside the circle, otherwise it is not.

[0167] S54, for the i-th projection point d i , if d i If d is within the current minimum enclosing circle, there is no need to update the enclosing circle. i If it is not within the current minimum enclosing circle, the projection points d1 and d i The distance is the diameter to construct the middle circle C i ;

[0168] Middle circle C i There is no guarantee that the second to i-1 projection points are all within the circle. The algorithm needs to determine whether the second to i-1 projection points are within the circle. i relationship;

[0169] S55, check whether the 2nd to i-1th projection points in the sequence are included in the middle circle C i If the 2nd to i-1th projection points are contained in the middle circle C i Inside, the middle circle C i Become the new minimum enclosing circle; if there is a projection point d among the 2nd to i-1th projection points j (j<i, further 2≤j≤i-1) not in the middle circle C iIf d1 and d j The distance is the diameter to construct the middle circle C j ;

[0170] Same as above, middle circle C j There is no guarantee that the 2nd to j-1st projection points can be included. The algorithm needs to determine whether the 2nd to j-1st projection points are consistent with circle C. j relationship;

[0171] S56, then check whether the 2nd to j-1th projection points in the sequence are included in the middle circle C j If the 2nd to j-1th projection points are contained in the middle circle C j Inside, the middle circle C j Become the new smallest containing circle;

[0172] If there is a projection point d among the 2nd to j-1th projection points k (k<j<i, further 2≤k≤j-1) not in the middle circle C j If the projection point d i d j d k Three points form a new circle C i ', construct the new circle C i ' is the minimum enclosing circle that contains the first i points of the sequence. When constructing a circle, given three non-collinear points, a unique circle can be constructed through the three points. All the projection points in the sequence are traversed to determine the three projection points on the final minimum enclosing circle. The coordinates of these three points can be used to calculate the diameter of the minimum enclosing circle.

[0173] S6, measuring the diameter of the minimum containing circle and comparing it with a preset parallelism tolerance, and judging whether the parallelism of the measured hole is qualified according to the comparison result.

[0174] The diameter of the minimum containing circle obtained in step S5 is the hole position parallelism error value of the measured hole. Compare the diameter of the circle with the given preset parallelism tolerance to determine whether the measured hole is within the specified tolerance range. If the diameter of the minimum containing circle is less than or equal to the given parallelism tolerance value, then the parallelism error of the measured hole is acceptable. If the diameter is greater than the tolerance value, then the measured hole exceeds the specified parallelism error range, that is, there is an error. If this error value is within the allowable tolerance range, then the measured hole meets the design requirements; if it exceeds the tolerance, then the measured hole does not meet the specification requirements.

[0175] The hole position parallelism error measurement method based on line structured light vision of the present invention can efficiently and automatically measure the parallelism error of the hole position online, not only realizes the non-contact precise measurement of the hole position parallelism, but also performs automatic classification and evaluation through statistical analysis methods, fundamentally solving the shortcomings of traditional contact measurement in efficiency, online measurement capability and accuracy, significantly improving the detection efficiency of hole position parallelism in mass production, and reducing the occurrence of missed detection and wrong detection.

[0176] Example 2

[0177] In this embodiment, a hole group axis parallelism error detection device is also provided to implement the above-mentioned embodiments and preferred implementation modes, and those that have been explained will not be repeated here.

[0178] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0179] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0180] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0181] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0182] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0183] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for detecting parallelism error of a hole group axis, characterized in that: include: A line laser and a camera are used to obtain first point cloud data of a reference hole for positioning, and second point cloud data of a hole to be detected, wherein the reference hole is used to evaluate the parallelism of the hole to be detected relative to the reference hole, the line laser is used to emit line structured light, and by moving the line laser multiple times, intersection lines formed by the structured light plane with the reference hole and the hole to be detected are obtained, and the camera is used to capture the intersection line and convert it into point cloud data; Based on the first point cloud data and the second point cloud data, the ellipse centers corresponding to the intersection lines of the reference hole and the measured hole are respectively obtained by an ellipse fitting algorithm; According to the ellipse center of each intersection line of the reference hole, the reference axis is calculated by using a nonlinear optimization algorithm; According to the reference axis and the centers of the ellipses of the measured holes, an evaluation plane perpendicular to the reference axis is established; Projecting the ellipse centers of the intersection lines of the measured holes onto the evaluation plane, and obtaining the minimum enclosing circle that encloses all the projection points on the projection plane; The diameter of the minimum containing circle is measured and compared with a preset parallelism tolerance, and whether the parallelism of the measured hole is qualified is determined based on the comparison result.

