Method for measuring section limit of second lining of subway tunnel based on SLAM mobile scanning

Abstract

The present application relates to the field of measurement, specifically relates to a kind of subway tunnel second lining section limit measurement method based on SLAM mobile scanning, comprising steps 1: tunnel point cloud acquisition and target clustering point cloud acquisition;Step 2: CPIII control point coordinate extraction;Step 3: point cloud coordinate conversion;Step 4: point cloud processing, eliminate the noise in tunnel second lining point cloud and non-second lining target point cloud data;Step 5: three-dimensional limit deviation analysis, using voxel deviation mapping algorithm, point cloud under construction coordinate system and subway second lining tunnel design three-dimensional model are compared and analyzed in space, through voxel clustering, continuous deviation area is extracted, and three-dimensional deviation heat map is generated.Combined with the high-precision control point of CPIII in tunnel, the second lining tunnel point cloud obtained by SLAM mobile scanning is accurately converted to the construction coordinate system, which can realize the fast, intuitive and three-dimensional visual comparison and analysis with the design three-dimensional model, and provide intuitive and reliable data support for line adjustment, track laying and defect treatment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of metro tunnel secondary lining section limit measurement, in particular to a metro tunnel secondary lining section limit measurement method based on SLAM mobile scanning. BACKGROUND

[0002] After the secondary lining work of the metro tunnel (after the secondary lining of the mine method or the completion of the shield / TBM segment assembly), the secondary lining section needs to be measured to calculate and analyze the deviation between the actual profile and the design profile, and to extract the section area with large deviation, so as to provide a basis for line and slope adjustment, that is, if the deviation meets the requirements, the track can be laid according to the original design, and if the deviation exceeds the allowable value, the design track alignment needs to be adjusted according to the actual excavation to meet the smoothness of track laying. The measurement methods commonly used in the industry include two methods, the first method is to use a section meter or a total station to measure a single point, that is, to collect multiple spatial point information of a section at an interval of every 5 m along the tunnel alignment, to sequentially connect the points collected on each section to generate a measured section, and to compare it with the design section, this method is low in efficiency and easy to miss the out-of-limit section; the second method is to use a station-mounted scanner to obtain point cloud information of the tunnel based on the method of "single station scanning + multi-station splicing", and then to extract section point cloud data at a certain mileage interval and compare it with the design section, this method needs to set up targets and move stations, which is time-consuming, and the calculation, analysis and result display are all in two-dimensional form, which lacks intuitiveness. With the compression of the metro construction period and the improvement of the requirement for three-dimensional visualization, the above methods have been difficult to meet the detection requirements of "fast, full section, high precision and true three-dimensional".

[0003] In recent years, handheld three-dimensional laser scanners based on SLAM algorithm have shown advantages such as no need for GNSS, no need for leveling, scanning while walking, and continuous data in space measurement. In order to solve the above problems, the present application proposes a method of collecting metro tunnel secondary lining point cloud based on SLAM handheld mobile scanning, obtaining point cloud data in the construction coordinate system combined with CPIII control point coordinate information, and finally comparing and analyzing with the design three-dimensional model to obtain the three-dimensional metro tunnel secondary lining section limit result. SUMMARY

[0004] In view of the problems of low measurement efficiency, single data form, weak spatial continuity, and insufficient intuitive results in the prior art, the purpose of the present application is to provide a metro tunnel secondary lining section limit measurement method based on SLAM mobile scanning, which uses a handheld three-dimensional laser scanner based on SLAM principle for walking scanning, obtains tunnel secondary lining point cloud in the construction coordinate system according to CPIII control points, and realizes three-dimensional continuous measurement and deviation area extraction of the tunnel secondary lining section combined with noise elimination algorithm and spatial deviation analysis algorithm, to provide intuitive three-dimensional results.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a method for measuring the clearance of secondary lining sections of subway tunnels based on SLAM moving scan, comprising the following steps:

[0006] Step 1: Tunnel point cloud acquisition and target clustering point cloud acquisition. A high-reflectivity circular black and white target is used instead of the prism used for total station measurement. It is inserted into the connecting rod of the CPIII control point and the travel speed is controlled. A 3D laser scanner is used to acquire the 3D point cloud of the inner wall of the secondary lining of the tunnel in real time, and the target forms a high-density, high-reflectivity clustered point cloud in the point cloud.

