Duct piece size measuring method, duct piece size calibrating device and assembly type lining assembling system

Abstract

The invention provides a segment size measurement method, a segment size calibration device and an assembly type lining assembly system. The method comprises the following steps: acquiring model point cloud data and actually measured point cloud data, and registering the actually measured point cloud data to obtain registered point cloud data; performing background separation on the registered point cloud data through the model point cloud data and the registered point cloud data to obtain to-be-detected point cloud data of the prefabricated segment; size measurement is carried out based on the point cloud data to be detected, and actual size parameters of the prefabricated segment are obtained; the invention provides a detection means for a lining complex structure, especially a segment structure, and meets the tolerance requirement of high-precision segment size measurement.

Description

Technical Field

[0001] The present invention relates to the technical field of surveying and mapping engineering, and particularly to a segment dimension measurement method, a segment dimension calibration device, and an assembled lining assembly system. Background Art

[0002] Assembled linings are widely used in projects such as tunnels and underground pipe galleries, and their dimensional accuracy directly affects the assembly quality and structural safety. Currently, commonly used automated dimension measurement methods have been gradually applied to industrial inspections, including two-dimensional image recognition, depth camera point cloud analysis, three-dimensional scanning dimension measurement, etc., which greatly improve the inspection efficiency.

[0003] However, the precast segments used in assembled linings have complex curved surfaces. The above-mentioned automated dimension measurement methods lack detection means for the complex structure of the lining and are difficult to meet the tolerance requirements of high-precision segment dimension measurement. Summary of the Invention

[0004] Aiming at the deficiencies in the prior art, the present invention provides a segment dimension measurement method, which solves the problems in the prior art that the automated dimension measurement method lacks detection means for the complex structure of the lining and is difficult to meet the tolerance requirements of high-precision segment dimension measurement.

[0005] According to an embodiment of the present invention, a segment dimension measurement method is provided in a first aspect, including: Obtaining model point cloud data and measured point cloud data, and registering the measured point cloud data to obtain registered point cloud data; Separating the background of the registered point cloud data through the model point cloud data and the registered point cloud data to obtain the to-be-detected point cloud data of the precast segment; Performing dimension measurement based on the to-be-detected point cloud data to obtain the actual dimension parameters of the precast segment; Wherein, the model point cloud data is obtained based on a segment design model; the measured point cloud data is obtained by scanning a precast segment through a pre-deployed scanning system, including three-dimensional space data with different coordinate systems provided by different three-dimensional scanner stations; the three-dimensional space data provided by different three-dimensional scanner stations in the registered point cloud data has the same coordinate system and the same coordinate system as the model point cloud data; the actual dimension parameters include the rectangular end face geometric parameters and the multi-centered circle side face geometric parameters of the precast segment.

[0006] Optionally, the pre-deployed scanning system includes multiple three-dimensional scanner stations and multiple scanning targets; One or more three-dimensional scanner stations are used to scan one precast segment; When multiple 3D scanner stations scan the same precast segment, the overlap degree of the 3D spatial data provided by adjacent 3D scanner stations is higher than a preset overlap value, and the 3D spatial data provided by each 3D scanner station includes at least three scanning targets, where the three scanning targets are not on the same straight line.

[0007] Optionally, registering the measured point cloud data includes: Registering the 3D spatial data provided by each 3D scanner station according to the target center coordinates to obtain the initially registered point cloud data; Extracting the plane features of the model point cloud data and extracting the plane features of the initially registered point cloud data to obtain the model plane features and the measured plane features; Based on the model plane features and the measured plane features, registering the initially registered point cloud data with the model point cloud data to obtain the completely registered point cloud data.

[0008] Optionally, registering the 3D spatial data provided by each 3D scanner station according to the target center coordinates to obtain the initially registered point cloud data includes: Obtaining the 3D spatial data provided by each 3D scanner station, using RANSAC (random sample consensus) spherical fitting and the least squares method to calculate the target center coordinates of each scanning target, and calculating n pairs of target center coordinates, where n is greater than or equal to 3; Wherein, when the 3D spatial data provided by one 3D scanner station and the 3D spatial data provided by another 3D scanner station have the same scanning target, the target center coordinates of a scanning target in the 3D spatial data provided by one 3D scanner station and the target center coordinates of a scanning target in the 3D spatial data provided by another 3D scanner station are a pair of target center coordinates; Registering the measured point cloud data according to n pairs of target center coordinates to obtain the initially registered point cloud data.