2. The method for detecting parallelism error of hole group axes according to claim 1, characterized in that: Based on the first point cloud data and the second point cloud data, obtaining the ellipse centers corresponding to the intersection lines of the reference hole and the measured hole respectively through the ellipse fitting algorithm includes: For each intersection line in the first point cloud data and the second point cloud data, the point cloud data of the intersection line is converted into two-dimensional coordinates in the local coordinate system through coordinate transformation. The coordinate transformation formula is as follows: Among them, R1 is the rotation transformation matrix, T is the translation matrix, (X A , Y A , Z A ) is the coordinate value of each point cloud data before coordinate transformation, (X F , Y F , Z F ) is the coordinate value of each point cloud data in the local coordinate system after coordinate transformation; According to the point cloud coordinates of each intersection line after coordinate transformation, the least squares method is used to fit the ellipse equation: Among them, A, B, C, D, and E are the coefficients of the ellipse equation; The coordinates of the center point of the ellipse are obtained according to the ellipse equation obtained by fitting The coordinates of the ellipse center point are converted from the local coordinate system back to the camera coordinate system through inverse transformation. The transformation formula is as follows: in, is the inverse matrix of the rotation matrix R1, T -1 is the inverse matrix of the translation matrix T, are the local coordinates of the ellipse center, The camera coordinates of the center point of the ellipse.

3. The method for detecting parallelism error of hole group axes according to claim 1, characterized in that: According to the ellipse center of each intersection line of the reference hole, the reference axis is calculated using a nonlinear optimization algorithm, including: Set a cylindrical model, where the center point of the cylindrical model is X0, and the coordinates of X0 in the camera coordinate system are (x C0 ,y C0 , z C0 ), the direction vector of the central axis of the cylinder is (m, n, p), the radius of the cylinder is r, and the equation of the cylinder model is expressed as: (X c -x C0 ) 2 +(Y′ C -y C0 ) 2 +(Z c -z C0 ) 2 -[m(X c -x C0 )+n(Y C -y C0 )+p(Z C -z C0 )] 2 =r 2 ; In the formula, (X C , Y C , Z C ) represents the coordinate value of any point on the cylindrical surface; For the i-th point (X Ci , Y Ci , Z Ci ), whose distance to the central axis of the cylinder is r i for: According to the distance from the point to the central axis of the cylinder and the radius of the cylinder, the objective function of nonlinear optimization is established: Wherein, N is the number of center points of the fitted reference hole ellipse, and the objective function aims to minimize the sum of the squares of the differences between the cylinder radius and the distances from all fitting points to the center axis of the cylinder; The objective function is processed by the Levenberg-Marquardt algorithm to obtain the center axis direction, center point coordinates and radius of the optimized cylinder; The optimized center axis of the cylinder is the reference axis, the direction vector of the center axis of the cylinder is the direction vector of the reference axis, and the coordinates of the center point of the cylinder are the coordinates of the reference point of the reference axis.

4. The method for detecting parallelism error of hole group axes according to claim 3, characterized in that: The setting of a cylindrical model comprises: Assume the coordinates of the reference axis in the camera coordinates are: In the formula, (X D ,Y D ,Z D ) represents the coordinate value of any point on the reference axis; Change the above formula and use Z D X D , Y D Parameterized representation: in, Parameterized representation of X D ,Y D The equation can be regarded as the equation of two space planes, which intersect at the reference axis. The equations of the two planes are obtained by the least squares method, and the overdetermined equation system is obtained: Combine the above overdetermined equations to solve the parameters of the plane equation: Among them, (x Ci ,y Ci , z Ci ) is the camera coordinate of the center point of the ellipse of the intersection line corresponding to each scan of the reference hole; By formula Determine the z-axis coordinate of a center point on the reference axis C0 ; According to the obtained parameters k1, b1, k2, b2 and z C0 , calculate the x-axis coordinate x of the center point on the reference axis C0 and y-axis coordinate yC0; Assign a value to the p component in the direction vector of the reference axis, and use k1 and k2 to solve the m and n components on the direction vector; The obtained (z C0 ,y C0 , z C0 ) are used as the center point coordinates of the cylindrical model, (m, n, p) are used as the direction vector of the central axis of the cylinder, and half of the short axis length of each intersection line of the reference hole obtained by elliptical fitting is used as the initial value of the cylinder radius r to construct an initial cylindrical model, which is used for subsequent nonlinear optimization.

