Steel pipe main arch rib point cloud scanning splicing method and device

Abstract

The invention provides a steel tube main arch rib point cloud scanning splicing method and device, and relates to the technical field of steel tube concrete arch bridge construction. The method comprises the steps of performing multi-station scanning and cutting through a ground three-dimensional laser scanner, accurately obtaining point clouds of a key area, reducing data redundancy, distinguishing the point clouds through filtering and plane fitting, determining component postures, determining a relative position relation with the ground, improving splicing accuracy, performing normal vector calculation and clustering to remove irrelevant point clouds, and finally obtaining the component postures. The method comprises the following steps: firstly, obtaining a cylinder node cloud to facilitate subsequent processing, converting the complex three-dimensional cylinder node cloud into two-dimensional plane data and subdividing the two-dimensional plane data through two-dimensional projection and slicing processing, creating favorable conditions for cylindrical curved surface fitting, and finally realizing accurate splicing through cylindrical curved surface fitting and rotation and translation parameter calculation; according to the method, the problem that component point cloud data collected by different observation stations cannot be efficiently spliced is effectively solved, and the point cloud data of the component is more complete and more accurate.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of steel tube concrete arch bridge construction, and in particular to a method and device for scanning and splicing point clouds of steel tube main arch ribs. Background Art

[0002] In bridge construction, the manufacture and installation of the steel tube main arch ribs of steel tube concrete arch bridges face many challenges. The manufacture and installation of steel tube main arch ribs are often carried out in segments and parts. In order to ensure the accuracy of component manufacturing, pre-assembly in the factory is particularly important. The traditional pre-assembly measurement technology is to use a plumb line to project the segments onto the ground, and then use a high-precision total station to measure the projection coordinates and linear shape of the components. Although this method is effective, it is cumbersome and inefficient.

[0003] With the development of technology, 3D laser scanning technology has been introduced into the pre-assembly process. By acquiring point cloud data on the surface of steel components and using computer technology for virtual pre-assembly, the pre-assembly is digitalized and scientific. This method not only improves the accuracy and efficiency of measurement, but also reduces the occupation of the site. However, when using 3D laser scanning technology, due to the complex shape of the components (such as cylinder sections), there is a problem of scanning blind spots, which needs to be solved by supplementary measurement and data splicing at different measuring stations.

[0004] There are precedents for using handheld or ground-based 3D laser scanners for data collection. In actual operation, these methods still require the deployment of targets for data stitching. The existing data stitching methods are not suitable for this scenario and have large errors.

[0005] In summary, there is an urgent need for a method that can automatically splice the surface point clouds of the steel pipe main arch ribs at different measuring stations in this scenario with high efficiency and accuracy, as well as a method that can evaluate the corresponding splicing accuracy. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a method and device for scanning and splicing point clouds of steel pipe main arch ribs in view of the deficiencies in the prior art.

[0007] The technical solution of the present invention to solve the above technical problem is as follows: A method for scanning and splicing point clouds of steel pipe main arch ribs, comprising the following steps:

[0008] S1, using a plurality of measuring stations of a preset ground three-dimensional laser scanner to scan the bridge steel member to obtain a point cloud of the main arch rib steel member, and cutting out a steel member point cloud area from the point cloud of the main arch rib steel member;

[0009] S2, filtering the steel component point cloud area to obtain a non-component point cloud and a component point cloud, performing plane fitting processing on the non-component point cloud, and determining a fitting plane corresponding to the component point cloud through the fitting plane;

[0010] S3, calculating the normal vector of the fitting plane corresponding to the component point cloud, and clustering the calculated normal vector to obtain the cylinder node cloud;

[0011] S4, performing two-dimensional plane projection processing on the cylinder node cloud to obtain a cylinder node cloud projection plane;

[0012] S5, calculating boundary points of the cylinder node cloud projection plane, connecting the calculated boundary points into a sideline, determining a sideline perpendicular line according to the sideline, establishing a coordinate axis according to the sideline and the sideline perpendicular line, and equidistantly slicing the cylinder segments in the cylinder node cloud projection plane according to the coordinate axis to obtain a plurality of cylinder segment point clouds;

[0013] S6, performing cylindrical surface fitting processing on the plurality of cylinder segment point clouds to obtain a cylindrical surface corresponding to each cylinder segment point cloud;

[0014] S7. Based on the fact that the cylindrical surfaces after fitting the point cloud of each corresponding cylinder segment are the side surfaces of the same cylinder, the rotation parameters and translation parameters for splicing the cylindrical surfaces of all cylinder segments into one are calculated, and the point clouds of all cylindrical surfaces are spliced ​​according to the translation parameters and rotation parameters to obtain the final component point cloud.

[0015] Another technical solution of the present invention to solve the above technical problem is as follows: a point cloud scanning and splicing device for a steel pipe main arch rib, comprising:

[0016] A preprocessing module is used to scan the bridge steel components using multiple measuring stations of a preset ground three-dimensional laser scanner to obtain a point cloud of the main arch rib steel component, and to cut out a steel component point cloud area from the main arch rib steel component point cloud;

[0017] A plane fitting module is used to filter the steel component point cloud area to obtain a non-component point cloud and a component point cloud, and perform plane fitting on the non-component point cloud to determine a fitting plane corresponding to the component point cloud through a fitting plane;

[0018] A clustering processing module is used to calculate the normal vector of the fitting plane corresponding to the component point cloud, and to perform clustering processing on the calculated normal vector to obtain a cylinder node cloud;

[0019] A projection module, used for performing two-dimensional plane projection processing on the cylinder node cloud to obtain a cylinder node cloud projection plane;