[0009] Optionally, obtaining the 3D spatial data provided by any one 3D scanner station and using RANSAC spherical fitting and the least squares method to calculate the target center coordinates of a scanning target includes: Sa. Randomly grab the point data about the target scanning target in the 3D spatial data provided by any one 3D scanner station, obtain the target sphere based on the grabbed point data, and calculate the spherical equation of the target sphere; Sb. Calculate the distance from the ungrabbed point data to the target sphere, and compare it with a preset distance threshold to obtain the comparison result of whether the ungrabbed point data is an inlier or the ungrabbed point data is an outlier, and record the number of inliers in the comparison result based on the target sphere at the same time; Sc. Repeat Sa and Sb to obtain M target spheres and M comparison results based on the M target spheres; Sd. Obtain the model parameters of the m target spheres with the largest number of inlier points, and perform least squares sphere fitting on the point cloud data of the inlier points among the m target spheres as the final sphere fitting coefficients, where the final sphere fitting coefficients are used to calculate the target center coordinates of the target target; Wherein, M is a positive integer, and m is a positive integer less than or equal to M.

[0010] Optionally, based on the model point cloud data and the registered point cloud data, perform background separation on the registered point cloud data to obtain the point cloud data to be detected of the precast segment, including: Construct a KD-Tree (k-dimensional-Tree) index of the model point cloud data and the registered point cloud data; Search in the registered point cloud data, and the point data at the current search position and the point data within a preset search radius based on the current search position are the data to be matched; According to the KD-Tree index, if there is no corresponding point data in the model point cloud data for the data to be matched, mark the data to be matched as background point data.

[0011] Optionally, after marking the data to be matched as background point data, further include: Perform morphological dilation and clustering on all the data to be matched marked as background point data to obtain K point sets; Detect the K point sets. If it is detected that the data to be matched marked as background point data included in the k-th point set is greater than a preset quantity value, delete the k-th point set; otherwise, retain the k-th point set; where K is a positive integer, and k is a positive integer less than or equal to K; Merge all the detected point sets as the point cloud data to be detected.

[0012] Optionally, perform dimensional measurement based on the point cloud data to be detected to obtain the actual dimensional parameters of the precast segment, including: Extract edge point data based on the point cloud data to be detected; According to the edge point data, use the RANSAC line fitting method to obtain the contour line of the rectangular end face of the precast segment. The positions of the intersections of adjacent contour lines are the corner points of the precast segment, and calculate the geometric parameters of the rectangular end face of the precast segment according to the corner points of the precast segment; Detect the arcs and turning points of adjacent arcs in the edge point data, divide the adjacent arcs into multiple circular arcs according to the turning points, and perform single circular arc fitting on each circular arc, where the multiple circular arcs form the multi-centered circular side of the precast segment; Calculate the geometric parameters of the multi-centered circular side of the precast segment according to the single circular arc fitting results of the multiple circular arcs.

[0013] In a second aspect, a segment size calibration device is provided, including a design size parameter acquisition module, an actual size parameter acquisition module, and a size parameter comparison module. The actual size parameter acquisition module measures a target precast segment by the segment size measurement method as described above to obtain the actual size parameters of the target precast segment; The design size parameter acquisition module is used to obtain design size parameters according to the segment design model; The size parameter comparison module is used to compare the actual size parameters of the target precast segment with the design size parameters. If it exceeds the preset error value, the target precast segment is marked as a segment to be repaired.

[0014] In a third aspect, an assembled lining assembly system is provided, including a segment size calibration device and a lining device. The segment size calibration device includes a design size parameter acquisition module, an actual size parameter acquisition module, and a size parameter comparison module. The actual size parameter acquisition module measures a target precast segment by the segment size measurement method as described above to obtain the actual size parameters of the target precast segment; The design size parameter acquisition module is used to obtain design size parameters according to the segment design model; The size parameter comparison module is used to compare the actual size parameters of the target precast segment with the design size parameters. If it exceeds the preset value, the target precast segment is marked as a segment to be repaired. If not, the target precast segment is marked as a target precast segment to be assembled; The lining device includes a segment grouping module and a simulated assembly module; The segment grouping module is used to group the precast segments to be assembled according to the actual size parameters of all the precast segments to be assembled. Among them, all the precast segments to be assembled are the precast segments calibrated by the segment size calibration device, and the precast segments to be assembled with size parameters within the same range are divided into one group for lining of the same ring; The simulated assembly module is used to simulate the assembly process based on the grouped precast segments to be assembled to obtain the arrangement order of the precast segments to be assembled, so that the assembly equipment assembles the precast segments to be assembled according to the arrangement order.