5. The method for detecting parallelism error of hole group axes according to claim 1, characterized in that: The step of establishing an evaluation plane perpendicular to the reference axis according to the reference axis and the ellipse centers of the measured holes comprises: According to the coordinate values ​​of the ellipse center points of each intersection line of the measured hole, the numerical mean values ​​of the coordinates of all the ellipse center points of the measured hole in the three dimensions of X, Y, and Z are calculated to obtain the mean point; According to the coordinates of the mean point and the direction vector of the reference axis, an evaluation plane is constructed, and the equation of the evaluation plane is: m(X E -x C )+n(Y E -y C )+p(Z E -z C )=0: Among them, (m,n,p) is the direction vector of the reference axis, (x C ,y C , z C ) is the coordinate of the mean point, (X E , Y E , Z E ) are the coordinates of any point on the evaluation plane.

6. The method for detecting parallelism error of hole group axes according to claim 1, characterized in that: The method of obtaining the minimum enclosing circle enclosing all projection points on the projection plane includes: The projection points are sorted according to the order of line laser scanning to form a sequence; Take the first two projection points d1 and d2 of the sequence, and use the distance between d1 and d2 as the diameter to construct the initial minimum enclosing circle C2; Traverse all the projection points in the sequence in the order of the sequence, add the projection points to the current minimum enclosing circle one by one, and determine whether the added projection points are within the current minimum enclosing circle; For the i-th projection point d i , if d i If the circle is within the current minimum enclosing circle, there is no need to update the enclosing circle; If d i If it is not within the current minimum enclosing circle, the projection points d1 and d i The distance is the diameter to construct the middle circle C i ; Check whether the 2nd to i-1th projection points in the sequence are contained in the middle circle C i If the 2nd to i-1th projection points are contained in the middle circle C i Inside, the middle circle C i Become the new smallest containing circle; If there is a projection point d among the 2nd to i-1th projection points j (2≤j≤i-1) is not in the middle circle C i If d1 and d j The distance is the diameter to construct the middle circle C j ; Then check whether the 2nd to j-1th projection points in the sequence are included in the middle circle C j If the 2nd to j-1th projection points are contained in the middle circle C j Inside, the middle circle C j Become the new smallest containing circle; If there is a projection point d among the 2nd to j-1th projection points k (2≤k≤j-1) is not in the middle circle C j If the projection point d i d j d k Three points construct a new circle C i ', construct the new circle C i ' is the smallest enclosing circle that contains the first i points of the sequence.

7. The method for detecting parallelism error of hole group axes according to claim 1, characterized in that: The measuring of the diameter of the minimum enclosing circle and comparing it with the preset parallelism tolerance, and judging whether the parallelism of the measured hole is qualified according to the comparison result includes: If the diameter of the minimum enclosing circle is greater than the preset parallelism tolerance, it is determined that the measured hole exceeds the prescribed parallelism error range and does not meet the specification requirements; If the diameter of the minimum containing circle is less than or equal to the preset parallelism tolerance, it is determined that the parallelism error of the measured hole is within an acceptable range and meets the design requirements.

8. The method for detecting parallelism error of hole group axes according to claim 1, characterized in that: Before respectively obtaining the ellipse centers corresponding to the intersection lines of the reference hole and the measured hole through an ellipse fitting algorithm based on the first point cloud data and the second point cloud data, the method further includes: The first point cloud data and the second point cloud data are subjected to noise filtering and error point elimination processing to allow point cloud data within a specific dimensional range to pass through and eliminate point cloud data beyond a preset range.

9. A device for detecting parallelism error of a hole group axis, characterized in that: The hole group axis parallelism error detection device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the method as described in any one of claims 1 to 8 when executed by the processor.

10. A storage medium, characterized in that: The storage medium stores a computer program, which implements the steps of the method according to any one of claims 1 to 8 when executed by a processor.

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