[0020] A slicing module is used to calculate the boundary points of the cylinder node cloud projection plane, connect the calculated boundary points into a sideline, determine the sideline perpendicular line according to the sideline, establish a coordinate axis according to the sideline and the sideline perpendicular line, and perform equidistant slicing on the cylinder segment in the cylinder node cloud projection plane according to the coordinate axis to obtain a plurality of cylinder segment point clouds;

[0021] A surface fitting module, used for performing cylindrical surface fitting processing on the plurality of cylinder segment point clouds to obtain a cylindrical surface corresponding to each cylinder segment point cloud;

[0022] The point cloud splicing module is used to calculate the rotation parameters and translation parameters for splicing the cylindrical surfaces of all cylinder sections into one based on the cylindrical surface fitted after the point cloud of each corresponding cylinder segment is the side of the same cylinder, and splice the point clouds of all cylindrical surfaces according to the translation parameters and rotation parameters to obtain the final component point cloud.

[0023] The beneficial effects of the present invention are as follows: through multi-station scanning and cropping of a ground-based 3D laser scanner, point clouds in key areas are accurately acquired, data redundancy is reduced, filtering and plane fitting distinguish point clouds and determine component postures, clarify the relative position relationship with the ground, and improve splicing accuracy; normal vector calculation and clustering are used to remove irrelevant point clouds, and cylinder node clouds are obtained for subsequent processing; two-dimensional projection and slicing processing convert complex three-dimensional cylinder node clouds into two-dimensional plane data and subdivide them, creating favorable conditions for cylindrical surface fitting; ultimately, accurate splicing is achieved through cylindrical surface fitting and rotation and translation parameter calculation, effectively solving the problem that component point cloud data collected by different stations cannot be efficiently spliced, and component point cloud data is more complete and more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic flow chart of a method for scanning and splicing a point cloud of a steel pipe main arch rib provided in an embodiment of the present invention;

[0025] Figure 2 A schematic connection diagram of each module of the steel pipe main arch rib point cloud scanning and splicing module provided in an embodiment of the present invention;

[0026] Figure 3 A schematic diagram of determining the equidistant cutting direction of a component point cloud provided by an embodiment of the present invention;

[0027] Figure 4 A schematic diagram of a point cloud of a component accessory provided in an embodiment of the present invention;

[0028] Figure 5 This is an accuracy assessment diagram provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0030] In order to solve the problem of large errors in existing data splicing methods, the present invention aims to provide a method for accurately splicing and evaluating the accuracy of component point clouds at different measuring stations when a ground 3D laser scanner is used to scan the steel component point cloud during the pre-assembly process of the steel pipe main arch rib, aiming to solve the problem that the component point cloud data at different measuring stations cannot be perfectly spliced ​​when the ground 3D laser scanner is used to scan the steel pipe main arch rib point cloud data to complete the pre-assembly process. The following is a detailed introduction through specific embodiments.

[0031] Example 1: Figure 1 As shown, an embodiment of the present invention provides a method for scanning and splicing point clouds of a steel pipe main arch rib, comprising the following steps:

[0032] S1, using a plurality of measuring stations of a preset ground three-dimensional laser scanner to scan the bridge steel member to obtain a point cloud of the main arch rib steel member, and cutting out a steel member point cloud area from the point cloud of the main arch rib steel member;

[0033] S2, filtering the steel component point cloud area to obtain a non-component point cloud and a component point cloud, performing plane fitting processing on the non-component point cloud, and determining a fitting plane corresponding to the component point cloud through the fitting plane;

[0034] S3, calculating the normal vector of the fitting plane corresponding to the component point cloud, and clustering the calculated normal vector to obtain the cylinder node cloud;

[0035] S4, performing two-dimensional plane projection processing on the cylinder node cloud to obtain a cylinder node cloud projection plane;

[0036] S5, calculating boundary points of the cylinder node cloud projection plane, connecting the calculated boundary points into a sideline, determining a sideline perpendicular line according to the sideline, establishing a coordinate axis according to the sideline and the sideline perpendicular line, and equidistantly slicing the cylinder segments in the cylinder node cloud projection plane according to the coordinate axis to obtain a plurality of cylinder segment point clouds;

[0037] S6, performing cylindrical surface fitting processing on the plurality of cylinder segment point clouds to obtain a cylindrical surface corresponding to each cylinder segment point cloud;

[0038] S7. Based on the fact that the cylindrical surfaces after fitting the point cloud of each corresponding cylinder segment are the side surfaces of the same cylinder, the rotation parameters and translation parameters for splicing the cylindrical surfaces of all cylinder segments into one are calculated, and the point clouds of all cylindrical surfaces are spliced ​​according to the translation parameters and rotation parameters to obtain the final component point cloud.

[0039] In this embodiment, the point cloud data is preprocessed. The software Cloudcompare is used to import the data, and the scene data collected by the ground 3D laser scanner is processed using the cropping function to retain the component point cloud. The retained point cloud is subjected to point cloud basic processing methods such as denoising. The above operations are performed on all collected data.

[0040] In this step, since there is actually a partial contact area between the component and the non-component area, the contact area cannot accurately retain the component point cloud using the cropping function, and this part of the data needs to be processed through step S2 later.