[0015] Compared with the prior art, the present invention has the following beneficial effects: First, the measured point cloud data of the precast segment is obtained by scanning the precast segment with a pre-deployed scanning system; then, it is registered with the model point cloud data of the segment design model to achieve a better background separation effect and obtain accurate point cloud data to be measured only for the precast segment; finally, dimension measurement is performed based on the point cloud data to be measured, and parameters to be measured are designed for the complex structure of the segment, that is, the geometric parameters of the rectangular end face and the geometric parameters of the multi-centered circle side face of the precast segment. Thus, a segment dimension measurement process of automatic point cloud registration, background separation and dimension calculation is formed, which not only greatly improves the detection efficiency and meets the requirements of mass production, but also does not require manual intervention in the whole process from point cloud preprocessing to dimension output. Description of the Drawings

[0016] Figure 1 It is a schematic implementation flowchart of the segment dimension measurement method according to an embodiment of the present invention; Figure 2 It is a layout diagram of the 3D scanner stations and scanning targets in the pre-deployed scanning system according to an embodiment of the present invention; Figure 3 It is a dimension parameter annotation and schematic diagram based on the point cloud data to be detected of the precast segment according to an embodiment of the present invention; Figure 4 Based on Figure 3 The annotation and schematic diagram of the geometric parameters of the multi-centered circle side face; Figure 5 It is a schematic composition structure diagram of the segment dimension calibration device according to an embodiment of the present invention; Figure 6 It is a schematic composition structure diagram of the assembled lining assembly system according to an embodiment of the present invention. Detailed Embodiment

[0017] The technical solutions in the present invention will be further described below with reference to the drawings and embodiments.

[0018] As Figure 1 shown, an embodiment of the present invention proposes a segment dimension measurement method applied to an assembled lining, including but not limited to the following steps: S101. Obtain model point cloud data and measured point cloud data, and register the measured point cloud data to obtain registered point cloud data; S102. Perform background separation on the registered point cloud data through the model point cloud data and the registered point cloud data to obtain the point cloud data to be detected of the precast segment; S103. Perform dimension measurement based on the point cloud data to be detected to obtain the actual dimension parameters of the precast segment; Among them, the model point cloud data is obtained based on the segment design model; the measured point cloud data is obtained by scanning the prefabricated segment through a pre-deployed scanning system, including three-dimensional space data with different coordinate systems provided by different three-dimensional scanner stations; the three-dimensional space data provided by different three-dimensional scanner stations in the registered point cloud data has the same coordinate system and the same coordinate system as the model point cloud data; the actual dimension parameters include the rectangular end face geometric parameters and the multi-centered circle side face geometric parameters of the prefabricated segment.

[0019] In the embodiment of the present invention, the pre-deployed scanning system includes a plurality of three-dimensional scanner stations and a plurality of scanning targets. One or more three-dimensional scanner stations are used to scan a prefabricated segment to ensure that each part of the data can be extracted. As Figure 2 shown, it is the positional relationship among the three-dimensional scanner station 21, the scanning target 22, and the prefabricated segment 23 shown by the pre-deployed scanning system in the embodiment of the present invention. In specific applications, the above pre-deployment needs to meet: when a plurality of three-dimensional scanner stations scan the same prefabricated segment, the overlap degree of the three-dimensional space data provided by adjacent three-dimensional scanner stations is higher than a preset overlap value, and the three-dimensional space data provided by each three-dimensional scanner station includes at least three scanning targets, where the three scanning targets are not on the same straight line. Thus, the pre-deployment method provided by the embodiment of the present invention can obtain complete point cloud data, which is beneficial to the acquisition of the point cloud data on the segment surface and the subsequent data processing.