[0007] Step 2: Extract CPIII control point coordinates, perform planar fitting on the clustered point cloud, and identify and fit two straight lines on the black and white target that represent the boundary between the black and white regions. The intersection of the two straight lines is used as the three-dimensional coordinate value of the target center.

[0008] Step 3: Point cloud coordinate transformation. Using the coordinates of the CPIII control points as the control reference, the moving scan point cloud is registered to the absolute coordinate system to obtain the tunnel secondary lining point cloud in the construction coordinate system.

[0009] Step 4: Point cloud processing, combining automated filtering algorithms with manual intervention, to remove noise and non-secondary lining target point cloud data from the tunnel secondary lining point cloud;

[0010] Step 5: Three-dimensional boundary deviation analysis. Using the voxelized deviation mapping algorithm, a spatial comparison analysis is performed between the point cloud in the construction coordinate system and the three-dimensional design model of the subway secondary lining tunnel. Continuous deviation regions are extracted through voxel clustering to generate a three-dimensional deviation heat map.

[0011] The above-mentioned method for measuring the clearance of secondary lining sections of subway tunnels based on SLAM moving scan, step 1 includes:

[0012] Step 1-1: Collect CPIII control point data. After the construction of the secondary lining of the subway tunnel, CPIII control points are set up in pairs every 60 m to 120 m along the tunnel direction. The three-dimensional coordinates of the CPIII control points are determined by the free stationing method of total station.

[0013] Steps 1-2: Field SLAM point cloud scanning. Adjust the target angle and use a handheld SLAM 3D laser scanner to walk at a constant speed along the tunnel. The scanner acquires the 3D point cloud of the inner wall of the tunnel lining in real time. During the walking process, pause every time you pass the CPIII control point to make the target form a high-density, high-reflectivity clustered point cloud in the point cloud.

[0014] The target surface is divided into four right-angle fan-shaped areas, and the interval is set as white with high reflectivity and black with low reflectivity, the boundary line between the black area and the white area is two mutually perpendicular straight lines, the intersection point of the two straight lines is the target center, and the center of the target is the same as the center of the total station standard prism.

[0015] The SLAM mobile scanning-based metro tunnel secondary lining section limit measurement method, the step 2 comprises:

[0016] Step 2-1: Intensity filtering is performed on the SLAM point cloud, a value higher than the background environment intensity but lower than the white target area intensity is set as a threshold, point cloud clusters with a reflection intensity higher than the set threshold are retained, a Euclidean clustering algorithm is used to cluster the high-intensity point cloud, and point cloud clusters corresponding to each target are identified;

[0017] Step 2-2: Plane fitting is performed on each clustered point cloud, a RANSAC algorithm is used to fit the best plane model, and the fitted plane equation is: wherein, is a plane normal vector, is a constant term, and (x, y, z) represents the spatial three-dimensional coordinates of any point on the plane;

[0018] Step 2-3: Further projection is performed on the clustered point cloud on the fitted plane, the three-dimensional point cloud is projected onto the fitted plane, and is converted into a two-dimensional point set, the intensity value of each point is retained during the projection, and in the two-dimensional point set after the projection, each target point cloud is divided into two subsets: a high-intensity point set and a low-intensity point set based on the intensity value, and the boundary points between the high-intensity area and the low-intensity area are identified based on a preset intensity threshold;

[0019] Step 2-4: Straight line fitting algorithm is used to fit two straight lines and representing the boundary between the black and white areas, the straight line equation is: and the straight line equation is: wherein, represents a slope, , represents a constant;

[0020] Step 2-4: The coordinates of the target center on the two-dimensional plane are the intersection points of the two fitted straight lines, and the equation group is solved: and the center coordinates are obtained: The calculated two-dimensional circle center coordinates are back-projected to the space plane fitted by the formula of the plane equation to obtain the coordinates of the target circle center in the original SLAM point cloud relative to the coordinate system.

