Component deviation measurement method, device and system and storage medium

Abstract

The invention provides a component deviation measurement method, device and system and a storage medium, and belongs to the technical field of component detection, and the method comprises the steps: scanning a to-be-measured main arch rib steel pipe component through a three-dimensional laser scanner to obtain original point cloud data; filtering the original point cloud data to obtain filtered point cloud data; performing feature analysis on the filtered point cloud data to obtain a viewpoint feature histogram feature descriptor and a target feature point; performing registration analysis on the target feature point according to the feature descriptor of the viewpoint feature histogram to obtain target point cloud data; and performing deviation measurement on the target point cloud data through the component reference model to obtain a component deviation measurement result. According to the method, the stability and precision of registration are improved, the risk of operation in a potential dangerous construction area is effectively avoided, normal operation of construction production activities is ensured, any interference is avoided, and smooth progress of the construction progress is ensured.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of component detection, and in particular to a component deviation measurement method, device, system and storage medium. Background Art

[0002] In steel tube concrete arch bridges, the main arch rib steel tube is the main load-bearing component. Its main characteristics are that the surface shape is circular, the wall thickness of the steel tube is large, and the area and size are large. It is often used in situations with large spans such as across rivers and mountain canyons. Its span size ranges from 100m to 500m and its weight ranges from tens of tons to thousands of tons. In order to prevent the main arch rib steel tube components from affecting the quality of the project due to their own dimensional errors, traditional manual measurement methods are often used to measure the relevant dimensional points with a total station at the prefabrication site to obtain the component deviation results to ensure that the main arch rib steel tube meets the design requirements, but its work efficiency is low and it may also bring safety hazards, which is contrary to the principle of safe production operations.

[0003] Compared with traditional measurement methods, the method of measuring component deviation using point cloud data obtained by 3D laser scanners has been widely used in the industry. In the process of processing point cloud data, point cloud registration is the basis and necessary condition for measuring the complete point cloud data of components. The most classic point cloud registration algorithm is the ICP algorithm, but this algorithm has high requirements for the initial measurement position of the 3D laser scanner, otherwise it is easy to fall into the local optimal solution, resulting in matching failure, and ultimately resulting in low accuracy in component deviation measurement, with an accuracy of only 2cm-3cm, which obviously cannot meet the measurement requirements of the main arch rib steel pipe of the steel tube concrete arch bridge. The main arch rib steel pipe is measured at the prefabrication site, which is interfered by a large number of equipment and other steel pipes. Under normal circumstances, it is difficult to find the ideal initial measurement position, and more measuring stations can only be set up, and the point cloud data of multiple measuring stations are spliced. Therefore, relying on existing algorithms and technologies, the accuracy of 3D laser measurement of the main arch rib steel pipe of the steel tube concrete arch bridge at the prefabrication site does not meet the engineering requirements.

[0004] In view of this, it is urgent to develop a component deviation measurement method to improve the stability and accuracy of alignment. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a component deviation measurement method, device, system and storage medium in view of the deficiencies in the prior art.

[0006] The technical solution of the present invention to solve the above technical problem is as follows: A component deviation measurement method comprises the following steps:

[0007] The main arch rib steel pipe component to be measured is scanned by a 3D laser scanner to obtain multiple original point cloud data;

[0008] Performing filtering processing on all the original point cloud data to obtain a plurality of filtered point cloud data;

[0009] Performing feature analysis on all the filtered point cloud data to obtain a viewpoint feature histogram feature descriptor and a plurality of target feature points;

[0010] Performing registration analysis on all the target feature points according to the viewpoint feature histogram feature descriptor to obtain a plurality of target point cloud data;

[0011] A component reference model is imported, and deviation measurement is performed on all the target point cloud data using the component reference model to obtain a component deviation measurement result.

[0012] Another technical solution of the present invention to solve the above technical problem is as follows: a component deviation measuring device, comprising:

[0013] A scanning module is used to scan the main arch rib steel pipe component to be measured by a three-dimensional laser scanner to obtain multiple original point cloud data;

[0014] A filtering module, used for filtering all the original point cloud data to obtain a plurality of filtered point cloud data;

[0015] A feature analysis module, used to perform feature analysis on all the filtered point cloud data to obtain a viewpoint feature histogram feature descriptor and multiple target feature points;

[0016] A registration analysis module, used for performing registration analysis on all the target feature points according to the viewpoint feature histogram feature descriptor to obtain a plurality of target point cloud data;

[0017] The measurement result acquisition module is used to import a component reference model, perform deviation measurement on all the target point cloud data through the component reference model, and obtain component deviation measurement results.

[0018] Based on the above-mentioned component deviation measurement method, the present invention also provides a component deviation measurement system.

[0019] Another technical solution of the present invention to solve the above technical problem is as follows: a component deviation measurement system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the component deviation measurement method described above is implemented.

[0020] Based on the above-mentioned component deviation measurement method, the present invention also provides a computer-readable storage medium.

[0021] Another technical solution of the present invention to solve the above technical problem is as follows: a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the component deviation measurement method as described above is implemented.

[0022] The beneficial effects of the present invention are as follows: original point cloud data is obtained by scanning the main arch rib steel pipe component to be measured with a three-dimensional laser scanner, filtered point cloud data is obtained by filtering the original point cloud data, feature analysis of the filtered point cloud data is obtained to obtain a viewpoint feature histogram feature descriptor and a target feature point, target point cloud data is obtained by alignment analysis of the target feature point according to the viewpoint feature histogram feature descriptor, component deviation measurement results are obtained by measuring the deviation of the target point cloud data with a component reference model, the stability and accuracy of the alignment are improved, the risk of operations in potentially dangerous construction areas is effectively avoided, the normal progress of construction production activities is ensured, any interference is avoided, the smooth progress of the construction progress is guaranteed, the efficiency of data acquisition, the accuracy of data and the safety of measurement operations are improved, and at the same time, the dimensional measurement outside the dangerous area is realized, so that the safety of the operators is guaranteed while the dimensional accuracy of the measurement and the deviation of the main arch rib steel pipe component are calculated. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic diagram of a flow chart of a component deviation measurement method provided in an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of deviation analysis of a component deviation measurement method provided in an embodiment of the present invention;

[0025] Figure 3 A module block diagram of a component deviation measuring device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] 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.

[0027] Figure 1 A schematic flow chart of a component deviation measurement method provided in an embodiment of the present invention.

[0028] like Figure 1 As shown, a component deviation measurement method includes the following steps:

[0029] The main arch rib steel pipe component to be measured is scanned by a 3D laser scanner to obtain multiple original point cloud data;

[0030] Performing filtering processing on all the original point cloud data to obtain a plurality of filtered point cloud data;

[0031] Performing feature analysis on all the filtered point cloud data to obtain a viewpoint feature histogram feature descriptor and a plurality of target feature points;

[0032] Performing registration analysis on all the target feature points according to the viewpoint feature histogram feature descriptor to obtain a plurality of target point cloud data;

[0033] A component reference model is imported, and deviation measurement is performed on all the target point cloud data using the component reference model to obtain a component deviation measurement result.

[0034] It should be understood that the scanning point cloud data P and Q (ie, original point cloud data) of the main arch rib steel pipe member to be measured are obtained by using the front and rear stations of the three-dimensional laser scanner respectively.

[0035] It should be understood that the scanned point cloud component (ie, the original point cloud data) is preprocessed by the nearest point voxel filtering algorithm to obtain the point cloud data p, q (ie, the filtered point cloud data).

[0036] Specifically, the aligned point cloud data of the main arch rib steel tube component (i.e., the target point cloud data) and the CAD design reference model (i.e., the component reference model) are loaded into the Geomagic software together, and the point cloud scanning data of the main arch rib steel tube component (i.e., the target point cloud data) and the CAD design reference model (i.e., the component reference model) are analyzed and compared to obtain a dimension deviation analysis report (i.e., the component deviation measurement result).