[0020] In the embodiment of the present invention, the measured point cloud data includes three-dimensional space data output by the scanning system scanning the prefabricated segment from multiple angles. Therefore, the three-dimensional space data at different angles is usually provided by different three-dimensional scanner stations, and the scanning of each station defaults to use the position of the three-dimensional scanner station as the center coordinate origin. After the scanning is completed, the three-dimensional space data with different coordinate systems needs to be processed, that is, the registration of the measured point cloud data in the above step S101. It should be noted that the registration in the above step S101 includes converting the three-dimensional space data provided by different three-dimensional scanner stations into the same coordinate system, so that they can be integrated into a whole to obtain a complete scanning model, that is, the point cloud data of the initial registration in the following steps. It also includes the registration of the initially registered point cloud data with the model point cloud data, so that it also has the same coordinate system as the model point cloud data. The registered point cloud data can obtain a complete scanning model to complete the subsequent segment dimension measurement. Based on this, the implementation steps of registering the measured point cloud data in the above step S101 include: S1011. Register the three-dimensional space data provided by each three-dimensional scanner station according to the target center coordinates to obtain the initially registered point cloud data; S1012. Extract the plane features of the model point cloud data, and extract the plane features of the point cloud data of the initial registration to obtain the model plane features and the measured plane features; S1013. Based on the model plane features and the measured plane features, register the point cloud data of the initial registration with the model point cloud data to obtain the registered point cloud data.

[0021] For the above step S1011, the embodiment of the present invention provides an implementation method for registering the measured point cloud data, which is to solve the coordinate transformation between the point cloud data of different 3D scanner stations through the target center coordinates of the same scanning target obtained by different 3D scanner stations. In a preferred implementation, the above step S1011 includes: S10111. Obtain the 3D space data provided by each 3D scanner station, calculate the target center coordinates of each scanning target using RANSAC spherical fitting and the least squares method, and calculate n pairs of target center coordinates, where n is greater than or equal to 3; Wherein, when the 3D space data provided by one 3D scanner station and the 3D space data provided by another 3D scanner station have the same scanning target, the target center coordinates of a scanning target in the 3D space data provided by one 3D scanner station and the target center coordinates of a scanning target in the 3D space data provided by another 3D scanner station are a pair of target center coordinates; S10112. Register the measured point cloud data according to n pairs of target center coordinates to obtain the point cloud data of the initial registration.

[0022] Exemplarily, the target center coordinates of a scanning target in the measured point cloud data provided by one 3D scanner station are P1, and the target center coordinates of a scanning target in the measured point cloud data provided by another 3D scanner station are P2. The rigid body transformation of the point cloud data is represented by the translation matrix R and the rotation matrix T. P1 is the target coordinate system: (X, Y, Z), and P2 is the coordinate system to be rotated: (x, y, z). The conversion process is as follows:

[0023]

[0024]

[0025] In the formula: is the rotation angle along the X, Y, and Z axes; is the displacement.

[0026] According to the above formula, it can be known that can be obtained through three pairs of common point coordinates and , thereby realizing the above step S10112, wherein the common point coordinates are the coordinate positions of the center of the ball target scanned by each station.

[0027] The embodiment of the present invention also provides a step of using RANSAC spherical fitting and least squares method to calculate the center coordinates of a scanning target, and illustrates the implementation steps of the above step S10111, including: Sa. randomly grabbing point data about a target scanning target in the three-dimensional space data provided by any three-dimensional scanner station, obtaining a target sphere based on the grabbed point data, and calculating a spherical equation of the target sphere; Sb, calculating the distance from the ungrasped point data to the target spherical surface, and comparing it with a preset distance threshold to obtain a comparison result of whether the ungrasped point data is an inner point or an outer point, and recording the number of inner points in the comparison result based on the target spherical surface; Sc, repeat Sa and Sb to obtain M target spheres and M comparison results based on the M target spheres; Sd, obtain the model parameters of the m target spheres with the largest number of inner points, and perform least squares spherical fitting on the point cloud data of the m target spheres as the inner points, as the final spherical fitting coefficients, and the final spherical fitting coefficients are used to calculate the target center coordinates of the target.

[0028] It should be noted that the embodiment of the present invention can eliminate the error introduced by random sampling in the RANSAC stage through the above steps and improve the fitting accuracy. In addition, an iterative judgment factor is provided to determine whether the above step Sc is executed, that is, whether the above step Sb is repeated, wherein Sb also includes: Statistical error rate, number of best internal points, total number of samples, current number of iterations, and obtain the iteration end judgment factor; The iteration ends when the iteration end judgment factor is greater than or equal to the preset iteration value, and Sc is executed when the iteration end judgment factor is less than the preset iteration value.