[0041] In this embodiment, the use of multi-station scanning and cropping of a ground-based three-dimensional laser scanner can accurately obtain the key areas of the point cloud of the main arch rib steel component, provide an accurate data basis for subsequent processing, and effectively reduce data redundancy. Secondly, the filtering and plane fitting operations not only distinguish between component and non-component point clouds, but also determine the component posture through the non-component point cloud fitting plane, and especially clarify the relative position relationship between the component and the ground. This common feature provides an important reference for subsequent splicing and greatly improves the accuracy and reliability of splicing. Furthermore, normal vector calculation and clustering processing remove irrelevant point clouds to obtain a pure tube node cloud, highlighting the cylindrical characteristics of the tube section, which is conducive to subsequent targeted processing. Then, two-dimensional projection and slicing processing convert the complex three-dimensional tube node cloud into two-dimensional plane data and subdivide it, reducing the difficulty of data processing and creating favorable conditions for cylindrical surface fitting. Finally, the cylindrical surface fitting and point cloud stitching based on rotation and translation parameters successfully achieved the precise stitching of point cloud data of components from different measuring stations, solving the problems of low efficiency and poor accuracy in data stitching in the existing technology. The stitched component point cloud data is complete and accurate, providing a solid guarantee for subsequent operations such as line extraction and reverse modeling, promoting the digitalization and scientificization of work related to bridge steel components, and improving work efficiency and quality.

[0042] Preferably, in S2, filtering is performed on the steel component point cloud region to obtain a non-component point cloud and a component point cloud, and plane fitting is performed on the non-component point cloud to determine a fitting plane corresponding to the component point cloud by fitting the plane, specifically:

[0043] The steel component point cloud region is filtered by a cloth simulation filtering algorithm to obtain a non-component point cloud and a component point cloud;

[0044] Assume that the fitting plane corresponding to the non-component point cloud passes through the point p(x0, y0, z0), and fit the non-component point cloud based on the least squares method to obtain the fitting plane:

[0045] a(x-x0)+b(y-y0)+c(z-z0)=0,

[0046] Among them, a, b and c are the normal vectors of the fitting plane;

[0047] The fitting plane is transformed based on the relative position relationship between the component position in the component point cloud and the fitting plane to obtain the fitting plane corresponding to the component point cloud.

[0048] In this embodiment, the component point cloud processed in step S1 is filtered to obtain relatively complete component point cloud data. A cloth simulation filtering algorithm is used to filter out component and non-component point clouds. The non-component point cloud data is fitted with a least squares plane, and the fitted plane is used to determine partial posture information of the component.

[0049] In this step, the component posture information refers to the overall position information of the component point cloud. The overall position information of the component point cloud includes the relative position information of the two component point clouds, among which the relative position information of the two component point clouds and the ground is a major focus and can be used as a common feature in subsequent splicing. The partial component posture information obtained in this step is the relative position information of the point cloud and the ground.

[0050] It should be understood that the use of non-component point cloud data to fit a plane to determine the component posture is because the non-component point cloud is a ground point cloud, and the ground in this area is theoretically the same ground, so the fitted plane should be the same, and the position of the component and the ground is relative, so this plane can be used to determine the partial posture of the component.

[0051] In this embodiment, a cloth simulation filtering algorithm is used to screen component and non-component point clouds to obtain relatively complete component point cloud data. The non-component point cloud data is fitted with a plane using the least squares method, and the fitted plane is used to determine part of the component's posture information (the overall position information of the component point cloud, especially the relative position information to the ground), providing common features for subsequent splicing.

[0052] Preferably, if Figure 4 As shown, in S3, the normal vector of the fitting plane corresponding to the component point cloud is calculated, and the calculated normal vector is clustered to obtain the cylinder node cloud, which is specifically:

[0053] Obtain the minimum eigenvector in the fitting plane corresponding to the component point cloud as the normal vector;

[0054] Multiple parameters in the Euclidean clustering algorithm are set, including the minimum number of points in each cluster, the maximum number of points and the minimum Euclidean distance between two different clusters. The calculated normal vector is clustered using the Euclidean clustering algorithm and the set parameters to obtain multiple accessory point clouds on the component. The multiple accessory point clouds are removed to obtain the tube node cloud.

[0055] In this embodiment, the normal vector of the component point cloud is calculated, and the point cloud of the component box beam area is removed by normal vector clustering. The remaining accessories on the component are removed by methods such as Euclidean clustering and density clustering. Finally, point cloud data with cylindrical characteristics containing only the cylinder section is obtained.

[0056] In this step, the theoretical method for calculating the normal vector of the component point cloud is the same as the method for obtaining the normal vector of the plane to be fitted by principal component analysis of the point cloud described in step S2. Both methods use the least squares method to fit a plane based on all points in the neighborhood of a point in the component point cloud, and use the simultaneous equations of the fitting plane parameters to solve the minimum eigenvector of the equation group, which is the normal vector.

[0057] In this step, it is necessary to explain that the steel tube main arch rib structure of the steel tube concrete arch bridge involved in the present invention comprises a cylinder section, a box beam and other accessories welded on the component. The other accessories include an arch rib base, connecting components between arch ribs, etc. In this step, the point cloud of the component box beam area is removed by normal vector clustering, and the point cloud of accessories is removed by density clustering and other methods.

[0058] In this step, the Euclidean clustering method includes but is not limited to using a neighbor query algorithm based on KD-Tree, and the parameters are set as follows: the minimum number of points in each cluster is 30, the maximum number of points is 10,000, and the minimum Euclidean distance between two different clusters is 0.2m. Density clustering includes but is not limited to using the DBSCAN algorithm to divide the observation data in the n×p data matrix X into clusters, and the parameters are set as follows: specifying an epsilon value of 2 and a minimum number of cluster points of 50.

[0059] In this embodiment, the normal vector of the component point cloud is calculated and the point cloud of the component box girder area is removed by normal vector clustering. Then, the remaining accessories on the component (such as the arch rib base, connecting components between arch ribs, etc.) are removed by combining methods such as Euclidean clustering or density clustering. Finally, point cloud data with cylindrical characteristics containing only the barrel section is obtained, which is convenient for subsequent processing operations on the barrel section.

