Tunnel contour change detection method and device based on mobile laser scanning and storage medium

Abstract

The invention relates to a tunnel contour change detection method and device based on mobile laser scanning and a storage medium. The method comprises the steps of obtaining laser point clouds of all sections in a tunnel based on mobile laser scanning; acquiring a plurality of feature points marked based on the section point cloud at the starting point; segmenting the tunnel based on the neighborhood density, segmenting the tunnel into a plurality of tunnel segments based on each actual demarcation point, obtaining the length and contour of each tunnel segment, and combining the lengths and contours of all tunnel segments to obtain the contour of the tunnel. Compared with the prior art, the method has the advantages of improving the contour detection accuracy and speed of the tunnel with the complex shape and the like.

Description

Technical Field

[0001] The present invention relates to the field of tunnel profile detection, and in particular to a tunnel profile change detection method, device and storage medium based on mobile laser scanning. Background Art

[0002] For tunnels with complex geometric shapes, a certain section may include multiple cross-section types. In the case of missing or difficult to obtain design data, it is urgent to accurately determine the demarcation points of tunnel contour changes and further extract the contours of each segmented tunnel. This process is of great practical significance for the production of point cloud orthophotos and the reconstruction of existing tunnel models.

[0003] In the prior art, depending on the available hardware equipment and data processing methods, the commonly used technologies for identifying and extracting tunnel profile changes include measurement robot technology, photogrammetry technology and 3D laser scanning technology, as follows:

[0004] (1) The measurement robot technology first determines the location of each segment through manual inspection, and then uses the measurement robot to measure the tunnel section to obtain the tunnel profile. This technology has high accuracy, but low efficiency and high labor costs. In addition, the data obtained only represents the current local area, and the measurement results are limited.

[0005] (2) Photogrammetry technology takes photos of the interior of the tunnel and uses image processing methods to determine changes in the tunnel contour. However, this technology has high requirements for lighting, and the image data lacks depth information, making it difficult to obtain spatial geometric information, which is not conducive to extracting the tunnel contour.

[0006] (3) Three-dimensional laser scanning technology has been used in tunnel contour extraction because it can obtain high-precision, high-density three-dimensional point cloud data inside the tunnel. However, the existing methods are highly dependent on the central axis of the tunnel and are easily affected by factors such as occlusion by facilities and equipment.

[0007] In summary, the existing related technologies can improve the efficiency of tunnel contour extraction to a certain extent, but the efficiency of tunnel contour change recognition is generally not high, and tunnel contour change recognition and extraction technology still faces some challenges in practical application. Summary of the invention

[0008] The purpose of the present invention is to provide a tunnel profile change detection method, device and storage medium based on mobile laser scanning.

[0009] The purpose of the present invention can be achieved by the following technical solutions:

[0010] A tunnel profile change detection method based on mobile laser scanning, comprising:

[0011] Step S1: Acquire laser point clouds of each section in the tunnel based on mobile laser scanning;

[0012] Step S2: filtering the acquired laser point cloud;

[0013] Step S3: obtaining a plurality of feature points annotated based on the cross-section point cloud at the starting point;

[0014] Step S4: Determine whether there is a next section. If yes, calculate the neighborhood density of each feature point in the laser point cloud of the next section. Otherwise, execute step S9.

[0015] Step S5: Determine whether there is at least one feature point whose neighborhood density is less than a pre-configured threshold. If yes, execute step S6; otherwise, return to step S4;

[0016] Step S6: taking the current section as a potential demarcation point;

[0017] Step S7: Calculate the neighborhood density of each feature point in the laser point cloud of the next section, and determine whether there are still feature points whose neighborhood density is less than the pre-configured threshold. If yes, execute step S8, otherwise, return to step S4;

[0018] Step S8: Determine whether the length from the current section to the potential demarcation point exceeds a pre-configured threshold length. If yes, take the potential demarcation point as the actual demarcation point, return to the actual demarcation point and obtain multiple feature points marked based on the actual demarcation point to replace the original feature points, and return to step S4;

[0019] Step S9: dividing the tunnel into a plurality of segmented tunnel segments based on each actual demarcation point, obtaining the length and profile of each segmented tunnel, and combining the lengths and profiles of all segmented tunnels to obtain the profile of the tunnel.