[0029] Based on the implementation of the above step S10111, the initial registered point cloud data obtained in the above step S10112 is the only and optimal plane hypothesis model obtained based on n pairs of target center coordinates. Its implementation steps exemplarily include: iteratively performing single plane fitting based on n pairs of target center coordinates and measured point cloud data, and each time the fitting is successful, the internal points obtained this time are segmented from the measured point cloud data until all plane features are fitted or the number of iterations reaches a set threshold. At this point, the above step S1011 ends.

[0030] Regarding the above steps S1012 and S1013, the registration of the initially registered point cloud data and the model point cloud data includes registration based on plane features, which exemplarily includes: RANSAC is used to extract the plane features of the point cloud; Based on the extracted plane features, a rough registration is performed. Each lining block has four planes. The initial rigid body transformation matrix matching the measured point cloud with the design model point cloud is obtained through the centroid positions of the four planes. In a better implementation, the registration accuracy can also be improved by using an algorithm, such as using an ICP algorithm to further perform precise registration. At this point, the above step S1012 ends.

[0031] According to the above-mentioned step S101 and its detailed implementation steps, the pre-deployed scanning system in the embodiment of the present invention provides an optimized layout method based on multiple three-dimensional scanning sites and scanning targets, such as the design of layout overlap, non-coplanar target layout, and target sphere RANSAC spherical fitting algorithm, combined with the least squares method to optimize the target center coordinates, etc., which improves the accuracy and efficiency of multi-site cloud registration.

[0032] In an embodiment of the present invention, the point cloud data obtained after the registration according to the above steps cannot be directly used for measuring the size of the pipe segment, but needs to be separated from the background, that is, the point cloud data of the background part is deleted, and the point cloud data about the prefabricated pipe segment part is retained, wherein the point cloud data about the prefabricated pipe segment part is the point cloud data to be detected in the above step S102.

[0033] In specific applications, the background separation of point cloud data can be achieved through a deep learning algorithm or a range query algorithm. The embodiment of the present invention exemplarily outputs an implementation method of background separation using a KD-Tree index. Therefore, the above step S102 includes: S1021, constructing a KD-Tree index of the model point cloud data and the registered point cloud data; S1022, searching in the registered point cloud data, the point data at the current search position and the point data within a preset search radius based on the current search position are the data to be matched; S1023. According to the KD-Tree index, if there is no point data corresponding to the data to be matched in the model point cloud data, the data to be matched is marked as background point data.

[0034] It should be noted that, in the above step S1022, the search radius can be set according to the accuracy requirement. In a better implementation, the search radius is set to 5 mm.

[0035] In the embodiment of the present invention, after marking the to-be-matched data as background point data in the above step S1023, the to-be-matched point data marked as background point data are processed to remove the to-be-matched data marked as background point data, and at the same time, expansion and clustering algorithms are used to avoid accidental deletion. Exemplarily, the steps include: S1024. Morphologically dilate all the to-be-matched point data marked as background point data and perform clustering to obtain K point sets; S1025. Detect the K point sets. If it is detected that the to-be-matched point data marked as background point data included in the k-th point set is greater than a preset numerical value, delete the k-th point set; otherwise, retain the k-th point set; S1026. Merge all the detected point sets as the to-be-detected point cloud data.

[0036] It should be noted that in the above step S1024, the degree of morphological dilation can be set according to the accuracy requirement. In a better implementation, the degree of morphological dilation is set to 3 mm.

[0037] According to the above step S102 and its detailed implementation steps, in the embodiment of the present invention, based on the registration of the measured point cloud data and the model point cloud data, by discretizing the design model into a point cloud, combining RANSAC plane feature extraction and ICP precise registration, the automatic alignment of the measured point cloud and the design model is realized. In the background point cloud removal method provided by the embodiment of the present invention, the KD-Tree index is used to efficiently remove the background point cloud, reducing manual intervention.