[0037] Specifically, the specific process of using a 3D laser scanner to obtain point cloud data is as follows:

[0038] Clean the surface of the main arch rib steel pipe component to avoid objects blocking the main arch rib steel pipe component and the 3D laser scanner; because the material of the main arch rib steel pipe component is affected by the temperature environment deformation, the 3D laser scanner chooses to operate at night and early morning to ensure that the environment is 20°C (the allowable temperature difference is within ±2°); determine the relationship between several scanning stations and their positions according to the shape and height of the main arch rib steel pipe component to ensure that the scanning range of the 3D laser scanner at several scanning stations can cover the main arch rib steel pipe component; adjust the corresponding parameters of the 3D laser scanner according to the working environment of the 3D laser scanner; according to the adjustment of the parameters, the 3D laser scanner scans the main arch rib steel pipe component at each scanning station to obtain the point cloud scanning data P and Q (i.e., original point cloud data) of the main arch rib steel pipe component.

[0039] It should be understood that the component reference model can be obtained through SolidWorks software.

[0040] In the above embodiment, the original point cloud data is obtained by scanning the main arch rib steel pipe component to be measured by a three-dimensional laser scanner, the original point cloud data is filtered to obtain filtered point cloud data, the feature analysis of the filtered point cloud data is obtained to obtain the viewpoint feature histogram feature descriptor and the target feature point, the target feature point cloud data is obtained by the registration analysis of the target feature point according to the viewpoint feature histogram feature descriptor, and the component deviation measurement result is obtained by measuring the deviation of the target point cloud data through the component reference model, thereby improving the stability and accuracy of the registration, effectively avoiding the risk of operations in potentially dangerous construction areas, ensuring the normal progress of construction and production activities, avoiding any interference, ensuring the smooth progress of the construction progress, improving the efficiency of data acquisition, data accuracy and the safety of measurement operations, and at the same time, enabling operators to perform dimensional measurements outside dangerous areas, while ensuring the measurement dimensional accuracy and calculating the deviation of the main arch rib steel pipe component, ensuring the safety of operators.

[0041] Optionally, as an embodiment of the present invention, the process of performing feature analysis on all the filtered point cloud data to obtain a viewpoint feature histogram feature descriptor and a plurality of target feature points includes:

[0042] Performing feature point analysis on all the filtered point cloud data to obtain multiple target feature points;

[0043] The feature descriptors of all the target feature points are analyzed to obtain a viewpoint feature histogram feature descriptor.

[0044] In the above embodiment, feature analysis is performed on all filtered point cloud data to obtain viewpoint feature histogram feature descriptors and multiple target feature points, which improves the stability and accuracy of the alignment, effectively avoids the risks of operations in potentially dangerous construction areas, ensures the normal progress of construction and production activities, avoids any interference, ensures the smooth progress of construction, and improves data acquisition efficiency, data accuracy and the safety of measurement operations.

[0045] Optionally, as an embodiment of the present invention, the process of analyzing feature points of all the filtered point cloud data to obtain multiple target feature points includes:

[0046] Performing local feature analysis on all the filtered point cloud data to obtain a plurality of first initial feature points;

[0047] The weighted vector sine value is analyzed for all the filtered point cloud data, and the analysis result is used as the second initial feature point set, and the feature points in the first initial feature point set and the second initial feature point set are used as target feature points, thereby obtaining multiple target feature points.

[0048] It should be understood that the feature points of the point cloud of part of the main arch rib steel pipe component (ie, the first initial feature points) are extracted.

[0049] Specifically, for the area where the surface of the main arch rib steel tube component changes gently, the points (i.e., the second initial feature points) calculated using the sine values ​​of the weighted vectors at different scales are combined with the obtained feature points (i.e., the first initial feature points) as the final feature points (i.e., the target feature points).

[0050] In the above embodiment, feature point analysis is performed on all the filtered point cloud data to obtain multiple target feature points, which improves the stability and accuracy of the alignment, effectively avoids the risks of operations in potentially dangerous construction areas, ensures the normal progress of construction and production activities, avoids any interference, ensures the smooth progress of the construction progress, and improves data acquisition efficiency, data accuracy and the safety of measurement operations.

[0051] Optionally, as an embodiment of the present invention, the process of performing local feature analysis on all the filtered point cloud data to obtain a plurality of first initial feature points includes:

[0052] Performing neighborhood searches on each of the filtered point cloud data according to a K-dimensional tree algorithm and a preset first radius to obtain a plurality of first neighborhood points corresponding to each of the filtered point cloud data;

[0053] The first point cloud weights are calculated for each of the filtered point cloud data and a plurality of first neighborhood points corresponding to each of the filtered point cloud data by using the first formula to obtain a plurality of first point cloud weights corresponding to each of the filtered point cloud data. The first formula is:

[0054]

[0055] Among them, w1 ij is the first point cloud weight corresponding to the jth first neighborhood point of the i-th filtered point cloud data, p i is the i-th filtered point cloud data, is the jth first neighborhood point corresponding to the i-th filtered point cloud data;

[0056] The covariance matrix of the preset first radius, each of the filtered point cloud data, a plurality of first neighborhood points corresponding to each of the filtered point cloud data, and a plurality of first point cloud weights corresponding to each of the filtered point cloud data is calculated by the second formula to obtain a covariance matrix corresponding to each of the filtered point cloud data. The second formula is:

[0057]

[0058] Among them, C iis the covariance matrix corresponding to the i-th filtered point cloud data, r iss is the preset first radius, w1 ij is the first point cloud weight corresponding to the jth first neighborhood point of the i-th filtered point cloud data, p i is the i-th filtered point cloud data, is the first neighborhood point corresponding to the i-th filtered point cloud data;

[0059] Performing eigenvalue decomposition on each of the covariance matrices to obtain a first initial eigenvalue corresponding to each filtered point cloud data, a second initial eigenvalue corresponding to each filtered point cloud data, and a third initial eigenvalue corresponding to each filtered point cloud data;

[0060] The first initial eigenvalues, the second initial eigenvalues ​​corresponding to the filtered point cloud data, and the third initial eigenvalues ​​corresponding to the filtered point cloud data are sorted in ascending order to obtain the first sorted eigenvalues ​​corresponding to the filtered point cloud data, the second sorted eigenvalues ​​corresponding to the filtered point cloud data, and the third sorted eigenvalues ​​corresponding to the filtered point cloud data;

[0061] When the first sorted eigenvalue, the second sorted eigenvalue, and the third sorted eigenvalue satisfy the third formula and the fourth formula, the filtered point cloud data corresponding to the first sorted eigenvalue is used as the first initial feature point, thereby obtaining multiple first initial feature points. The third formula is:

[0062]

[0063] Among them, λ i2 is the second sorted eigenvalue corresponding to the i-th filtered point cloud data, λ i3 is the third sorted eigenvalue corresponding to the i-th filtered point cloud data, and ε1 is the preset first characteristic threshold;

[0064] The fourth formula is:

[0065]

[0066] Among them, λ i1 is the first sorted eigenvalue corresponding to the i-th filtered point cloud data, λ i2 is the second sorted eigenvalue corresponding to the i-th filtered point cloud data, and ε2 is the preset second characteristic threshold.

[0067] It should be understood that for a point pi in a given point cloud data P (i.e., the filtered point cloud data), the KD-tree algorithm is used to perform a neighborhood search to obtain the radius r of point pi: iss The point set p in the neighborhood spacej (i.e. multiple first neighboring points).

[0068] It should be understood that the K-dimensional tree algorithm (KD-Tree algorithm) is a data structure for organizing points in K-dimensional space, which is particularly suitable for efficient search of multidimensional data. The KD tree recursively divides the hyperplane in the K-dimensional space, divides the space into two parts, and forms a binary tree structure, where each node represents a point in the K-dimensional space. This data structure can significantly improve the efficiency of searching in multidimensional space, and is particularly suitable for operations such as range search and nearest neighbor search. By constructing a balanced KD tree, a specific point or a group of points in the space can be quickly located, which is widely used in many fields such as computer vision, robot navigation, and database retrieval.