[0021] The above subway tunnel secondary lining section limit measurement method based on SLAM mobile scanning, in step 3, the coordinates of N CPIII control points in the construction coordinate system measured by the total station in step 1 are , the corresponding target center coordinates fitted by the SLAM point cloud in step 2 are , and a rigid transformation is solved through the corresponding point pairs, so that the SLAM point cloud in the relative coordinate system is transformed into the construction coordinate system.

[0022] The above subway tunnel secondary lining section limit measurement method based on SLAM mobile scanning, the step 3 comprises:

[0023] Step 3-1: Calculate the center of two point sets: , , wherein represents the center coordinates of the target point set in the SLAM point cloud, represents the center coordinates of the target point set in the construction coordinate system.

[0024] Step 3-2: Calculate the decentered point set: , , wherein represents the new target center coordinate point set formed after decentering in the SLAM point cloud, represents the new target center coordinate point set formed after decentering in the construction coordinate system.

[0025] Step 3-3: Construct the covariance matrix: , the SVD decomposition of the covariance matrix H is: , the rotation matrix R is: , and the translation vector t is: ;

[0026] Step 3-4: For each point P in the SLAM point cloud, the coordinates Q in the construction coordinate system are: .

[0027] The above subway tunnel secondary lining section limit measurement method based on SLAM mobile scanning, in the step 4, a statistical outlier rejection algorithm is used to remove discrete noise points in the point cloud, and the average distance of each point in the point cloud to the nearest neighbor point is calculated : , if the greater than a preset threshold If so, the point is determined to be an outlier and is removed, representing a neighboring point of the point;

[0028] For large objects in the point cloud that do not obviously belong to the secondary lining structure, the box selection deletion is performed in the point cloud software through manual interaction, or the points deviating from the design model by more than a set threshold are regarded as noise and removed.

[0029] The subway tunnel secondary lining section limit measurement method based on SLAM mobile scanning described above, the step 5 comprises:

[0030] Step 5-1: voxelizing the point cloud and the design model into a voxel grid with the same resolution, and setting the voxelized grid point sets of the measured point cloud and the design model as and ;

[0031] Step 5-2: for any voxel grid in the measured point cloud, the design model voxel grid is searched until the design model voxel grid is searched, and the center minimum distance between the voxel grids and is obtained as the deviation value of the measured point cloud and the design model at the voxel grid, a deviation threshold G_max is set, and when the deviation value is greater than G_max, the voxel grid is marked as a deviation region;

[0032] Step 5-3: the continuous deviation region is extracted by voxel clustering, and a three-dimensional deviation heat map is generated.

[0033] The subway tunnel secondary lining section limit measurement method based on SLAM mobile scanning has the beneficial effects that: the subway secondary lining tunnel is continuously scanned by the handheld SLAM three-dimensional laser scanner, the subway secondary lining tunnel point cloud in the construction coordinate system is obtained according to the CPIII control points in the tunnel, the three-dimensional design model is combined, the subway secondary lining tunnel limit condition is analyzed and calculated, the data support for subway line adjustment and slope adjustment and track laying is provided, and compared with the traditional limit measurement method, the method can realize single-person operation, the field efficiency is significantly improved, the achievement breaks through the limitation of the traditional "two-dimensional section", the three-dimensional limit measurement achievement is provided, and the design, construction and operation are convenient for multi-party collaborative decision-making BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 It is a CPIII control point data acquisition site CPIII point picture of the application;

[0035] Figure 2 The field CPIII point picture for the field SLAM point cloud scanning of the application. DETAILED DESCRIPTION

[0036] In order to make the technical personnel in the art better understand the technical solutions of the application, the technical solutions of the application will be described below in combination with specific embodiments and drawings.