[0060] The theoretical method for calculating the normal vector of the component point cloud is clarified (the same as the method of obtaining the normal vector of the plane to be fitted through principal component analysis of the point cloud), and the specific parameter settings of the Euclidean clustering algorithm and the density clustering algorithm are given, which enhances the operability and repeatability of the method.

[0061] Preferably, in S4, the cylinder node cloud is subjected to two-dimensional plane projection processing to obtain a cylinder node cloud projection plane, specifically:

[0062] The projection plane is fitted and calculated on the cylinder node cloud by the RANSAC algorithm to obtain the projection plane:

[0063] Ax+By+Cz+D=0,

[0064] Among them, A, B and C are the normal vector components corresponding to the projection plane, and D is the position parameter of the projection plane relative to the coordinate origin;

[0065] The new coordinates of orthogonally projecting the cylinder node cloud onto the projection plane are calculated using a coordinate calculation formula, and the coordinate calculation formula is:

[0066] xp=x×(B 2 +C 2 )-A×(y×B+z×C+D) / norm(|A,B,C|),

[0067] yp=y×(A 2 +C 2 )-B×(x×A+z×C+D) / norm(|A,B,C|),

[0068] zp=z×(A 2 +B 2 )-C×(x×A+y×B+D) / norm(|A,B,C|),

[0069] Among them, xp, yp and zp are the new coordinates, norm(|A,B,C|) is the Euclidean length of the normal vector of the projection plane,

[0070] Each new coordinate of the cylinder node cloud is projected onto the projection plane to obtain the cylinder node cloud projection plane.

[0071] It should be understood that the RANSAC (Random Sample Consensus) algorithm is an iterative model parameter estimation method. The basic idea is to estimate the model parameters by repeatedly selecting random subsets of the data, and then evaluate the entire data set based on these parameters to determine which data points are "inliers" that meet the model and which are "outliers" that do not meet the model.

[0072] In this embodiment, it is used to perform projection plane fitting calculation on the cylinder node cloud. The specific process is to randomly select some points from the cylinder node cloud to fit a projection plane, and then calculate the distance from other points to the plane. Points within a certain threshold are considered to be inner points, otherwise they are outer points. By repeating this process continuously, the plane containing the most inner points is found as the final projection plane. In this way, the influence of noise and outliers can be effectively removed, and a more accurate and reliable projection plane can be obtained, thereby converting the three-dimensional cylinder node cloud into a two-dimensional plane, laying the foundation for subsequent operations such as determining boundary points and slicing, reducing the difficulty of data processing, and improving the accuracy and stability of the entire point cloud processing process.

[0073] In this embodiment, the projection plane fitting calculation of the cylinder node cloud is performed through the RANSAC algorithm, and the projection plane of the cylinder node cloud is obtained using the coordinate calculation formula, so that the three-dimensional cylinder node cloud is converted into a two-dimensional plane, which reduces the complexity of subsequent processing and provides a basis for accurately determining the boundary points of the cylinder node cloud on the two-dimensional plane and performing slicing and other operations.

[0074] Preferably, if Figure 3 As shown, in S5, the boundary points of the cylinder node cloud projection plane are calculated, the calculated boundary points are connected into a sideline, the sideline perpendicular is determined according to the sideline, a coordinate axis is established according to the sideline and the sideline perpendicular, and the cylinder section in the cylinder node cloud projection plane is sliced ​​equidistantly according to the coordinate axis to obtain a plurality of cylinder section segment point clouds, specifically:

[0075] Assume that the reference point P0 (x0, y0) and the target point P1 (x1, y1) are two points on the projection plane of the cylinder node cloud, and the polar angle θ of the target point P1 relative to the reference point P0 is calculated by the polar angle calculation formula. The polar angle θ corresponding to the target point P1 is:

[0076] θ=arctan2(y1-y0,x1-x0);

[0077] Suppose the vector on the projection plane of the tube node cloud is are two vectors on the projection plane of the tube node cloud, and the vector is calculated by the cross product calculation formula and vector The two-dimensional vector cross product between is:

[0078]

[0079] In this way, the polar angle θ and the two-dimensional vector cross product corresponding to all point clouds on the projection plane of the tube node cloud are calculated;

[0080] The convex hull vertex is identified by using the convex hull algorithm for the polar angle θ and the two-dimensional vector cross product corresponding to all point clouds, and the identified convex hull vertices are connected as boundary points to obtain the edge line L;

[0081] The edge line perpendicular line L' is determined according to the edge line L, and the edge line L and the edge line perpendicular line L' are used as the X axis and Y axis of the coordinate axis respectively. The cylinder node cloud in the cylinder node cloud projection plane is sliced ​​equidistantly in the Y axis direction to obtain multiple cylinder segment point clouds.

[0082] The process of determining the boundary points of the tube node cloud based on the convex hull vertices is as follows:

[0083] 1) Calculate the polar angle and determine the convex hull vertex:

[0084] First, calculate the polar angle of each point on the plane relative to a reference point (usually the point with the smallest horizontal coordinate and the smallest vertical coordinate in the point set). The polar angle calculation formula is θ=arctan2(y1-y0,x1-x0), where (x1, y1) is the point to be calculated for the polar angle, and (x0, y0) is the reference point. By calculating the polar angle, the angle information of each point relative to the reference point can be obtained, thereby determining the relative direction of the point on the plane.