[0069] Specifically, the Euclidean distance between the query point pi (i.e., the filtered point cloud data) and each point in the neighborhood (i.e., multiple first neighborhood points) is calculated, and the weight w is calculated. ij (i.e. the weight of the first point cloud), the specific formula is as follows:

[0070]

[0071] Calculate the covariance matrix C between the query point pi (i.e., the filtered point cloud data) and all points in the neighborhood (i.e., multiple first neighborhood points). The specific formula is as follows:

[0072]

[0073] Perform eigenvalue decomposition on the covariance matrix C to obtain {λ1, λ2, λ3}, satisfying λ1<λ2<λ3.

[0074] Preferably, the thresholds ε1=0.985 and ε2=0.985 are set.

[0075] Specifically, a threshold ε1 (i.e., a preset first feature threshold) and ε2 (i.e., a preset second feature threshold) are set, and the point pi is considered to be a feature point (i.e., a first initial feature point) if the following formula is satisfied. The specific formula is as follows:

[0076]

[0077] In the above embodiment, local feature analysis is performed on all filtered point cloud data to obtain multiple first initial feature points, which improves the stability and accuracy of the alignment, effectively avoids the risks of operations in potentially dangerous construction areas, ensures the normal progress of construction and production activities, avoids any interference, ensures the smooth progress of construction, and improves data acquisition efficiency, data accuracy and the safety of measurement operations.

[0078] Optionally, as an embodiment of the present invention, the process of analyzing the weighted vector sine value of all the filtered point cloud data and using the analysis result as the second initial feature point set includes:

[0079] Performing neighborhood searches on each of the filtered point cloud data according to a K-dimensional tree algorithm and a preset second radius to obtain a plurality of second neighborhood points corresponding to each of the filtered point cloud data;

[0080] Performing neighborhood searches on each of the filtered point cloud data according to a K-dimensional tree algorithm and a preset third radius to obtain a plurality of third neighborhood points corresponding to each of the filtered point cloud data;

[0081] Vector calculations are performed on each of the filtered point cloud data, a plurality of second neighborhood points corresponding to each of the filtered point cloud data, and a plurality of third neighborhood points corresponding to each of the filtered point cloud data by using a first set of equations to obtain a plurality of first neighborhood point vectors corresponding to each of the filtered point cloud data and a plurality of second neighborhood point vectors corresponding to each of the filtered point cloud data. The first set of equations is:

[0082]

[0083] in, is the first neighborhood point vector corresponding to the ath second neighborhood point of the i-th filtered point cloud data, pi is the i-th filtered point cloud data, is the ath second neighborhood point corresponding to the i-th filtered point cloud data, is the second neighborhood point vector corresponding to the bth third neighborhood point of the i-th filtered point cloud data, is the bth third neighborhood point corresponding to the i-th filtered point cloud data;

[0084] The second point cloud weights are calculated for each of the plurality of first neighborhood point vectors corresponding to each of the filtered point cloud data by the fifth formula to obtain a plurality of second point cloud weights corresponding to each of the filtered point cloud data. The fifth formula is:

[0085]

[0086] Among them, w2 ia is the second point cloud weight corresponding to the a-th second neighborhood point of the i-th filtered point cloud data, is the first neighborhood point vector corresponding to the ath second neighborhood point of the i-th filtered point cloud data;

[0087] The third point cloud weights are calculated for each of the plurality of second neighborhood point vectors corresponding to each of the filtered point cloud data by the sixth formula to obtain a plurality of third point cloud weights corresponding to each of the filtered point cloud data. The sixth formula is:

[0088]

[0089] Among them, w3 ib is the third point cloud weight corresponding to the bth third neighborhood point of the i-th filtered point cloud data, is the second neighborhood point vector corresponding to the bth third neighborhood point of the i-th filtered point cloud data;

[0090] The neighborhood angles are calculated for the multiple first neighborhood point vectors corresponding to each of the filtered point cloud data, the multiple second neighborhood point vectors corresponding to each of the filtered point cloud data, the multiple second point cloud weights corresponding to each of the filtered point cloud data, and the multiple third point cloud weights corresponding to each of the filtered point cloud data through the seventh formula to obtain the initial neighborhood angles corresponding to each of the filtered point cloud data. The seventh formula is:

[0091]

[0092] Among them, θ i is the initial neighborhood angle corresponding to the i-th filtered point cloud data, is the first neighborhood point vector corresponding to the ath second neighborhood point of the i-th filtered point cloud data, is the second neighborhood point vector corresponding to the bth third neighborhood point of the i-th filtered point cloud data, w2 ia is the second point cloud weight corresponding to the ath second neighborhood point of the i-th filtered point cloud data, w3 ib is the third point cloud weight corresponding to the bth third neighborhood point of the i-th filtered point cloud data;

[0093] Sorting all the initial neighborhood angles in descending order to obtain sorted neighborhood angles corresponding to each of the filtered point cloud data;

[0094] Filtering out the sorted neighborhood angles that meet the screening condition from all the sorted neighborhood angles, and obtaining a plurality of filtered neighborhood angles after screening, wherein the screening condition is that the sorted neighborhood angle is less than a preset angle threshold;

[0095] The filtered point cloud data corresponding to the first A filtered neighborhood angles are used as second initial feature points, thereby obtaining a plurality of second initial feature points, and all of the second initial feature points are combined to obtain a second initial feature point set.

[0096] Preferably, the preset second radius may be 0.05, the preset third radius may be 0.1, and the preset angle threshold may be 5 degrees.

[0097] It should be understood that for each point in p (i.e., the point cloud data after filtering), different radius neighborhoods r1 (i.e., the preset second radius) and r2 (i.e., the preset third radius) are set; for a point pi in p (i.e., the point cloud data after filtering), the point set p1x (i.e., multiple second neighborhood points) in the neighborhood space of radius r1 of point pi and the point set p2y (i.e., multiple third neighborhood points) in the neighborhood space of radius r2 are obtained through neighborhood search.

[0098] Specifically, we obtain the center point pi to the point set p 1i 、p 2i The vector of (i.e. the first neighborhood point vector and the second neighborhood point vector) is as follows:

[0099]

[0100] Calculate the sum of the vectors of all points in different neighborhoods respectively, and calculate the weight function (i.e. the weight of the second point cloud and the weight of the third point cloud). The specific formula is as follows:

[0101]

[0102] The cosine value of the weighted vector angle is calculated using the dot product of the vector. The specific formula is as follows:

[0103]

[0104] In the above formula, θ is the angle between the weighted points in different neighborhoods and the center point pi (i.e., the initial neighborhood angle);

[0105] Using the sine-cosine conversion relationship, the sine value of the angle can be obtained using the cosine value of the angle. The specific formula is as follows:

[0106]

[0107] According to the above steps, the KD-Tree algorithm is used to traverse and calculate the weighted vector sine value of each point p, and arrange them from large to small, set the angle threshold θ (that is, the preset angle threshold), and the percentage threshold ε0, and select the points that are less than the angle threshold θ and ranked high as feature points (that is, the second initial feature points).

[0108] It should be understood that taking the filtered point cloud data corresponding to the first A filtered neighborhood angles as the second initial feature point means taking the filtered point cloud data corresponding to the first A ranked filtered neighborhood angles as the second initial feature point.

[0109] It should be understood that the angle threshold θ=5° and the percentage threshold ε0=30% are set, and points that are smaller than the angle threshold θ and ranked high are selected as feature points (ie, the second initial feature points).