[0037] Embodiment 1

[0038] In view of the narrow space characteristics of the subway tunnel, and the complex point cloud denoising and low coordinate conversion accuracy, this embodiment proposes a set of point cloud denoising, control point high-precision extraction and coordinate registration method suitable for tunnel environment.

[0039] The SLAM (Simultaneous Localization and Mapping) mobile three-dimensional laser scanning subway tunnel secondary lining section limit measurement method is used, combined with the CPIII high-precision control points in the tunnel, to accurately obtain the secondary lining tunnel point cloud data in the construction coordinate system, and the fast, intuitive and three-dimensional visual comparison and analysis with the design three-dimensional model can be realized, thereby providing intuitive and reliable data support for line and slope adjustment, track laying and defect treatment.

[0040] Specifically, a kind of subway tunnel secondary lining section limit measurement method based on SLAM mobile scanning, at least contains the following steps:

[0041] 1. CPIII control point data acquisition. After the secondary lining of the subway tunnel is constructed, the CPIII control points are usually arranged in pairs along the tunnel trend every 60 m to 120 m, and the three-dimensional coordinates thereof are measured by using the free station method of the total station.

[0042] 2. Field SLAM point cloud scanning. Before operation, a high-reflectivity circular black-and-white target is used instead of the prism used for total station measurement, which is inserted into the connecting rod of the CPIII control point. A handheld SLAM three-dimensional laser scanner is used to scan the tunnel secondary lining inner wall three-dimensional point cloud at a uniform speed along the tunnel. During the walking process, a short pause is made every time a CPIII control point is passed, so that the target forms a high-density, high-reflectivity cluster in the point cloud.

[0043] 3. CPIII control point coordinate extraction. The cluster point cloud is fitted, and two straight lines representing the boundary between the black and white areas on the black and white target are identified and fitted. The intersection of the two straight lines is taken as the three-dimensional coordinate value of the target center.

[0044] 4. Point cloud coordinate conversion. The CPIII control point coordinates are taken as the control reference, the mobile scanning point cloud is registered to the absolute coordinate system, and the tunnel secondary lining point cloud in the construction coordinate system is obtained.

[0045] 5. SLAM point cloud processing. In combination with automated filtering algorithm and manual intervention, remove noise points and non-tunnel lining target point cloud data in tunnel lining point cloud.

[0046] 6. Three-dimensional limit deviation analysis. Using voxelized deviation mapping algorithm, compare the point cloud in construction coordinate system with the three-dimensional model of metro tunnel lining design in space. The measured point cloud and the design model are voxelized into the same resolution voxel grid (such as 0.01m×0.01m×0.01m); for any voxel grid in the measured point cloud , search the design model voxel grid until the design model voxel grid is found, so that the center distance between the voxel grids and is minimized. The minimum distance is the deviation value of the measured point cloud and the design model at the voxel grid . Set the deviation threshold G_max. When the deviation value is greater than G_max, mark the voxel as a deviation area. Finally, extract the continuous deviation area through voxel clustering and generate a three-dimensional deviation heat map. This algorithm innovatively introduces voxel grid deviation analysis algorithm, realizes full-tunnel continuous three-dimensional deviation analysis, and avoids the limitations of traditional discrete sections.

[0047] Example 2

[0048] A metro tunnel lining section limit measurement method based on SLAM mobile scanning, comprising the following steps.

[0049] Step 1: CPIII control point data acquisition. After the construction of the metro tunnel lining, the CPIII control points are usually arranged in pairs along the tunnel direction every 60 m to 120 m, as shown in Figure 1 . The field CPIII point is inserted by a connecting rod during measurement. The other end of the connecting rod is connected to the standard prism of the total station. The three-dimensional coordinates are measured by using the total station free setting method. A total of N CPIII points are measured.