[0085] Next, we determine the convex hull vertex by calculating the two-dimensional vector cross product. Suppose there are two vectors on the plane and The formula for calculating the cross product is: When calculating the convex hull, the points are connected in order according to their polar angles to form vectors, and the cross product of adjacent vectors is determined to determine whether it is a convex hull vertex. If the cross product is greater than 0, it means that the vector rotates counterclockwise, and the point may be a convex hull vertex; if the cross product is less than 0, it means that the vector rotates clockwise, and the point is unlikely to be a convex hull vertex.

[0086] 2) Connect the convex hull vertices to form a boundary:

[0087] After the convex hull vertices are determined by the above calculations, these convex hull vertices are connected in order, and the lines formed are the boundaries of the tube node cloud. These boundary points surround the main part of the tube node cloud, and can accurately outline the general shape of the tube node cloud on the two-dimensional plane, providing important boundary information for subsequent operations (such as equidistant slicing of the tube node cloud according to the coordinate axis, etc.).

[0088] In this embodiment, the boundary points on the projection plane of the cylinder node cloud are calculated based on the convex hull algorithm, and the convex hull vertices are determined by calculating the polar angle and the two-dimensional vector cross product. This can accurately outline the approximate shape of the cylinder node cloud on the two-dimensional plane, providing important boundary information for the subsequent equidistant slicing of the cylinder node cloud according to the coordinate axis.

[0089] The process of determining the boundary points of the tube node cloud based on the convex hull vertices is described in detail, including the specific methods of calculating the polar angle and judging the convex hull vertices and connecting the convex hull vertices to form the boundary, making the operation of this step clearer.

[0090] Preferably, in S6, cylindrical surface fitting processing is performed on the plurality of cylinder segment point clouds to obtain a cylindrical surface corresponding to each cylinder segment point cloud, specifically:

[0091] Let Q1(x1,y1,z2), Q2(x2,y2,z2) and Q(x3,y3,z3) be three points on the cylindrical surface, and calculate the vector and vector For the vector and the vector Perform cross product calculation to obtain the vertical plane vector, and determine the axis direction of the cylinder based on the vertical plane vector;

[0092] Assume that the center coordinates of the cylinder are (x0, y0) and the radius is r. According to the circle equation (x-x0)+(y-y0)2=r, the least squares method is used to fit the circle. Based on the error function E, the partial derivatives of the center coordinates and radius are calculated and the error function E is set to 0. The error function E equation group is solved to obtain the values ​​of the center coordinates and radius. The error function E equation group is:

[0093]

[0094] It should be understood that for multiple points on the cylindrical surface, the above calculation process can be repeated to obtain multiple vectors perpendicular to different three-point planes. Since the axis direction of the cylinder is perpendicular to these perpendicular vectors, the direction vector of the cylinder axis is determined by analyzing multiple such vector combinations.

[0095]

[0096] A common method to determine the axis direction of the cylinder based on the perpendicular plane vectors is to perform some kind of averaging or fitting operation on these vectors. For example, you can use multiple vectors Add (or perform weighted addition, giving different weights according to the distribution of points), and then normalize the added vectors. The unit vector obtained is the cylinder axis direction vector Normalization is to divide the vector by its modulus. The formula for calculating the modulus length is Then the normalized cylinder axis direction vector In this way, by comprehensively considering the information of multiple points, the direction vector of the cylinder axis can be determined more accurately to adapt to various possible postures and shape changes of the cylinder.

[0097] In this embodiment, the vertical plane vector is obtained by calculating the vector cross product formed by three points on the cylindrical surface, and then the axial direction of the cylinder is determined. The circle is fitted by the least squares method according to the circle equation to solve the values ​​of the center coordinates and the radius. The cylindrical surface can be accurately fitted to the cylinder segment point cloud, adapting to various postures and shape changes that may exist in the cylinder, and providing an accurate cylindrical surface model for subsequent point cloud stitching.

[0098] Preferably, in S7, based on the fact that the cylindrical surfaces after fitting the point clouds of each corresponding cylinder segment are the side surfaces of the same cylinder, the rotation parameters and translation parameters for combining the cylindrical surfaces of all cylinder segments into one are calculated, specifically:

[0099] Based on the cylindrical surface fitted by the point cloud of each corresponding cylinder segment as the side of the same cylinder, calculate the direction vector corresponding to the two cylindrical surfaces to be spliced and direction vector

[0100]

[0101] According to the direction vector and direction vector Calculate the rotation angle;

[0102] According to the direction vector and direction vector Calculate the axis of rotation;

[0103] Construct a rotation matrix K according to the rotation angle and the rotation axis, and construct an antisymmetric matrix of the rotation matrix K using the Rodriguez rotation formula to obtain a rotation matrix R, which is the rotation parameter;

[0104] Calculate the translation vector required to move the center of the bottom of one cylinder to the center of the bottom of another cylinder based on the coordinates of the center of the bottom of two adjacent cylinders Is the translation parameter, the translation vector corresponding to the cylinder to be translated for:

[0105]

[0106] Among them, the coordinates of the center of the bottom surface of the reference cylinder are O0 (X0, Y0, Z0), and the coordinates of the center of the bottom surface of the cylinder to be translated are O1 (X1, Y1, Z1).

[0107] In this embodiment, based on the fact that the cylindrical surface after fitting the point cloud of each corresponding cylinder segment is the side of the same cylinder, the rotation parameters (including the rotation angle and rotation axis, and then constructing the rotation matrix) and translation parameters of the cylindrical surfaces of all cylinder segments are accurately calculated, which provides the key parameter basis for accurate point cloud stitching and helps to achieve perfect stitching of component point cloud data from different measuring stations.