[0110] In the above embodiment, the weighted vector sine value analysis is performed on all filtered point cloud data, and the analysis results are used as the second initial feature point set, which ensures the normal progress of construction and production activities, avoids any interference, ensures the smooth progress of construction, improves data acquisition efficiency, data accuracy and the safety of measurement operations, and at the same time, enables operators to perform dimensional measurements outside dangerous areas, thereby ensuring the safety of operators while ensuring the measurement dimensional accuracy and calculating the deviation of the main arch rib steel pipe components.

[0111] Optionally, as an embodiment of the present invention, the process of analyzing feature descriptors of all the target feature points to obtain viewpoint feature histogram feature descriptors includes:

[0112] Using a k-nearest neighbor algorithm to perform neighborhood searches on each of the target feature points, to obtain a plurality of fourth neighborhood points corresponding to each of the target feature points;

[0113] Using a principal component analysis algorithm, normal vectors are calculated for a plurality of fourth neighborhood points corresponding to each of the target feature points to obtain normal vectors corresponding to each of the target feature points;

[0114] The center point of all the target feature points is calculated by the seventh formula to obtain the center point The seventh formula is:

[0115]

[0116] in, is the X-axis coordinate of the center point, is the Y-axis coordinate of the center point, is the Z-axis coordinate of the center point, N is the total number of target feature points, x k is the X-axis coordinate of the kth target feature point, y k is the Y-axis coordinate of the kth target feature point, z k is the Z-axis coordinate of the kth target feature point;

[0117] The center point angle corresponding to each of the target feature points is calculated by the eighth formula respectively for the center point, each of the target feature points, and the normal vector corresponding to each of the target feature points to obtain the center point angle corresponding to each of the target feature points. The eighth formula is:

[0118]

[0119] Among them, α k is the center point angle corresponding to the kth target feature point, o is the center point, p4 k is the kth target feature point, n k is the normal vector corresponding to the kth target feature point;

[0120] The normal vector angles corresponding to each of the target feature points and the normal vector corresponding to any adjacent target feature point are calculated by the ninth formula to obtain a plurality of normal vector angles corresponding to each of the target feature points. The ninth formula is:

[0121]

[0122] Among them, β ku is the normal vector angle between the kth target feature point and the uth target feature point, n k is the normal vector corresponding to the kth target feature point, n u is the normal vector corresponding to the u-th target feature point, arccos() is the inverse cosine function;

[0123] Performing a viewpoint feature histogram analysis on all the center point angles and all the normal vector angles to obtain a viewpoint feature histogram;

[0124] Vectorization is performed on the viewpoint feature histogram to obtain a viewpoint feature histogram feature descriptor.

[0125] It should be understood that, according to the final feature point (ie, the target feature point), the viewpoint feature histogram feature descriptor is obtained by utilizing the relationship between the normal vector, the point, and the centroid.

[0126] It should be understood that the viewpoint feature histogram feature descriptor, i.e., the VFH (Viewpoint Feature Histogram) feature descriptor, is a descriptor for point cloud data, which is mainly used for point cloud clustering recognition and six-degree-of-freedom pose estimation problems. The VFH feature descriptor is developed based on the FPFH (Fast Point Feature Histograms) descriptor. It generates a global feature vector by calculating the normal vector distribution of each point in the point cloud to describe the shape and geometric characteristics of the entire point cloud. VFH is rotationally invariant and robust to noise, so it is widely used in tasks such as three-dimensional target recognition, classification, and clustering.

[0127] Specifically, the K-Nearest Neighbors (KNN) algorithm is a distance-based classification and regression method. Its core idea is: in a labeled data set, for a new instance, find the K training instances closest to it based on the distance metric, and then predict the label of the new instance based on the information of these K neighbors. In classification problems, the most common approach is to use the majority voting method, that is, the category that appears most frequently among the K nearest neighbors will be assigned to the new instance. In regression problems, the average of the target values ​​of the K nearest neighbors is usually calculated as the prediction result. The KNN algorithm can be used for both classification and regression problems.

[0128] It should be understood that the principal component analysis algorithm (PCA) is a statistical dimensionality reduction technique that converts a set of variables that may be correlated into a set of linearly uncorrelated variables through orthogonal transformation. These new variables are called principal components. The purpose of PCA is to reduce the dimension of the data and simplify the model while retaining the original data information as much as possible.

[0129] Specifically, assuming that there are N feature points (i.e., target feature points), for each feature point indexed (i.e., target feature point), the kNN algorithm is used to find its k nearest neighboring points according to the K-dimensional tree principle. These points constitute a local neighborhood (i.e., multiple fourth neighborhood points). The normal vector n of each feature point is calculated using the PCA algorithm. i (n xi , n yi , n zi ).

[0130] Specifically, the x, y, and z coordinates of each point are summed up to find the centroid of the entire point cloud. (i.e. the center point), the specific formula is as follows:

[0131]

[0132] Calculate the normal vector n for each point i The angles between the centroid and the line connecting them (i.e., the center point angle) are respectively set to a feature point p (i.e., the target feature point). The specific formula is as follows:

[0133]

[0134] Calculate the angle between adjacent normal vectors (i.e. normal vector angle), assuming that the normal vector of another feature point is v, the specific formula is as follows:

[0135]

[0136] A VFH feature descriptor histogram (ie, viewpoint feature histogram feature descriptor) is generated according to the above-mentioned angles α and φ.

[0137] It should be understood that the vector form of the histogram H (ie, the viewpoint feature histogram) is the viewpoint feature histogram feature descriptor.

[0138] In the above embodiment, feature descriptors of all target feature points are analyzed to obtain viewpoint feature histogram feature descriptors, which enables operators to perform dimensional measurements outside the danger zone. While ensuring the accuracy of dimensional measurements and calculation of deviations of the main arch rib steel pipe components, the safety of operators is ensured.

[0139] Optionally, as an embodiment of the present invention, the process of analyzing a viewpoint feature histogram of all the center point angles and all the normal vector angles to obtain a viewpoint feature histogram includes:

[0140] Filter out the minimum value from all the center point angles, and obtain the minimum center point angle after filtering; filter out the maximum value from all the center point angles, and obtain the maximum center point angle after filtering; filter out the minimum value from all the normal vector angles, and obtain the minimum normal vector angle after filtering; filter out the maximum value from all the normal vector angles, and obtain the maximum normal vector angle after filtering;

[0141] The center point angle interval is obtained by taking the minimum center point angle as the minimum value and the maximum center point angle as the maximum value;

[0142] The normal vector angle interval is obtained by taking the minimum normal vector angle as the minimum value and the maximum normal vector angle as the maximum value;

[0143] Dividing the center point angle interval into a plurality of center point angle sub-intervals with equal distances;

[0144] Counting the total number of all the center point angle sub-intervals to obtain the total number of center point angle sub-intervals; dividing the normal vector angle interval into a plurality of normal vector angle sub-intervals with equal distances;

[0145] Counting the total number of all the normal vector angle sub-intervals to obtain the total number of normal vector angle sub-intervals;

[0146] The frequency of the minimum center point angle, the maximum center point angle, the total number of center point angle sub-intervals, and the multiple center point angles in each center point angle sub-interval is calculated by the tenth formula to obtain the center point angle frequency corresponding to each center point angle sub-interval. The tenth formula is:

[0147]

[0148] in,

[0149] Among them, H α (j′) is the frequency of the center point angle corresponding to the j′th center point angle subinterval, N is the total number of target feature points, is the indicator function, α min is the minimum center point angle, α max is the maximum center point angle, Δα is the center point angle interval width, α k is the center point angle corresponding to the kth target feature point, m′ is the total number of center point angle subintervals, and j′ is the j′th center point angle subinterval;

[0150] The frequency of the normal vector angle corresponding to each normal vector angle subinterval is obtained by calculating the minimum normal vector angle, the maximum normal vector angle, the total number of normal vector angle subintervals, and the multiple normal vector angles in each normal vector angle subinterval using the eleventh formula, and the eleventh formula is:

[0151]

[0152] in,

[0153] Among them, H β (k′) is the frequency of the normal vector angle corresponding to the k′th normal vector angle subinterval, N is the total number of target feature points, is the indicator function, adj(k) is the set of target feature points adjacent to the kth target feature point, β min is the minimum normal vector angle, Δβ is the normal vector angle interval width, β ku is the normal vector angle between the kth target feature point and the uth target feature point, k′ is the k′th normal vector angle subinterval, β max is the maximum normal vector angle, n′ is the total number of normal vector angle subintervals;

[0154] The viewpoint feature histogram is calculated for all the center point angle frequencies and all the normal vector angle frequencies through the twelfth formula to obtain the viewpoint feature histogram. The twelfth formula is:

[0155] H=|H α (1), H α (2), ..., H α (j′), H β (1), H β (2), ..., H β (k′)|,

[0156] Among them, H is the viewpoint feature histogram, H α (j′) is the frequency of the center point angle corresponding to the j′th center point angle subinterval, Hβ (k′) is the normal vector angle frequency corresponding to the k′th normal vector angle subinterval.