[0050] Step 2: Field SLAM point cloud scanning. Before scanning, the end of the CPIII control point connecting rod is inserted into the scanning high-reflectivity circular black-and-white target. The target surface is divided into four right-angle sector areas, of which the opposite two sectors are high-reflectivity white, and the other two are low-reflectivity black. The boundary between the black and white areas is two mutually perpendicular straight lines, and the intersection point is the target center, as shown in Figure 2As shown. The center of the black and white target is in the same spatial position as the center of the total station's standard prism, meaning the 3D coordinates of the prism obtained in step 1 are the 3D coordinates of the center of the circular black and white target. Adjust the target angle to ensure that the handheld scanner can identify targets at an angle while walking. Using a handheld SLAM scanner integrating a 32-line LiDAR, IMU, and panoramic camera, walk slowly and uniformly along the tunnel, and the scanner acquires the 3D point cloud of the tunnel wall in real time. During the walk, pause briefly every time a pair of CPIII control points are passed to allow the target to form a high-density, high-reflectivity cluster in the point cloud.

[0051] Step 3: CPIII Control Point Coordinate Extraction. First, intensity filtering is applied to the SLAM point cloud, retaining point cloud clusters with reflection intensity higher than a set threshold. This threshold should be set to a value higher than the background environment intensity but lower than the intensity of the white target area. Next, Euclidean clustering algorithm is used to cluster the high-intensity point cloud, identifying the point cloud cluster corresponding to each target.

[0052] For each cluster of point clouds, a plane fit is performed, and the RANSAC (Random Sample Consensus) algorithm is used to fit the best plane model. Let the fitted plane equation be: (1), In equation (1), It is a plane normal vector. Let (x, y, z) be a constant term, and let (x, y, z) represent the three-dimensional spatial coordinates of any point on this plane.

[0053] On the fitted plane, the clustered point cloud is further projected, transforming the 3D point cloud into a 2D point set by projecting it onto the fitted plane, while retaining the intensity value of each point during the projection process. On the projected 2D point set, each target point cloud is divided into two subsets based on its intensity value: a high-intensity point set and a low-intensity point set. (Corresponding to the white area) and low-intensity point set (corresponding to the black area), and classify based on a preset intensity threshold to identify the boundary points between high-intensity and low-intensity areas.

[0054] For the identified boundary points, a straight line fitting algorithm is used to fit two straight lines representing the boundary between the black and white regions. and .straight line The equation is: ,straight line The equation is: ,in, , Indicates the slope. , Represents a constant.

[0055] Considering the orthogonal nature of the target design, these two lines should satisfy a perpendicular relationship, that is... This constraint is added in the fitting process to improve the accuracy and robustness of the fitting. , and , denote the parameters of the two plane linear equations, , The geometric meaning of is the "inclination degree" of the straight line relative to the positive direction of the x-axis, , The geometric meaning of is the y-coordinate of the intersection of the straight line with the y-axis, xy represents the variable of the equation, and has universality, all of which represent unknown numbers in the equation, which here represents the plane coordinates of any point on the plane linear equation.

[0056] The coordinates of the target center in the two-dimensional plane The intersection point of the two fitted straight lines is (2),

[0057] The center coordinates of the circle can be obtained:

[0058] (3).

[0059] The calculated two-dimensional center coordinates are back-projected back to the space plane fitted by formula (1), thereby obtaining the coordinates in the original SLAM point cloud relative to the coordinate system.

[0060] Step 4: Point cloud coordinate conversion. Let the coordinates of N CPIII control points in the construction coordinate system measured by the total station in step 1 be , and the corresponding target center coordinates obtained by step 3 in the SLAM point cloud fitting be , and through these corresponding point pairs, an optimal rigid body transformation (rotation matrix R and translation vector t) is solved, so that the entire SLAM point cloud in the relative coordinate system is transformed into the construction coordinate system.

[0061] First, calculate the centers of the two point sets: , (4), where denotes the center coordinates of the target point set in the SLAM point cloud, denotes the center coordinates of the target point set in the construction coordinate system.