[0108] Preferably, in S7, all cylindrical surfaces are spliced ​​into point clouds according to the rotation parameters and the translation parameters to obtain the final component point cloud, specifically:

[0109] Convert each point cloud coordinate in the cylindrical surface into the corresponding homogeneous coordinate (x, y, z, 1), and convert each homogeneous coordinate Multiply them by the rotation matrix R to get the point cloud after the coordinates of each point cloud are rotated.

[0110]

[0111] set up Construct the translation matrix T, construct the translation matrix, and rotate the point cloud coordinates of each point cloud Multiplying with the translation matrix T, we get the point cloud after rotation and translation, and get the final component point cloud based on the point cloud after rotation and translation.

[0112] In this embodiment, after converting the coordinates of each point cloud in the cylindrical surface into homogeneous coordinates, they are multiplied with the rotation matrix and the translation matrix respectively to obtain the point cloud after rotation and translation, thereby realizing point cloud splicing of all cylindrical surfaces according to the rotation parameters and translation parameters to obtain the final component point cloud, completing the process of scanning and splicing the point cloud of the entire steel pipe main arch rib, so that the splicing result can be presented in the form of a final component graphic, which is convenient for intuitive observation and evaluation of the splicing effect.

[0113] Preferably, the direction vector and direction vector Calculate the rotation angle, specifically:

[0114] The direction vector is calculated by the vector dot product formula and the vector modulus formula and direction vector The rotation angle of , the vector dot product formula is:

[0115]

[0116] The vector modulus formula is:

[0117]

[0118] in, Obtaining the angle through inverse trigonometric functions θ is the rotation angle.

[0119] In this embodiment, the rotation angles of the two direction vectors are accurately calculated through the vector dot product formula and the vector modulus formula, which provides the necessary angle information for determining the rotation parameters for splicing the cylindrical surfaces of all tube sections into one, and helps to achieve accurate point cloud splicing.

[0120] Preferably, the direction vector and direction vector Calculate the rotation axis as:

[0121] Calculate the direction vector and direction vector The cross product of Get the cross product vector That is the rotation axis, where xn=y1z2-z1y2, yn=z1x2-x1z2, zn=x1y2-y1x2.

[0122] In this embodiment, the rotation axis is obtained by calculating the cross product of two direction vectors, which provides key axis information for determining the rotation parameters for stitching all cylindrical surfaces of all tube sections into one, and constructs a rotation matrix together with the rotation angle, thereby achieving accurate point cloud stitching operations.

[0123] like Figure 5 As shown, the following is a method for scanning and splicing a steel tube main arch rib point cloud according to the present invention for splicing components and evaluating the splicing accuracy, which specifically includes the following steps:

[0124] Step T1: segment the spliced ​​data again according to the above-mentioned section starting line, and visualize each segmented section.

[0125] Step T2: Determine the stitching accuracy based on the visual graphics, which should be such that no delamination is visible to the naked eye.

[0126] Compared with the existing point cloud splicing methods, the point cloud splicing method of the present invention is aimed at the scenario of using a ground three-dimensional laser scanner to collect the point cloud data required for the pre-assembly of the steel pipe main arch rib point cloud. According to the steps, the component point cloud data of different measuring stations can be accurately spliced ​​and the splicing accuracy can be evaluated, which effectively solves the problem that the component point cloud data collected by different measuring stations cannot be efficiently spliced. Solving this problem can make the point cloud data of the component more complete and more accurate, and then the spliced ​​accurate point cloud can be used to extract the line shape, reverse modeling and other operations.

[0127] Example 2: Figure 2 As shown, an embodiment of the present invention further provides a point cloud scanning and splicing device for a steel pipe main arch rib, comprising:

[0128] A preprocessing module is used to scan the bridge steel components using multiple measuring stations of a preset ground three-dimensional laser scanner to obtain a point cloud of the main arch rib steel component, and to cut out a steel component point cloud area from the main arch rib steel component point cloud;

[0129] A plane fitting module is used to filter the steel component point cloud area to obtain a non-component point cloud and a component point cloud, and perform plane fitting on the non-component point cloud to determine a fitting plane corresponding to the component point cloud through a fitting plane;

[0130] A clustering processing module is used to calculate the normal vector of the fitting plane corresponding to the component point cloud, and to perform clustering processing on the calculated normal vector to obtain a cylinder node cloud;

[0131] A projection module, used for performing two-dimensional plane projection processing on the cylinder node cloud to obtain a cylinder node cloud projection plane;

[0132] A slicing module is used to calculate the boundary points of the cylinder node cloud projection plane, connect the calculated boundary points into a sideline, determine the sideline perpendicular line according to the sideline, establish a coordinate axis according to the sideline and the sideline perpendicular line, and perform equidistant slicing on the cylinder segment in the cylinder node cloud projection plane according to the coordinate axis to obtain a plurality of cylinder segment point clouds;

[0133] A surface fitting module, used for performing cylindrical surface fitting processing on the plurality of cylinder segment point clouds to obtain a cylindrical surface corresponding to each cylinder segment point cloud;

[0134] The point cloud splicing module is used to calculate the rotation parameters and translation parameters for splicing the cylindrical surfaces of all cylinder sections into one based on the cylindrical surface fitted after the point cloud of each corresponding cylinder segment is the side of the same cylinder, and splice the point clouds of all cylindrical surfaces according to the translation parameters and rotation parameters to obtain the final component point cloud.