[0157] It should be understood that the histogram dimension for constructing the VFH feature descriptor and the way to divide the angle into intervals are first determined. Assuming that the final VFH histogram is divided into M dimensions, the value ranges of the angle α and the angle β are equally spaced into m and n intervals (m+n=M) (i.e., the center point angle sub-interval and the normal vector angle sub-interval).

[0158] Specifically, for α (i.e., the center point angle), let its value range be [α max , α min ]; for β (i.e. the normal vector angle), let its value range be [β max , β min ] Among them, for the angle α (i.e. the center point angle), the width of each interval is Δα (i.e. the center point angle interval width), and for the angle β (i.e. the normal vector angle), the width of each interval is Δβ (i.e. the normal vector angle interval width). The specific formula is as follows:

[0159]

[0160] Then, the frequency of the angle falling into each interval is counted, which is specifically divided into angle statistics for the angle α connected to the center of mass and angle statistics for the angle β between adjacent normal vectors.

[0161] Specifically, the angle statistics method for the angle α with the centroid (i.e., the frequency of the center point angle) is as follows: traverse all feature points p in the point cloud i (i=1, 2, ..., N, N is the total number of feature points) (i.e., target feature points), for each feature point, calculate the angle α between its normal vector and the line connecting the centroid i (i.e. the center point angle). Then determine α i (i.e., the center point angle) falls into which of the m divided intervals (i.e., the center point angle sub-intervals), let H α (j) represents the number (frequency) of feature points whose angle α falls into the jth interval (j=1, 2, ..., m) (i.e., the frequency of the center point angle). The statistical formula can be specifically expressed as:

[0162]

[0163] Specifically, the angle β between adjacent normal vectors (i.e., the angle frequency of normal vectors) is calculated as follows: ij (represents the normal vector angle between the i-th and j-th adjacent feature points) (i.e. the normal vector angle), judge β ij(i.e. normal vector angle) falls into which of the n β intervals (i.e. normal vector angle sub-intervals) that have been divided. Let H β (k) represents the frequency of the angle β falling into the kth interval (k=1, 2, ..., n) (i.e., the frequency of the normal vector angle), and the statistical formula can be specifically expressed as:

[0164]

[0165] Wherein, adj(i) represents the set of feature points adjacent to the i-th feature point (ie, the target feature point set).

[0166] It should be understood that the m interval frequencies H of the angle α obtained by statistics are α (j) (i.e., the frequency of the center angle) and the frequency of the n intervals about the angle β H β (k) (i.e., the frequency of the normal vector angle) together constitute the final VFH feature descriptor histogram H (i.e., the viewpoint feature histogram), which can be expressed as an M-dimensional vector (M = m + n). The specific formula can be expressed as:

[0167] H=[H α (1), H α (2), ..., H α (m), H β (1), H β (2), ..., H β (n)].

[0168] In the above embodiment, the viewpoint feature histogram is obtained by analyzing the viewpoint feature histogram of all center point angles and all normal vector angles, which enables operators to perform dimensional measurements outside the danger zone, thereby ensuring the safety of operators while ensuring the measurement accuracy of the dimensions and calculating the deviation of the main arch rib steel pipe components.

[0169] Optionally, as an embodiment of the present invention, the process of performing registration analysis on all the target feature points according to the viewpoint feature histogram feature descriptor to obtain a plurality of target point cloud data includes:

[0170] Performing rough registration processing on all the target feature points according to the NDT registration algorithm and the viewpoint feature histogram feature descriptor to obtain a plurality of rough registered point cloud data;

[0171] The ICP fine registration algorithm is used to perform fine registration processing on all the roughly registered point cloud data to obtain multiple target point cloud data.

[0172] It should be understood that the point cloud data of the complete main arch rib steel pipe component (ie, the target point cloud data) is obtained by using the least squares optimized point-to-surface ICP precise registration.

[0173] Specifically, the NDT registration algorithm mainly represents the distribution properties of the point cloud data set through a probability density function, and optimizes it through the Newton iterative optimization algorithm to minimize its probability distribution, and then obtains the transformation matrix between the two point clouds. The target point cloud is divided into a grid, and at least 5 points are set in the grid to achieve point cloud grid division. Through the above method, the transformation parameter T is calculated, and when the maximum number of iterations is reached, the transformation parameter T corresponding to the minimum error is selected.

[0174] It should be understood that the precise registration algorithm of the present invention mainly adopts the ICP precise registration algorithm of point to surface optimized by the least squares method, and the specific process is as follows:

[0175] From the target point cloud q i Search source point cloud p i , forming corresponding point pairs;

[0176] The error function value is as follows:

[0177] f(R, T) = ∑ i ((Rp i +Tq i )·n i ) 2 ,

[0178] Where R is the rotation matrix, T is the translation parameter, and p i is the source point cloud, q i is the target point cloud, n i It is the projection distance from the source point to the normal vector of the target point after transformation. The rotation and displacement are updated using the least squares method. The iteration is stopped when the number of iterations reaches the set maximum number of iterations or the registration error is less than the error function value.

[0179] It should be understood that the present invention uses the least squares optimized point-to-surface ICP fine registration on the basis of the rough registration to obtain the optimal rigid transformation matrix, and uses the matrix singular value decomposition method to calculate the transformation matrix, and calculates f(R, T) = ∑((Rp i +Tq i )·n i ) 2 The minimum transformation parameter T completes the point cloud transformation and achieves precise registration.

[0180] In the above embodiment, all target feature points are registered and analyzed according to the viewpoint feature histogram feature descriptor to obtain multiple target point cloud data, which ensures the normal progress of construction and production activities, avoids any interference, ensures the smooth progress of construction, improves data acquisition efficiency, data accuracy and the safety of measurement operations, and at the same time, enables operators to perform dimensional measurements outside dangerous areas, ensuring the safety of operators while ensuring the measurement accuracy of dimensions and calculating the deviation of the main arch rib steel pipe components.

[0181] Optionally, as another embodiment of the present invention, the method of the present invention includes: using a three-dimensional laser scanner to obtain point cloud data of the main arch rib steel pipe component; preprocessing its point cloud component data; splicing the point cloud scanning model data using a related registration algorithm, converting it into a PCD format point cloud, and importing it into Geomagic software together with the CAD model file output in STL format; comparing the design model and the scanned model to generate an analysis and comparison report. This method can enable operators to perform dimensional measurements outside the dangerous area, while ensuring the measurement dimensional accuracy and calculating the deviation of the main arch rib steel pipe component, and ensuring the safety of the operators.

[0182] Optionally, as another embodiment of the present invention, the present invention directly acquires point cloud data through a three-dimensional laser scanner and performs 3D comparative analysis with the CAD model, so that operators can perform dimensional measurements outside the danger zone, thereby ensuring the safety of operators while ensuring the measurement accuracy of the dimensions and calculating the deviation of the main arch rib steel pipe components.