[0062] Calculate the decentered point set: , (5), where denotes the new target center coordinate point set formed after decentering in the SLAM point cloud, denotes the new target center coordinate point set formed after decentering in the construction coordinate system, that is: denotes and respectively represent the new target center coordinate point set formed after the subtraction of each target center coordinate from the point set center coordinate in the SLAM point cloud relative coordinate system and the construction coordinate system, respectively.

[0063] Construct the covariance matrix: (6).

[0064] SVD decomposition is performed on H: (7).

[0065] The rotation matrix R is: (8).

[0066] The translation vector t is: (9), where U, V, and Σ are matrices after singular value decomposition (SVD) of the covariance matrix H, U and V are orthogonal matrices, and Σ is a diagonal matrix, and are the transpose matrices of the corresponding matrices.

[0067] For each point P in the SLAM point cloud, the coordinates Q in the construction coordinate system are: (10).

[0068] Step 5: SLAM point cloud processing. Comprehensive use of automatic filtering algorithm and manual intervention method, eliminate the noise points in the tunnel secondary lining point cloud and non-secondary lining target point cloud data.

[0069] Statistical outlier elimination algorithm is used to remove discrete noise points in the point cloud. For each point in the point cloud, the average distance to the nearest neighbor points is calculated: (11), where represents the neighbor point, i.e., any one of the nearest k points to point . If the

[0070] of a point is greater than the pre-set threshold , the point is determined to be an outlier and is removed. The threshold is usually set to 1 cm.

[0071] For large objects in the point cloud that are obviously not part of the secondary lining structure (such as construction equipment, pipelines, temporary supports, etc.), a manual interactive method is used to frame and delete in the point cloud software, or a filtering method based on the design section model is used to remove points that deviate from the design model by a certain threshold.

[0072] ​Step 6: Three-dimensional limit deviation analysis. Using the voxelized deviation mapping algorithm, the point cloud in the construction coordinate system is compared with the three-dimensional model of the subway second lining tunnel design. First, the point cloud and the design model are voxelized into the same resolution voxel grid (such as 0.01m x 0.01m x 0.01m). The voxel grid of the measured point cloud and the design model after voxelization is set as and For any voxel grid in the measured point cloud, search the design model voxel grid until the design model voxel grid is found, so that the center distance between the voxel grids and is minimized. The minimum distance is the deviation value of the measured point cloud and the design model at the voxel grid , (12).

[0073] Set the deviation threshold G_max, which is usually set to 5-8cm, and in this embodiment it is set to 5cm. When the deviation value is greater than G_max, mark the voxel as a deviation area. Finally, extract the continuous deviation area by voxel clustering and generate a three-dimensional deviation heat map.

[0074] Cluster analysis is performed on the voxel grid marked as a deviation area. Using a connectivity-based clustering algorithm, voxels with adjacent spatial positions and deviation values exceeding the threshold G_max are aggregated into a continuous deviation area. Each cluster area represents a tunnel section with significant deviation between the actual construction profile and the design model.

[0075] According to the deviation value of each voxel grid, a corresponding color value is assigned. The larger the deviation, the deeper the color (such as dark red), and the smaller the deviation, the lighter the color (such as light blue), forming a three-dimensional heat map represented by color gradient. This heat map can be superimposed on the original point cloud or design model for visual display, intuitively reflecting the spatial distribution of the tunnel limit deviation.

[0076] In the three-dimensional deviation heat map, the deviation distribution in the entire tunnel space is visually displayed through color gradient (such as from blue representing small deviation to red representing large deviation), helping engineers quickly identify the location, range, and severity of the out-of-limit section, providing reliable decision-making basis for metro tunnel alignment and slope adjustment, track laying, and defect remediation, avoiding the limitations of traditional two-dimensional analysis, and achieving more efficient three-dimensional spatial evaluation

[0077] This algorithm innovatively introduces the voxel grid deviation analysis algorithm, achieving continuous three-dimensional deviation analysis of the entire tunnel and avoiding the limitations of traditional discrete sections.