[0020] The filtering method in step S2 adopts through filtering and spatial statistical filtering.

[0021] The neighborhood density of the feature point is specifically the number of laser points in the cross section whose distance from the feature point is less than a first set threshold distance.

[0022] The first set threshold distance is 20 centimeters.

[0023] The preconfigured threshold length is the maximum length of the equipment installed on the inner wall of the tunnel along the axial direction of the tunnel.

[0024] In the process of obtaining the length and profile of each segmented tunnel, the length of the segmented tunnel is calibrated based on the milepost in the tunnel.

[0025] In the process of obtaining the length and contour of each segmented tunnel, the process of obtaining the contour of the segmented tunnel specifically includes:

[0026] Step S9-1: selecting point clouds of multiple sections within a segmented tunnel section;

[0027] Step S9-2: Projecting the selected point cloud along a direction parallel to the axis of the segmented tunnel onto a plane perpendicular to the axis of the segmented tunnel to obtain a cross-sectional projection diagram;

[0028] Step S9-3: In the cross-sectional projection diagram, select any two projection points;

[0029] Step S9-4: draw two circles with a pre-configured radius through the two selected projection points. If no other projection points exist in any of the drawn circles, the two selected projection points are used as boundary points.

[0030] Step S9-5: Determine whether all projection point combinations have been traversed. If yes, execute step S9-6. Otherwise, select another two projection point combinations that have not been traversed and return to step S9-4.

[0031] Step S9-6: Connect each boundary point in sequence in a clockwise or counterclockwise direction to obtain the outline of the segmented tunnel.

[0032] In step S9-1, the selected multiple sections are distributed at equal distances.

[0033] A tunnel profile change detection device based on mobile laser scanning includes a memory, a processor, and a program stored in the memory, wherein the processor implements the above method when executing the program.

[0034] A storage medium stores a program thereon, wherein the program implements the above method when executed.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. For complex-shaped tunnels, the contour changes are judged based on the neighborhood features of the cross-section feature points, which can efficiently segment the tunnel according to the contour shape. In addition, for interference such as occlusion of facilities and equipment in the tunnel, the neighborhood radius and judgment threshold are set specifically to effectively avoid misjudgment of tunnel contour changes caused by related interference factors.

[0037] 2. Combined with the boundary extraction algorithm, the tunnel contour can be extracted without the tunnel central axis, and the point cloud projection of multiple sections in the tunnel segment is used as the basic data for contour extraction, avoiding errors that may be caused by equipment occlusion in a single section. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic flow chart of the main steps of the method of the present invention;

[0039] Figure 2 This is an example diagram for setting the first cross-section feature point;

[0040] Figure 3 This is a schematic diagram of the tunnel cross-section point cloud;

[0041] Figure 4 for Figure 3 The contour extraction result of the cross-section point cloud is shown;

[0042] Figure 5 Extract the rendering of a tunnel outline;

[0043] Figure 6 This is a local model diagram of a tunnel where the contour changes. DETAILED DESCRIPTION

[0044] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0045] A tunnel profile change detection method based on mobile laser scanning, such as Figure 1 As shown, including:

[0046] Step S1: Acquire laser point clouds of each section in the tunnel based on mobile laser scanning;

[0047] Step S2: filtering the acquired laser point cloud;

[0048] During the scanning process, due to the influence of observation conditions, the instrument itself and external environmental conditions, the collected raw point cloud data often contains noise points, so filtering is required. The filtering methods use straight-through filtering and spatial statistical filtering. Among them, straight-through filtering is used to remove noise points outside the tunnel, and its threshold parameters can be determined after a preliminary estimate of the geometric dimensions of the tunnel; spatial statistical filtering is mainly used to remove discrete noise points. Its basic principle is to perform K-neighborhood statistical analysis on each point, calculate the average distance from the point to its K neighboring points, and assume that the result obeys a Gaussian distribution. The shape of the Gaussian distribution depends on the mean and standard deviation, and remove points whose average distance is outside the given threshold range. This processing method can effectively remove noise points from the tunnel section point cloud. The specific process is as follows:

[0049] (1) Input the cross-section point cloud data to be denoised;

[0050] (2) According to the two coordinate axis directions of the cross-section coordinate system where the point cloud is located, the tunnel contour threshold is determined based on prior knowledge, and the point cloud outside the threshold is eliminated;

[0051] (3) Calculate the average distance between neighboring points in the K neighborhood of each point in the remaining point cloud;

[0052] (4) Calculate the mean and standard deviation of the average distances corresponding to each point;

[0053] (5) Taking 1 times the mean square error as the threshold, remove the points whose average distance to neighboring points is greater than the threshold to obtain the denoised point cloud. If the denoising effect is poor, the corresponding K value and threshold can be adjusted.

[0054] Step S3: obtaining a plurality of feature points annotated based on the cross-section point cloud at the starting point;

[0055] In this embodiment, the characteristic points are obtained by manual setting. Of course, in other embodiments, other calculation or data analysis methods may be used to determine the characteristic points.

[0056] Step S4: Determine whether there is a next section. If yes, calculate the neighborhood density of each feature point in the laser point cloud of the next section. Otherwise, execute step S9.

[0057] In this embodiment, the neighborhood density of the feature point is specifically the number of laser points in the cross section whose distance from the feature point is less than a first set threshold distance.

[0058] Step S5: Determine whether there is at least one feature point whose neighborhood density is less than a pre-configured threshold. If yes, execute step S6; otherwise, return to step S4;

[0059] In this embodiment, the first set threshold distance is 20 centimeters. In other embodiments, it can also be set according to actual conditions.

[0060] Step S6: taking the current section as a potential demarcation point;

[0061] Step S7: Calculate the neighborhood density of each feature point in the laser point cloud of the next section, and determine whether there are still feature points whose neighborhood density is less than the pre-configured threshold. If yes, execute step S8, otherwise, return to step S4;

[0062] Among them, the preconfigured threshold is N0, and the value of N0 depends on the point cloud density collected by the laser scanner, which is determined according to the scanner angle resolution and the distance from the scanner to the inner wall of the tunnel. The specific setting method is as follows:

[0063]

[0064] In the above formula, N0 is the preconfigured threshold position; R is the first set threshold distance, which is set to 20 cm in the present invention; D is the maximum spacing distance between two adjacent scanning points on the inner wall of the tunnel; L is the maximum distance from the scanner laser emission point to the inner wall of the tunnel; α is the angular resolution of the scanner used.

[0065] Step S8: Determine whether the length from the current section to the potential demarcation point exceeds a pre-configured threshold length. If yes, take the potential demarcation point as the actual demarcation point, return to the actual demarcation point and obtain multiple feature points marked based on the actual demarcation point to replace the original feature points, and return to step S4;

[0066] Generally, the preconfigured threshold length is the maximum length of the equipment installed on the inner wall of the tunnel along the tunnel axis, to avoid mis-segmentation caused by some equipment.

[0067] Step S9: dividing the tunnel into a plurality of segmented tunnel segments based on each actual demarcation point, obtaining the length and profile of each segmented tunnel, and combining the lengths and profiles of all segmented tunnels to obtain the profile of the tunnel.

[0068] In the process of obtaining the length and profile of each segmented tunnel, the length of the segmented tunnel is calibrated based on the milepost in the tunnel. The specific process is as follows:

[0069] In actual operation, an odometer is usually used to record the mileage of the section obtained by mobile laser scanning, and then the mileage markers in the tunnel are used for correction. The mileage correction principle is as follows:

[0070] In a shorter distance, it can be considered that the measurement error of mileage is proportional to the driving distance, that is:

[0071]

[0072] Where: S is the measurement distance, is the actual distance, k is the proportional error coefficient. During calibration, a known reference distance value is used. The error coefficient can be obtained by combining the measured value S with the above formula:

[0073]

[0074] Then the value after any distance correction is:

[0075]

[0076] For shield tunnels, we can also take advantage of the high precision and fixed width of shield segment splicing. By identifying the annular joints, we can determine the number of segments, calculate the actual distance between different annular joints based on the segment width, and then achieve mileage correction based on the mileage reference distance between the first and last annular joints.