[0038] In specific applications, the end face of the precast segment is rectangular, while the form of the tunnel section is a multi-centered circle. Therefore, the upper and lower arcs on the side of the precast segment may be single arcs or segmented arcs. Then, the multi-centered circle side geometric parameters in the above step S103 include the arc length, radius measured based on each arc after single arc fitting by segmentation, and the position in the arc. Based on this, one implementation of the above step S103 includes: S1031. Extract edge point data based on the to-be-detected point cloud data; S1032. According to the edge point data, use the RANSAC line fitting method to obtain the contour line of the rectangular end face of the precast segment. The position of the intersection of adjacent contour lines is the corner point of the precast segment. Calculate the rectangular end face geometric parameters of the precast segment according to the corner points of the precast segment; S1033. Detect the arcs in the edge point data and the turning points based on adjacent arcs. Divide the adjacent arcs into multiple arcs according to the turning points, and perform single arc fitting on each arc, where the multiple arcs form the multi-centered circle side of the precast segment; S1034. Calculate the multi-centered circle side geometric parameters of the precast segment according to the single arc fitting results of the multiple arcs.

[0039] Such as Figure 3As shown in the figure, the embodiment of the present invention provides the point cloud data to be detected of the precast segment, and marks the edge point data extracted based on the point cloud data to be detected in the above step S1031, the contour line such as L of the rectangular end face of the precast segment in the above step S1032, and the arcs such as A and B in the edge point data in the above S1033.

[0040] It should be noted that in the above step S1032, the contour line of the rectangular end face is a straight line. Refer to Figure 3 , which exemplarily shows the corner points l1, l2, l3, and l4 of the precast segment. Then, the geometric parameters of the rectangular end face of the precast segment are calculated according to the corner points of the precast segment, mainly including the length and width of the rectangular end face. Among them, the length of the rectangular end face is the distance between the corner point l1 and the corner point l4, and is also the distance between the corner point l2 and the corner point l3. The width of the rectangular end face is the distance between the corner point l1 and the corner point l2, and is also the distance between the corner point l3 and the corner point l4.

[0041] It should be noted that in the above step S1033, the detection is based on the turning points of adjacent arcs. The implementation method can be: calculating the chord height of adjacent points along the arc length direction of adjacent arcs, and if the chord height of adjacent points exceeds the preset turning threshold, it is determined as a turning point. For example, Figure 3 in, the arc A and the arc B are adjacent arcs, and the shortest distance from any point a on the arc A to the arc B is the chord height of adjacent points. Then in the above step S1034, the geometric parameters of the multi-centered circular side of the precast segment include the arc length and radius of each arc and its position in the arc. Among them, the position of each arc in the arc is used to indicate that multiple arcs form the multi-centered circular side of the precast segment. Exemplarily, Figure 4 shows a section of arc C of the multi-centered circular side based on Figure 3 . Then the geometric parameters of the multi-centered circular side of the precast segment include the arc length lc and radius rc of the arc C and the position of the arc C in the arc B, such as the starting position coordinates and ending position coordinates of the arc C.

[0042] According to the above step S103 and its detailed implementation steps, in the embodiment of the present invention, for the automatic dimension measurement, the alpha-shape edge point extraction algorithm is combined with the RANSAC segmented fitting technology. For the rectangular end face and the multi-centered circular side of the assembled lining, through steps such as plane projection, chord height threshold segmentation, arc and straight line fitting, the geometric parameters of the rectangular end face and the multi-centered circular side are automatically calculated. It can be understood that the above method also supports the generalization detection of other lining structures and has the value of engineering promotion.

[0043] Such as Figure 5 shown, another embodiment of the present invention provides a segment dimension calibration device 50, including: The design dimension parameter acquisition module 51 is configured to acquire design dimension parameters according to the segment design model; The actual dimension parameter acquisition module 52 is configured to measure the target precast segment by the segment dimension measurement method as described above to obtain the actual dimension parameters of the target precast segment; The dimension parameter comparison module 53 is configured to compare the actual dimension parameters of the target precast segment with the design dimension parameters. If the preset error value is exceeded, the target precast segment is marked as a segment to be repaired.