[0078] The field efficiency of the present application is significantly better than the prior art subway tunnel secondary lining section limit measurement method, which can be completed by a single instrument and a single person. The three-dimensional spatial overall comparison algorithm is used to analyze the spatial difference between the measured point cloud and the design model, identify and output the three-dimensional results of the over-limit section, and replace the traditional "two-dimensional section + mileage list" mode.

[0079] The above examples are only for illustrating the structural concept and characteristics of the present application, and the purpose is to enable those skilled in the art to understand the content of the present application and implement it, and cannot limit the protection scope of the present application. Any equivalent changes or modifications made according to the essence of the present application should be covered within the protection scope of the present application.

Claims

1. A method for measuring the clearance of secondary lining sections of subway tunnels based on SLAM moving scan, characterized in that, Includes the following steps: Step 1: Tunnel point cloud acquisition and target clustering point cloud acquisition. A high-reflectivity circular black and white target is used instead of the prism used for total station measurement. It is inserted into the connecting rod of the CPIII control point and the travel speed is controlled. A 3D laser scanner is used to acquire the 3D point cloud of the inner wall of the secondary lining of the tunnel in real time, and the target forms a high-density, high-reflectivity clustered point cloud in the point cloud. Step 2: Extract CPIII control point coordinates, perform planar fitting on the clustered point cloud, and identify and fit two straight lines on the black and white target that represent the boundary between the black and white regions. The intersection of the two straight lines is used as the three-dimensional coordinate value of the target center. Step 3: Point cloud coordinate transformation. Using the coordinates of the CPIII control points as the control reference, the moving scan point cloud is registered to the absolute coordinate system to obtain the tunnel secondary lining point cloud in the construction coordinate system. Step 4: Point cloud processing, combining automated filtering algorithms with manual intervention, to remove noise and non-secondary lining target point cloud data from the tunnel secondary lining point cloud; Step 5: Three-dimensional boundary deviation analysis. Using the voxelized deviation mapping algorithm, a spatial comparison analysis is performed between the point cloud in the construction coordinate system and the three-dimensional design model of the subway secondary lining tunnel. Continuous deviation regions are extracted through voxel clustering to generate a three-dimensional deviation heat map.

2. The method for measuring the clearance of secondary lining sections of subway tunnels based on SLAM moving scan according to claim 1, characterized in that, Step 1 includes: Step 1-1: Collect CPIII control point data. After the construction of the secondary lining of the subway tunnel, CPIII control points are set up in pairs every 60 m to 120 m along the tunnel direction. The three-dimensional coordinates of the CPIII control points are determined by the free stationing method of total station. Steps 1-2: Field SLAM point cloud scanning. Adjust the target angle and use a handheld SLAM 3D laser scanner to walk at a constant speed along the tunnel. The scanner acquires the 3D point cloud of the inner wall of the tunnel lining in real time. During the walking process, pause every time you pass the CPIII control point to make the target form a high-density, high-reflectivity clustered point cloud in the point cloud.

3. The method for measuring the clearance of secondary lining sections of subway tunnels based on SLAM moving scan according to claim 2, characterized in that, The target surface is divided into four right-angled sector regions, with the intervals set as high-reflectivity white and low-reflectivity black. The dividing lines between the black and white regions are two mutually perpendicular straight lines, and their intersection is the target center. The center of the target is in the same spatial position as the center of the total station's standard prism.