[0183] Optionally, as another embodiment of the present invention, Figure 2 As shown, the specific process of analyzing and comparing the point cloud scanning data of the main arch rib steel pipe component and the CAD design reference model to obtain the shape dimension deviation analysis report is as follows:

[0184] The point cloud data of the main arch rib steel tube component was set as a control model in Geomagic Control software; the SolidWorks model of the main arch rib steel tube component was set as a design model in Geomagic Control software; the design model and the scanned model were adjusted to the same position and scale, and automatic position fitting was performed according to the prominent features of the main arch rib steel tube component; the scanned model of the main arch rib steel tube component and the SolidWorks design model were fitted and compared to obtain the component's outer dimensions, deviation heat map and comparative deviation analysis results.

[0185] The above comparative analysis includes the following sub-steps:

[0186] Deviation parameters were set in the 3D analysis function of Geomagic Control software, and critical values ​​and nominal values ​​were set in the color spectrum column; the deviation of the main arch rib steel pipe components was calculated using the comparative analysis function of Geomagic Control software to obtain a deviation heat map; the model comparative analysis report was automatically generated using Geomagic Control software, and the comparative analysis report included but was not limited to the overall size and installation accuracy of the main arch rib steel pipe components. By generating a comparative analysis report, construction and design problems were discovered, and locations that did not meet construction standards were marked in a timely manner, that is, the deviations of the main arch rib steel pipe component point cloud data and the SolidWorks model were marked to obtain the final analysis results.

[0187] Specifically, Figure 2 As shown in the figure, the registered point cloud data of the main arch rib steel pipe component and the CAD design reference model are loaded into the Geomagic software together, and the point cloud scanning data of the main arch rib steel pipe component and the CAD design reference model are analyzed and compared to obtain the shape dimension analysis report. Specifically:

[0188] Load the registered point cloud data of the main arch rib steel pipe component and the CAD design reference model into the Geomagic software. Save the processed data of the spliced ​​and registered point cloud data of the main arch rib steel pipe component as a pcd format file. Export the SolidWorks model of the main arch rib steel pipe component as an STL format file. Import the pcd format file and the STL format file into the Geomagic software to ensure that the data transmission and processing formats match. The pcd format file of the spliced ​​and registered point cloud data of the main arch rib steel pipe component is processed by Geomagic software such as faceting, and the point cloud scanning data of the main arch rib steel pipe component and the CAD design reference model are analyzed and compared to obtain the shape dimension analysis report. Set the point cloud data of the main arch rib steel pipe component as the control model (test data) in the Geomagic Control software. Set the SolidWorks model of the main arch rib steel pipe component as the design model (reference data) in the Geomagic Control software. The above-mentioned design model and the scanned model are adjusted to the same position and scale, and automatic position fitting is performed according to the prominent features of the main arch rib steel pipe component, which can be achieved by the Geomagic Control software function. The scanned model of the main arch rib steel pipe component and the SolidWorks design model are fitted and compared to obtain the component's external dimensions, deviation heat map and comparative analysis results. The deviation parameters are set in the 3D analysis function of the Geomagic Control software. The deviation parameters include standard deviation, average deviation, upper deviation value and lower deviation value, and the critical value and nominal value are set in the color spectrum column, which include the minimum nominal value and the maximum nominal value, and the tolerance threshold is set to 0.006m. The deviation of the main arch rib steel pipe component is calculated using the comparative analysis function of the Geomagic Control software to obtain the deviation heat map, and the Geomagic Control software is used to automatically generate a model comparative analysis report, which includes but is not limited to the overall size of the main arch rib steel pipe component, installation accuracy, etc. By generating a comparative analysis report, problems in construction and design are found, and the deviation parts of the point cloud data of the main arch rib steel pipe component and the CAD model are marked to obtain the final analysis results.

[0189] Optionally, as another embodiment of the present invention, compared with the prior art, the advantages of the present invention are:

[0190] 1. The present invention utilizes the characteristic of the main arch rib steel tube component with a smooth surface change, and proposes a feature point extraction method and a registration method obtained by calculating the sine value of the weighted vector at different scales, which can improve the registration stability and accuracy for the main arch rib steel tube component with a large surface that changes smoothly.

[0191] 2. The present invention mainly utilizes a three-dimensional laser scanner to perform a comprehensive scan on the surface of the main arch rib steel pipe component, thereby achieving complete coverage of the component surface and satisfying the high-precision and easy-to-operate equipment characteristics. Operators can complete accurate measurement of the target surface by scanning points outside the dangerous area, effectively avoiding the risks of operations in potentially dangerous construction areas, ensuring the normal progress of construction and production activities, avoiding any interference, and ensuring the smooth progress of construction, thereby improving data acquisition efficiency, data accuracy, and the safety of measurement operations.

[0192] 3. Using existing technology and algorithms, the accuracy can only reach 2-3cm, while the present invention can improve the accuracy to 2mm.

[0193] Optionally, as another embodiment of the present invention, the present invention mainly includes four parts: point cloud acquisition, point cloud preprocessing, point cloud registration, and comparative analysis between point cloud and design model.

[0194] 1. Point cloud acquisition stage:

[0195] Clean the surface of the main arch rib steel pipe component to avoid objects blocking the main arch rib steel pipe component and the 3D laser scanner; because the main arch rib steel pipe component material is affected by the temperature environment deformation, the 3D laser scanner is operated at night and early morning to ensure the environment is 20℃ (the allowable temperature difference is within ±2°).

[0196] Preferably, the 3D laser scanner uses the high-precision portable Austrian RIEGLVZ-1000, which can meet the needs of large-scene scanning tasks and provide complete on-site data without repeated scanning.

[0197] Determine the positional relationship between several scanning stations and the main arch rib steel pipe components according to their shape and height, and ensure that the scanning range of the three-dimensional laser scanner at several scanning stations can cover the main arch rib steel pipe components;

[0198] The parameters of the 3D laser scanner were adjusted to the high-frequency parameter mode to reduce the impact of environmental factors on the scanning effect of the 3D laser scanner. In order to prevent the on-site construction personnel from passing through the scanning area and affecting the scanning results, the construction site was temporarily blocked, and the status of the 3D laser scanner was carefully observed during the scanning process to prevent the 3D laser scanner from being vibrated. According to the adjustment of the parameters, the 3D laser scanner scanned the main arch rib steel pipe components at each scanning station to obtain the point cloud scanning data P and Q of the main arch rib steel pipe components;

[0199] 2. Point cloud preprocessing stage:

[0200] The nearest point voxel filtering algorithm is used to perform downsampling preprocessing on the point clouds of the main arch rib steel pipe components from two different perspectives. First, a three-dimensional voxel grid is created for the input point cloud data, and the centroid of all points in each voxel is calculated. Then, the KD-Tree algorithm is used to traverse the original point cloud to find the point in the point cloud that is closest to the centroid, and this point is used to replace all the points in the voxel to complete the downsampling preprocessing operation.

[0201] 3. Point cloud registration stage:

[0202] The feature point extraction in the registration stage of the present invention is improved by adding the sine value of weighted vectors at different scales to extract feature points, that is, after the point cloud data of the main arch rib steel pipe component is down-sampled by the nearest point voxel filtering algorithm, the ISS feature point extraction algorithm is used and a method based on the sine value of weighted vectors at different scales is proposed to extract the top-ranked feature points combined with the output of the final feature points, and then a rough registration is performed based on the calculated VFH feature descriptor to make the two point clouds obtain a suitable initial pose, and then the least squares optimized point-to-surface ICP fine registration is used to obtain the optimal rigid transformation matrix.

[0203] 4. Comparative analysis between point cloud and design model:

[0204] The aligned point cloud data of the main arch rib steel pipe component and the CAD design reference model are loaded into the Geomagic software together, and the point cloud scanning data of the main arch rib steel pipe component and the CAD design reference model are analyzed and compared to obtain the shape dimension analysis report.