[0044] As Figure 6 shown, another embodiment of the present invention provides an assembled lining assembly system 100, including: The segment dimension calibration device 50, the segment dimension calibration device 50 includes a design dimension parameter acquisition module 51, an actual dimension parameter acquisition module 52 and a dimension parameter comparison module 53; The design dimension parameter acquisition module 51 is configured to acquire design dimension parameters according to the segment design model; The actual dimension parameter acquisition module 52 is configured to measure the target precast segment by the segment dimension measurement method as described above to obtain the actual dimension parameters of the target precast segment; The dimension parameter comparison module 53 is configured to compare the actual dimension parameters of the target precast segment with the design dimension parameters. If the preset value is exceeded, the target precast segment is marked as a segment to be repaired. Otherwise, the target precast segment is marked as a target precast segment to be assembled; The lining device 60, the lining device includes a segment grouping module 61 and a simulated assembly module 62; The segment grouping module 61 is configured to group the precast segments to be assembled according to the actual dimension parameters of all the precast segments to be assembled. Among them, all the precast segments to be assembled are the precast segments calibrated by the segment dimension calibration device. The precast segments to be assembled with dimension parameters within the same range are divided into one group for lining of the same ring; The simulated assembly module 62 is configured to simulate the assembly process based on the grouped precast segments to be assembled to obtain the arrangement order of the precast segments to be assembled, so that the assembly device assembles the precast segments to be assembled according to the arrangement order.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the purpose and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for measuring the size of a segment, characterized in that, Including: Obtain model point cloud data and measured point cloud data, and register the measured point cloud data to obtain registered point cloud data; Based on the model point cloud data and the registered point cloud data, perform background separation on the registered point cloud data to obtain the point cloud data to be detected of the precast segment; Perform dimension measurement based on the point cloud data to be detected to obtain the actual dimension parameters of the precast segment; Wherein, the model point cloud data is obtained based on the segment design model; the measured point cloud data is obtained by scanning the precast segment through a pre-deployed scanning system, including three-dimensional space data with different coordinate systems provided by different three-dimensional scanner stations; the three-dimensional space data provided by different three-dimensional scanner stations in the registered point cloud data has the same coordinate system and the same coordinate system as the model point cloud data; the actual dimension parameters include the rectangular end face geometric parameters and the multi-centered circle side face geometric parameters of the precast segment.

2. The segment size measurement method according to claim 1, characterized in that, The pre-deployed scanning system includes multiple three-dimensional scanner stations and multiple scanning targets; One or more three-dimensional scanner stations are used to scan one precast segment; When multiple three-dimensional scanner stations scan the same precast segment, the overlap degree of the three-dimensional space data provided by adjacent three-dimensional scanner stations is higher than a preset overlap value, and the three-dimensional space data provided by each three-dimensional scanner station includes at least three scanning targets, wherein the three scanning targets are not on the same straight line.

3. The segment dimension measurement method according to claim 2, characterized in that, Registering the measured point cloud data includes: Register the three-dimensional space data provided by each three-dimensional scanner station according to the target center coordinates to obtain initially registered point cloud data; Extract the plane features of the model point cloud data and extract the plane features of the initially registered point cloud data to obtain model plane features and measured plane features; Based on the model plane features and the measured plane features, register the initially registered point cloud data with the model point cloud data to obtain registered point cloud data.

4. The segment dimension measurement method according to claim 3, characterized in that, Registering the three-dimensional space data provided by each three-dimensional scanner station according to the target center coordinates to obtain initially registered point cloud data includes: Obtain the three-dimensional space data provided by each three-dimensional scanner station, use RANSAC spherical fitting and the least squares method to calculate the target center coordinates of each scanning target, and calculate n pairs of target center coordinates, where n is greater than or equal to 3; Wherein, when the three-dimensional space data provided by one three-dimensional scanner station and the three-dimensional space data provided by another three-dimensional scanner station have the same scanning target, the target center coordinates of a scanning target in the three-dimensional space data provided by one three-dimensional scanner station and the target center coordinates of a scanning target in the three-dimensional space data provided by another three-dimensional scanner station are a pair of target center coordinates; Register the measured point cloud data according to n pairs of target center coordinates to obtain the initially registered point cloud data.

5. The segment dimension measurement method according to claim 4, characterized in that, Obtain the three-dimensional space data provided by any three-dimensional scanner station, and use RANSAC spherical fitting and the least squares method to calculate the target center coordinates of a scanning target, including: Sa. Randomly grab point data of the target scanning target from the three-dimensional space data provided at any three-dimensional scanner site, obtain the target spherical surface based on the grabbed point data, and calculate the spherical equation of the target spherical surface; Sb. Calculate the distance from the ungrabbed point data to the target spherical surface, and compare it with a preset distance threshold to obtain a comparison result of whether the ungrabbed point data is an inlier or the ungrabbed point data is an outlier. At the same time, record the number of inliers in the comparison result based on the target spherical surface; Sc. Repeat Sa and Sb to obtain M target spherical surfaces and M comparison results based on the M target spherical surfaces; Sd. Obtain the model parameters of the m target spherical surfaces with the largest number of inliers, and perform least squares spherical fitting on the point cloud data that is an inlier among the m target spherical surfaces as the final spherical fitting coefficient. The final spherical fitting coefficient is used to calculate the target center coordinates of the target target; Where M is a positive integer, and m is a positive integer less than or equal to M.