4. The method for measuring the clearance of secondary lining sections of subway tunnels based on SLAM moving scan according to claim 1, characterized in that, Step 2 includes: Step 2-1: Perform intensity filtering on the SLAM point cloud, set the value of the intensity higher than the background environment but lower than the intensity of the white target area as the threshold, retain the point cloud clusters with reflection intensity higher than the set threshold, and use the Euclidean clustering algorithm to cluster the high-intensity point cloud to identify the point cloud cluster corresponding to each target. Step 2-2: Perform plane fitting for each cluster point cloud, and use the RANSAC algorithm to fit the best plane model. Let the fitted plane equation be: ,in, It is a plane normal vector. Let (x, y, z) be a constant term, representing the three-dimensional spatial coordinates of any point on this plane; Steps 2-3: Further project the clustered point cloud onto the fitted plane, transforming the 3D point cloud into a 2D point set. During projection, retain the intensity value of each point. Based on the intensity values, divide each target point cloud into two subsets: a high-intensity point set and a low-intensity point set. and low-intensity point sets It classifies based on a preset intensity threshold and identifies the boundary points between high-intensity and low-intensity regions; Steps 2-4: For the identified boundary points, use a straight line fitting algorithm to fit two straight lines representing the boundary between the black and white regions. and ,straight line The equation is: ,straight line The equation is: ,in, , Indicates the slope. , Represents a constant; Steps 2-4: Coordinates of the target center on the two-dimensional plane That is, the intersection point of the two fitted lines. Solve the system of equations: The coordinates of the center of the circle can be obtained as follows: The calculated two-dimensional center coordinates are back-projected back onto the spatial plane fitted by the formula of the plane equation to obtain the coordinates of the target center in the relative coordinate system in the original SLAM scan point cloud.

5. The method for measuring the clearance of secondary lining sections of subway tunnels based on SLAM moving scan according to claim 1, characterized in that, In step 3, let the coordinates of the N CPIII control points in the construction coordinate system measured by the total station in step 1 be... The target center coordinates obtained by fitting the SLAM point cloud in step 2 are: By solving a rigid body transformation through corresponding point pairs, the SLAM point cloud in the relative coordinate system is transformed into the construction coordinate system.

6. The method for measuring the clearance of secondary lining sections of subway tunnels based on SLAM moving scan according to claim 4, characterized in that, Step 3 includes: Step 3-1: Calculate the centers of the two point sets: , ,in, This represents the center coordinates of the target point set in the SLAM point cloud. Indicates the center coordinates of the target point set under the construction coordinate system; Step 3-2: Calculate the decentralized point set: , ,in, This represents the new set of target center coordinates formed after decentralization in the SLAM point cloud. This represents the new set of target center coordinate points formed after decentering the construction coordinate system; Step 3-3: Construct the covariance matrix: Perform SVD decomposition on the covariance matrix H: The rotation matrix R is obtained as follows: The translation vector t is: ; Steps 3-4: For each point P in the SLAM point cloud, its coordinates Q in the construction coordinate system are: .

7. The method for measuring the clearance of secondary lining sections of subway tunnels based on SLAM moving scan according to claim 1, characterized in that, In step 4, a statistical outlier removal algorithm is used to remove discrete noise points from the point cloud, and the algorithm is used to calculate the number of outliers for each point in the point cloud. Until recently Average distance of neighboring points : If a certain point Greater than the preset threshold If the point is an outlier, it is identified as an outlier and removed from the list. express The nearest point; For large objects in the point cloud that are clearly not part of the secondary lining structure, they can be manually selected and deleted in the point cloud software, or filtered based on the design cross-section model, and points that deviate from the design model beyond a set threshold can be treated as noise and removed.

8. The method for measuring the clearance of secondary lining sections of subway tunnels based on SLAM moving scan according to claim 1, characterized in that, Step 5 includes: Step 5-1: Convert the point cloud and the design model into voxel meshes of the same resolution. Let the point sets of the measured point cloud and the voxelized mesh of the design model be respectively... and ; Step 5-2: For any voxel grid in the measured point cloud The process iterates through the voxelized mesh of the design model until the voxel mesh of the design model is found. Obtain a voxel mesh and minimum distance between centers This represents the deviation between the measured point cloud at the voxel mesh and the design model. Set a deviation threshold G_max. When the deviation value is greater than G_max, mark the voxel mesh. This is the deviation area; Step 5-3: Extract continuous deviation regions through voxel clustering and generate a three-dimensional deviation heatmap.

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