[0205] Figure 3 A module block diagram of a component deviation measuring device provided in an embodiment of the present invention.

[0206] Optionally, as another embodiment of the present invention, Figure 3 As shown, a component deviation measuring device comprises:

[0207] A scanning module is used to scan the main arch rib steel pipe component to be measured by a three-dimensional laser scanner to obtain multiple original point cloud data;

[0208] A filtering module, used for filtering all the original point cloud data to obtain a plurality of filtered point cloud data;

[0209] A feature analysis module, used to perform feature analysis on all the filtered point cloud data to obtain a viewpoint feature histogram feature descriptor and multiple target feature points;

[0210] A registration analysis module, used for performing registration analysis on all the target feature points according to the viewpoint feature histogram feature descriptor to obtain a plurality of target point cloud data;

[0211] The measurement result acquisition module is used to import a component reference model, perform deviation measurement on all the target point cloud data through the component reference model, and obtain component deviation measurement results.

[0212] Optionally, another embodiment of the present invention provides a component deviation measurement system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the component deviation measurement method described above is implemented. The system may be a computer or other system.

[0213] Optionally, another embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the component deviation measurement method as described above is implemented.

[0214] 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", "comprises" 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0220] 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 component deviation measurement method, characterized in that: The steps include: The main arch rib steel pipe component to be measured is scanned by a 3D laser scanner to obtain multiple original point cloud data; Performing filtering processing on all the original point cloud data to obtain a plurality of filtered point cloud data; Performing feature analysis on all the filtered point cloud data to obtain a viewpoint feature histogram feature descriptor and a plurality of target feature points; Performing registration analysis on all the target feature points according to the viewpoint feature histogram feature descriptor to obtain a plurality of target point cloud data; A component reference model is imported, and deviation measurement is performed on all the target point cloud data using the component reference model to obtain a component deviation measurement result.

2. The component deviation measurement method according to claim 1, characterized in that: The feature analysis is performed on all the filtered point cloud data to obtain the viewpoint features. The process of histogram feature descriptor and multiple target feature points includes: Performing feature point analysis on all the filtered point cloud data to obtain multiple target feature points; The feature descriptors of all the target feature points are analyzed to obtain a viewpoint feature histogram feature descriptor.

3. The component deviation measurement method according to claim 2, characterized in that: The process of analyzing the feature points of all the filtered point cloud data to obtain multiple target feature points includes: Performing local feature analysis on all the filtered point cloud data to obtain a plurality of first initial feature points; The weighted vector sine value is analyzed for all the filtered point cloud data, and the analysis result is used as the second initial feature point set, and the feature points in the first initial feature point set and the second initial feature point set are used as target feature points, thereby obtaining multiple target feature points.

4. The component deviation measurement method according to claim 3, characterized in that: The process of performing local feature analysis on all the filtered point cloud data to obtain a plurality of first initial feature points includes: Performing neighborhood searches on each of the filtered point cloud data according to a K-dimensional tree algorithm and a preset first radius to obtain a plurality of first neighborhood points corresponding to each of the filtered point cloud data; The first point cloud weights are calculated for each of the filtered point cloud data and a plurality of first neighborhood points corresponding to each of the filtered point cloud data by using the first formula to obtain a plurality of first point cloud weights corresponding to each of the filtered point cloud data. The first formula is: Among them, w1 ij is the first point cloud weight corresponding to the jth first neighborhood point of the i-th filtered point cloud data, p i is the i-th filtered point cloud data, is the jth first neighborhood point corresponding to the i-th filtered point cloud data; The covariance matrix of the preset first radius, each of the filtered point cloud data, a plurality of first neighborhood points corresponding to each of the filtered point cloud data, and a plurality of first point cloud weights corresponding to each of the filtered point cloud data is calculated by the second formula to obtain a covariance matrix corresponding to each of the filtered point cloud data. The second formula is: Among them, C i is the covariance matrix corresponding to the i-th filtered point cloud data, r iss is the preset first radius, w1 ij is the first point cloud weight corresponding to the jth first neighborhood point of the i-th filtered point cloud data, p i is the i-th filtered point cloud data, is the jth first neighborhood point corresponding to the i-th filtered point cloud data; Performing eigenvalue decomposition on each of the covariance matrices to obtain a first initial eigenvalue corresponding to each filtered point cloud data, a second initial eigenvalue corresponding to each filtered point cloud data, and a third initial eigenvalue corresponding to each filtered point cloud data; The first initial eigenvalues, the second initial eigenvalues ​​corresponding to the filtered point cloud data, and the third initial eigenvalues ​​corresponding to the filtered point cloud data are sorted in ascending order to obtain the first sorted eigenvalues ​​corresponding to the filtered point cloud data, the second sorted eigenvalues ​​corresponding to the filtered point cloud data, and the third sorted eigenvalues ​​corresponding to the filtered point cloud data; When the first sorted eigenvalue, the second sorted eigenvalue, and the third sorted eigenvalue satisfy the third formula and the fourth formula, the filtered point cloud data corresponding to the first sorted eigenvalue is used as the first initial feature point, thereby obtaining multiple first initial feature points. The third formula is: Among them, λ i2 is the second sorted eigenvalue corresponding to the i-th filtered point cloud data, λ i3 is the third sorted eigenvalue corresponding to the i-th filtered point cloud data, and ε1 is the preset first characteristic threshold; The fourth formula is: Among them, λ i1 is the first sorted eigenvalue corresponding to the i-th filtered point cloud data, λ i2 is the second sorted eigenvalue corresponding to the i-th filtered point cloud data, and ε2 is the preset second characteristic threshold.

5. The component deviation measurement method according to claim 4, characterized in that: The process of analyzing the weighted vector sine value of all the filtered point cloud data and using the analysis result as the second initial feature point set includes: Performing neighborhood searches on each of the filtered point cloud data according to a K-dimensional tree algorithm and a preset second radius to obtain a plurality of second neighborhood points corresponding to each of the filtered point cloud data; Performing neighborhood searches on each of the filtered point cloud data according to a K-dimensional tree algorithm and a preset third radius to obtain a plurality of third neighborhood points corresponding to each of the filtered point cloud data; Vector calculations are performed on each of the filtered point cloud data, a plurality of second neighborhood points corresponding to each of the filtered point cloud data, and a plurality of third neighborhood points corresponding to each of the filtered point cloud data by using a first set of equations to obtain a plurality of first neighborhood point vectors corresponding to each of the filtered point cloud data and a plurality of second neighborhood point vectors corresponding to each of the filtered point cloud data. The first set of equations is: in, is the first neighborhood point vector corresponding to the ath second neighborhood point of the i-th filtered point cloud data, p i is the i-th filtered point cloud data, is the ath second neighborhood point corresponding to the i-th filtered point cloud data, is the second neighborhood point vector corresponding to the bth third neighborhood point of the i-th filtered point cloud data, is the bth third neighborhood point corresponding to the i-th filtered point cloud data; The second point cloud weights are calculated for each of the plurality of first neighborhood point vectors corresponding to each of the filtered point cloud data by the fifth formula to obtain a plurality of second point cloud weights corresponding to each of the filtered point cloud data. The fifth formula is: Among them, w2 ia is the second point cloud weight corresponding to the a-th second neighborhood point of the i-th filtered point cloud data, is the first neighborhood point vector corresponding to the ath second neighborhood point of the i-th filtered point cloud data; The third point cloud weights are calculated for each of the plurality of second neighborhood point vectors corresponding to each of the filtered point cloud data by the sixth formula to obtain a plurality of third point cloud weights corresponding to each of the filtered point cloud data. The sixth formula is: Among them, w3 ib is the third point cloud weight corresponding to the bth third neighborhood point of the i-th filtered point cloud data, is the second neighborhood point vector corresponding to the bth third neighborhood point of the i-th filtered point cloud data; The neighborhood angles are calculated for the multiple first neighborhood point vectors corresponding to each of the filtered point cloud data, the multiple second neighborhood point vectors corresponding to each of the filtered point cloud data, the multiple second point cloud weights corresponding to each of the filtered point cloud data, and the multiple third point cloud weights corresponding to each of the filtered point cloud data through the seventh formula to obtain the initial neighborhood angles corresponding to each of the filtered point cloud data. The seventh formula is: Among them, θ i is the initial neighborhood angle corresponding to the i-th filtered point cloud data, is the first neighborhood point vector corresponding to the ath second neighborhood point of the i-th filtered point cloud data, is the second neighborhood point vector corresponding to the bth third neighborhood point of the i-th filtered point cloud data, w2 ia is the second point cloud weight corresponding to the ath second neighborhood point of the i-th filtered point cloud data, w3 ib is the third point cloud weight corresponding to the bth third neighborhood point of the i-th filtered point cloud data; Sorting all the initial neighborhood angles in descending order to obtain sorted neighborhood angles corresponding to each of the filtered point cloud data; Filtering out the sorted neighborhood angles that meet the screening condition from all the sorted neighborhood angles, and obtaining a plurality of filtered neighborhood angles after screening, wherein the screening condition is that the sorted neighborhood angle is less than a preset angle threshold; The filtered point cloud data corresponding to the first A filtered neighborhood angles are used as second initial feature points, thereby obtaining a plurality of second initial feature points, and all of the second initial feature points are combined to obtain a second initial feature point set.