6. The segment size measurement method according to claim 1, characterized in that, Through the model point cloud data and the registered point cloud data, perform background separation on the registered point cloud data to obtain the point cloud data to be detected of the precast segment, including: Construct the KD-Tree index of the model point cloud data and the registered point cloud data; Search in the registered point cloud data. The point data at the current search position and the point data within a preset search radius based on the current search position are the data to be matched; According to the KD-Tree index, if there is no corresponding point data in the model point cloud data for the data to be matched, mark the data to be matched as background point data.

7. The segment dimension measurement method according to claim 6, characterized in that, After marking the data to be matched as background point data, it further includes: Perform morphological dilation and clustering on all the data to be matched marked as background point data to obtain K point sets; Detect the K point sets. If it is detected that the data to be matched marked as background point data included in the kth point set is greater than a preset quantity value, delete the kth point set; otherwise, retain the kth point set; where K is a positive integer, and k is a positive integer less than or equal to K; Merge all the detected point sets as the point cloud data to be detected.

8. The segment dimension measurement method according to claim 1, characterized in that Based on the point cloud data to be detected, perform dimension measurement to obtain the actual dimension parameters of the precast segment, including: Extract edge point data based on the point cloud data to be detected; According to the edge point data, use the RANSAC line fitting method to obtain the contour line of the rectangular end face of the precast segment. The position of the intersection of adjacent contour lines is the corner point of the precast segment. Calculate the geometric parameters of the rectangular end face of the precast segment according to the corner points of the precast segment; Detect the arcs in the edge point data and the turning points based on adjacent arcs. Divide the adjacent arcs into multiple circular arcs according to the turning points, and perform single circular arc fitting on each circular arc. Among them, the multiple circular arcs form the multi-centered circular side of the precast segment; Calculate the geometric parameters of the multi-centered circular side of the precast segment according to the single circular arc fitting results of the multiple circular arcs.

9. A segment size calibration device, comprising a design size parameter acquisition module, an actual size parameter acquisition module, and a size parameter comparison module, characterized in that The actual dimension parameter acquisition module measures the target precast segment by the segment dimension measurement method according to any one of claims 1 to 8, and obtains the actual dimension parameters of the target precast segment; The design dimension parameter acquisition module is used to obtain design dimension parameters according to the segment design model; The dimension parameter comparison module is used to compare the actual dimension parameters of the target precast segment with the design dimension parameters. If the preset error value is exceeded, the target precast segment is marked as a segment to be repaired.

10. An assembled lining assembly system, comprising a segment dimension calibration device and a lining device, wherein the segment dimension calibration device includes a design dimension parameter acquisition module, an actual dimension parameter acquisition module, and a dimension parameter comparison module, characterized in that, The actual dimension parameter acquisition module measures the target precast segment by the segment dimension measurement method according to any one of claims 1 to 8, and obtains the actual dimension parameters of the target precast segment; The design dimension parameter acquisition module is used to obtain design dimension parameters according to the segment design model; The dimension parameter comparison module is used to compare the actual dimension parameters of the target precast segment with the design dimension parameters. If the preset value is exceeded, the target precast segment is marked as a segment to be repaired. Otherwise, the target precast segment is marked as a target precast segment to be assembled; The lining device includes a segment grouping module and a simulated assembly module; The segment grouping module is used to group the precast segments to be assembled according to the actual dimension parameters of all the precast segments to be assembled. Among them, all the precast segments to be assembled are the precast segments calibrated by the segment dimension calibration device. The precast segments to be assembled with dimension parameters within the same range are divided into one group for lining of the same ring; The simulated assembly module is used to simulate the assembly process based on the grouped precast segments to be assembled, and obtain the arrangement order of the precast segments to be assembled, so that the assembly equipment assembles the precast segments to be assembled according to the arrangement order.

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