[0135] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0136] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0137] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0138] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0139] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0140] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for scanning and splicing point clouds of steel pipe main arch ribs, characterized in that: The steps include: S1, using a plurality of measuring stations of a preset ground three-dimensional laser scanner to scan the bridge steel member to obtain a point cloud of the main arch rib steel member, and cutting out a steel member point cloud area from the point cloud of the main arch rib steel member; S2, filtering the steel component point cloud area to obtain a non-component point cloud and a component point cloud, performing plane fitting processing on the non-component point cloud, and determining a fitting plane corresponding to the component point cloud through the fitting plane; S3, calculating the normal vector of the fitting plane corresponding to the component point cloud, and clustering the calculated normal vector to obtain the cylinder node cloud; S4, performing two-dimensional plane projection processing on the cylinder node cloud to obtain a cylinder node cloud projection plane; S5, calculating boundary points of the cylinder node cloud projection plane, connecting the calculated boundary points into a sideline, determining a sideline perpendicular line according to the sideline, establishing a coordinate axis according to the sideline and the sideline perpendicular line, and equidistantly slicing the cylinder segments in the cylinder node cloud projection plane according to the coordinate axis to obtain a plurality of cylinder segment point clouds; S6, performing cylindrical surface fitting processing on the plurality of cylinder segment point clouds to obtain a cylindrical surface corresponding to each cylinder segment point cloud; S7. Based on the fact that the cylindrical surfaces after fitting the point cloud of each corresponding cylinder segment are the side surfaces of the same cylinder, the rotation parameters and translation parameters for splicing the cylindrical surfaces of all cylinder segments into one are calculated, and the point clouds of all cylindrical surfaces are spliced ​​according to the translation parameters and rotation parameters to obtain the final component point cloud.

2. The method for scanning and splicing point clouds of steel pipe main arch ribs according to claim 1 is characterized in that: In S2, the steel component point cloud region is filtered to obtain a non-component point cloud and a component point cloud, and the non-component point cloud is plane-fitted to determine a fitting plane corresponding to the component point cloud by fitting the plane, specifically: The steel component point cloud region is filtered by a cloth simulation filtering algorithm to obtain a non-component point cloud and a component point cloud; Assume that the fitting plane corresponding to the non-component point cloud passes through the point p(x0, y0, z0), and fit the non-component point cloud based on the least squares method to obtain the fitting plane: a(x-x0)+b(y-y0)+c(z-z0)=0, Among them, a, b and c are the normal vectors of the fitting plane; The fitting plane is transformed based on the relative position relationship between the component position in the component point cloud and the fitting plane to obtain the fitting plane corresponding to the component point cloud.

3. The method for scanning and splicing point clouds of steel pipe main arch ribs according to claim 1 is characterized in that: In S3, the normal vector of the fitting plane corresponding to the component point cloud is calculated, and the calculated normal vector is clustered to obtain the cylinder node cloud, which is specifically: Obtain the minimum eigenvector in the fitting plane corresponding to the component point cloud as the normal vector; Multiple parameters in the Euclidean clustering algorithm are set, including the minimum number of points in each cluster, the maximum number of points and the minimum Euclidean distance between two different clusters. The calculated normal vector is clustered using the Euclidean clustering algorithm and the set parameters to obtain multiple accessory point clouds on the component. The multiple accessory point clouds are removed to obtain the tube node cloud.

4. The method for scanning and splicing point clouds of steel pipe main arch ribs according to claim 1, characterized in that: In S4, the cylinder node cloud is subjected to two-dimensional plane projection processing to obtain a cylinder node cloud projection plane, which is specifically: The projection plane is fitted and calculated on the cylinder node cloud by the RANSAC algorithm to obtain the projection plane: Ax+By+Cz+D=0, Among them, A, B and C are the normal vector components corresponding to the projection plane, and D is the position parameter of the projection plane relative to the coordinate origin; The new coordinates of orthogonally projecting the cylinder node cloud onto the projection plane are calculated using a coordinate calculation formula, and the coordinate calculation formula is: xp=x×(B 2 +C 2 )-A×(y×B+z×C+D) / norm(|A,B,C|), yp=y×(A 2 +C 2 )-B×(x×A+z×C+D) / norm(|A,B,C|), zp=z×(A 2 +B 2 )-C×(x×A+y×B+D) / norm(|A,B,C|), Among them, xp, yp and zp are the new coordinates, norm(|A,B,C|) is the Euclidean length of the normal vector of the projection plane; Each new coordinate of the cylinder node cloud is projected onto the projection plane to obtain the cylinder node cloud projection plane.

5. The method for scanning and splicing point clouds of steel pipe main arch ribs according to claim 1, characterized in that: In S5, boundary points are calculated for the cylinder node cloud projection plane, the calculated boundary points are connected into a sideline, a sideline perpendicular is determined according to the sideline, a coordinate axis is established according to the sideline and the sideline perpendicular, and the cylinder segments in the cylinder node cloud projection plane are sliced ​​equidistantly according to the coordinate axis to obtain a plurality of cylinder segment point clouds, specifically: Assume that the reference point P0 (x0, y0) and the target point P1 (x1, y1) are two points on the projection plane of the cylinder node cloud, and the polar angle θ of the target point P1 relative to the reference point P0 is calculated by the polar angle calculation formula. The polar angle θ corresponding to the target point P1 is: θ=arctan2(y1-y0,x1-x0); Suppose the vector on the projection plane of the tube node cloud is are two vectors on the projection plane of the tube node cloud, and the vector is calculated by the cross product calculation formula and vector The two-dimensional vector cross product between is: In this way, the polar angle θ and the two-dimensional vector cross product corresponding to all point clouds on the projection plane of the tube node cloud are calculated; The convex hull vertex is identified by using the convex hull algorithm for the polar angle θ and the two-dimensional vector cross product corresponding to all point clouds, and the identified convex hull vertices are connected as boundary points to obtain the edge line L; The edge line perpendicular line L' is determined according to the edge line L, and the edge line L and the edge line perpendicular line L' are used as the X axis and Y axis of the coordinate axis respectively. The cylinder node cloud in the cylinder node cloud projection plane is sliced ​​equidistantly in the Y axis direction to obtain multiple cylinder segment point clouds.