6. The component deviation measurement method according to claim 2, characterized in that: The process of analyzing the feature descriptors of all the target feature points to obtain the viewpoint feature histogram feature descriptors includes: Using a k-nearest neighbor algorithm to perform neighborhood searches on each of the target feature points, to obtain a plurality of fourth neighborhood points corresponding to each of the target feature points; Using a principal component analysis algorithm, normal vectors are calculated for a plurality of fourth neighborhood points corresponding to each of the target feature points to obtain normal vectors corresponding to each of the target feature points; The center point of all the target feature points is calculated by the seventh formula to obtain the center point The seventh formula is: in, is the X-axis coordinate of the center point, is the Y-axis coordinate of the center point, is the Z-axis coordinate of the center point, N is the total number of target feature points, x k is the X-axis coordinate of the kth target feature point, y k is the Y-axis coordinate of the kth target feature point, z k is the Z-axis coordinate of the kth target feature point; The center point angle corresponding to each of the target feature points is calculated by the eighth formula respectively for the center point, each of the target feature points, and the normal vector corresponding to each of the target feature points to obtain the center point angle corresponding to each of the target feature points. The eighth formula is: Among them, α k is the center point angle corresponding to the kth target feature point, o is the center point, p4 k is the kth target feature point, n k is the normal vector corresponding to the kth target feature point; The normal vector angles corresponding to each of the target feature points and the normal vector corresponding to any adjacent target feature point are calculated by the ninth formula to obtain a plurality of normal vector angles corresponding to each of the target feature points. The ninth formula is: Among them, β ku is the normal vector angle between the kth target feature point and the uth target feature point, n k is the normal vector corresponding to the kth target feature point, n u is the normal vector corresponding to the u-th target feature point, arccos() is the inverse cosine function; Performing a viewpoint feature histogram analysis on all the center point angles and all the normal vector angles to obtain a viewpoint feature histogram; Vectorization is performed on the viewpoint feature histogram to obtain a viewpoint feature histogram feature descriptor.

7. The component deviation measurement method according to claim 6, characterized in that: The process of analyzing the viewpoint feature histogram of all the center point angles and all the normal vector angles to obtain the viewpoint feature histogram includes: Filter out the minimum value from all the center point angles, and obtain the minimum center point angle after filtering; The maximum value is selected from all the center point angles, and the maximum center point angle is obtained after the selection; Filter out the minimum value from all the normal vector angles, and obtain the minimum normal vector angle after filtering; Filter out the maximum value from all the normal vector angles, and obtain the maximum normal vector angle after filtering; The center point angle interval is obtained by taking the minimum center point angle as the minimum value and the maximum center point angle as the maximum value; The normal vector angle interval is obtained by taking the minimum normal vector angle as the minimum value and the maximum normal vector angle as the maximum value; Dividing the center point angle interval into a plurality of center point angle sub-intervals with equal distances; Counting the total number of all the center point angle sub-intervals to obtain the total number of center point angle sub-intervals; Dividing the normal vector angle interval into a plurality of normal vector angle sub-intervals at equal distances; Counting the total number of all the normal vector angle sub-intervals to obtain the total number of normal vector angle sub-intervals; The frequency of the minimum center point angle, the maximum center point angle, the total number of center point angle sub-intervals, and the multiple center point angles in each center point angle sub-interval is calculated by the tenth formula to obtain the center point angle frequency corresponding to each center point angle sub-interval. The tenth formula is: in, Among them, H α (j′) is the frequency of the center point angle corresponding to the j′th center point angle subinterval, N is the total number of target feature points, is the indicator function, α min is the minimum center point angle, α max is the maximum center point angle, Δα is the center point angle interval width, α k is the center point angle corresponding to the kth target feature point, m′ is the total number of center point angle subintervals, and j′ is the j′th center point angle subinterval; The frequency of the normal vector angle corresponding to each normal vector angle subinterval is obtained by calculating the minimum normal vector angle, the maximum normal vector angle, the total number of normal vector angle subintervals, and the multiple normal vector angles in each normal vector angle subinterval using the eleventh formula, and the eleventh formula is: in, Among them, H β (k′) is the frequency of the normal vector angle corresponding to the k′th normal vector angle subinterval, N is the total number of target feature points, is the indicator function, adj(k) is the set of target feature points adjacent to the kth target feature point, β min is the minimum normal vector angle, Δβ is the normal vector angle interval width, β ku is the normal vector angle between the kth target feature point and the uth target feature point, k′ is the k′th normal vector angle subinterval, β max is the maximum normal vector angle, n′ is the total number of normal vector angle subintervals; The viewpoint feature histogram is calculated for all the center point angle frequencies and all the normal vector angle frequencies through the twelfth formula to obtain the viewpoint feature histogram. The twelfth formula is: H=[H α (1),H α (2),...,H α (j′),H β (1),H β (2),...,H β (k′)] T , Among them, H is the viewpoint feature histogram, H α (j′) is the frequency of the center point angle corresponding to the j′th center point angle subinterval, H β (k′) is the normal vector angle frequency corresponding to the k′th normal vector angle subinterval.

8. The component deviation measurement method according to claim 1, characterized in that: The process of performing registration analysis on all the target feature points according to the viewpoint feature histogram feature descriptor to obtain a plurality of target point cloud data includes: Performing rough registration processing on all the target feature points according to the NDT registration algorithm and the viewpoint feature histogram feature descriptor to obtain a plurality of rough registered point cloud data; The ICP fine registration algorithm is used to perform fine registration processing on all the roughly registered point cloud data to obtain multiple target point cloud data.

9. A component deviation measuring device, characterized in that: include: A scanning module is used to scan the main arch rib steel pipe component to be measured by a three-dimensional laser scanner to obtain multiple original point cloud data; A filtering module, used for filtering all the original point cloud data to obtain a plurality of filtered point cloud data; A feature analysis module, used to perform feature analysis on all the filtered point cloud data to obtain a viewpoint feature histogram feature descriptor and multiple target feature points; A registration analysis module, used for performing registration analysis on all the target feature points according to the viewpoint feature histogram feature descriptor to obtain a plurality of target point cloud data; The measurement result acquisition module is used to import a component reference model, perform deviation measurement on all the target point cloud data through the component reference model, and obtain component deviation measurement results.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the component deviation measurement method according to any one of claims 1 to 8 is implemented.

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