6. The method for scanning and splicing point clouds of steel pipe main arch ribs according to claim 1, characterized in that: In S6, cylindrical surface fitting is performed on the plurality of cylinder segment point clouds to obtain a cylindrical surface corresponding to each cylinder segment point cloud, specifically: Let Q1(x1,y1,z2), Q2(x2,y2,z2) and Q3(x3,y3,z3) be three points on the cylindrical surface, and calculate the vector and vector For the vector and the vector Perform cross product calculation to obtain the vertical plane vector, and determine the axis direction of the cylinder based on the vertical plane vector; Assume that the center coordinates of the cylinder are (x0, y0) and the radius is r. According to the circle equation (x-x0)+(y-y0)2=r, the least squares method is used to fit the circle. Based on the error function E, the partial derivatives of the center coordinates and radius are calculated and the error function E is set to 0. The error function E equation group is solved to obtain the values ​​of the center coordinates and radius. The error function E equation group is:

7. The method for scanning and splicing point clouds of steel pipe main arch ribs according to claim 1, characterized in that: In S7, based on the fact that the cylindrical surfaces after fitting the point clouds of each corresponding cylinder segment are the side surfaces of the same cylinder, the rotation parameters and translation parameters for combining the cylindrical surfaces of all cylinder segments into one are calculated, specifically: Based on the cylindrical surface fitted by the point cloud of each corresponding cylinder segment as the side of the same cylinder, calculate the direction vector corresponding to the two cylindrical surfaces to be spliced and direction vector According to the direction vector and direction vector Calculate the rotation angle; According to the direction vector and direction vector Calculate the axis of rotation; Construct a rotation matrix K according to the rotation angle and the rotation axis, and construct an antisymmetric matrix of the rotation matrix K using the Rodriguez rotation formula to obtain a rotation matrix R, which is the rotation parameter; Calculate the translation vector required to move the center of the bottom of one cylinder to the center of the bottom of another cylinder based on the coordinates of the center of the bottom of two adjacent cylinders Is the translation parameter, the translation vector corresponding to the cylinder to be translated for: Among them, the coordinates of the center of the bottom surface of the reference cylinder are O0 (X0, Y0, Z0), and the coordinates of the center of the bottom surface of the cylinder to be translated are O1 (X1, Y1, Z1).

8. The method for scanning and splicing point clouds of steel pipe main arch ribs according to claim 6, characterized in that: In S7, all cylindrical surfaces are spliced ​​into point clouds according to the rotation parameters and the translation parameters to obtain the final component point cloud, which is specifically: Convert each point cloud coordinate in the cylindrical surface into the corresponding homogeneous coordinate (x, y, z, 1), and convert each homogeneous coordinate Multiply them by the rotation matrix R to get the point cloud after the coordinates of each point cloud are rotated. set up Construct the translation matrix T, construct the translation matrix, and rotate the point cloud coordinates of each point cloud Multiplying with the translation matrix T, we get the point cloud after rotation and translation, and get the final component point cloud based on the point cloud after rotation and translation.

9. The method for scanning and splicing point clouds of steel pipe main arch ribs according to claim 6, characterized in that: The direction vector and direction vector Calculate the rotation angle, specifically: The direction vector is calculated by the vector dot product formula and the vector modulus formula and direction vector The rotation angle of , the vector dot product formula is: The vector modulus formula is: in, Obtaining the angle through inverse trigonometric functions θ is the rotation angle.

10. A point cloud scanning and splicing device for a steel pipe main arch rib, characterized in that: include: A preprocessing module is used to scan the bridge steel components using multiple measuring stations of a preset ground three-dimensional laser scanner to obtain a point cloud of the main arch rib steel component, and to cut out a steel component point cloud area from the main arch rib steel component point cloud; A plane fitting module is used to filter the steel component point cloud area to obtain a non-component point cloud and a component point cloud, and perform plane fitting on the non-component point cloud to determine a fitting plane corresponding to the component point cloud through a fitting plane; A clustering processing module is used to calculate the normal vector of the fitting plane corresponding to the component point cloud, and to perform clustering processing on the calculated normal vector to obtain a cylinder node cloud; A projection module, used for performing two-dimensional plane projection processing on the cylinder node cloud to obtain a cylinder node cloud projection plane; A slicing module is used to calculate the boundary points of the cylinder node cloud projection plane, connect the calculated boundary points into a sideline, determine the sideline perpendicular line according to the sideline, establish a coordinate axis according to the sideline and the sideline perpendicular line, and perform equidistant slicing on the cylinder segment in the cylinder node cloud projection plane according to the coordinate axis to obtain a plurality of cylinder segment point clouds; A surface fitting module, used for performing cylindrical surface fitting processing on the plurality of cylinder segment point clouds to obtain a cylindrical surface corresponding to each cylinder segment point cloud; The point cloud splicing module is used to calculate the rotation parameters and translation parameters for splicing the cylindrical surfaces of all cylinder sections into one based on the cylindrical surface fitted after the point cloud of each corresponding cylinder segment is the side of the same cylinder, and splice the point clouds of all cylindrical surfaces according to the translation parameters and rotation parameters to obtain the final component point cloud.

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