[0077] In addition, in the process of obtaining the length and profile of each segmented tunnel, the profile obtaining process of the segmented tunnel specifically includes:

[0078] Step S9-1: selecting point clouds of multiple sections within a segmented tunnel section;

[0079] Step S9-2: Projecting the selected point cloud along a direction parallel to the axis of the segmented tunnel onto a plane perpendicular to the axis of the segmented tunnel to obtain a cross-sectional projection diagram;

[0080] Step S9-3: In the cross-sectional projection diagram, select any two projection points;

[0081] Step S9-4: draw two circles with a pre-configured radius through the two selected projection points. If no other projection points exist in any of the drawn circles, the two selected projection points are used as boundary points.

[0082] Step S9-5: Determine whether all projection point combinations have been traversed. If yes, execute step S9-6. Otherwise, select another two projection point combinations that have not been traversed and return to step S9-4.

[0083] Step S9-6: Connect each boundary point in sequence in a clockwise or counterclockwise direction to obtain the outline of the segmented tunnel.

[0084] In step S9-1, the selected multiple sections are distributed at equal distances.

[0085] In some embodiments, the boundary points of the tunnel contour are extracted using the built-in Boundary function of Matlab with the Alpha Shapes algorithm as the core, and finally the tunnel contour is obtained by sequentially connecting the boundary points. If the extraction effect is not good, the shrinkage factor of the Boundary function can be appropriately adjusted, such as Figure 3 and Figure 4 Shown is the contour extraction result.

[0086] The method was applied in a tunnel in a certain subway line section. The tunnel in this section is a mining tunnel and contains tunnel profiles of various shapes. Due to the confidentiality agreement, this document sets the start and end mileage range of the tunnel as K0+0.00-K2+331.60 according to the actual distance of the tunnel. The tunnel profile change recognition and extraction method was used to obtain 16 profiles in turn. The profiles extracted from each segmented tunnel are as follows: Figure 5 The starting and ending mileages of each segmented tunnel are shown in Table 1.

[0087] Table 1

[0088]

[0089]

[0090] The tunnel profile change recognition and extraction results of this application can be applied to reconstruct tunnel models, such as Figure 6 As shown, this is a local model of a certain contour change of the tunnel.

[0091] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the 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 method described in 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, etc., which can store program code.

Claims

1. A tunnel profile change detection method based on mobile laser scanning, characterized in that: include: Step S1: Acquire laser point clouds of each section in the tunnel based on mobile laser scanning; Step S2: filtering the acquired laser point cloud; Step S3: obtaining a plurality of feature points annotated based on the cross-section point cloud at the starting point; Step S4: Determine whether there is a next section. If yes, calculate the neighborhood density of each feature point in the laser point cloud of the next section. Otherwise, execute step S9. Step S5: Determine whether there is at least one feature point whose neighborhood density is less than a pre-configured threshold. If yes, execute step S6; otherwise, return to step S4; Step S6: taking the current section as a potential demarcation point; Step S7: Calculate the neighborhood density of each feature point in the laser point cloud of the next section, and determine whether there are still feature points whose neighborhood density is less than the pre-configured threshold. If yes, execute step S8, otherwise, return to step S4; Step S8: Determine whether the length from the current section to the potential demarcation point exceeds a pre-configured threshold length. If yes, take the potential demarcation point as the actual demarcation point, return to the actual demarcation point and obtain multiple feature points marked based on the actual demarcation point to replace the original feature points, and return to step S4; Step S9: dividing the tunnel into a plurality of segmented tunnel segments based on each actual demarcation point, obtaining the length and profile of each segmented tunnel, and combining the lengths and profiles of all segmented tunnels to obtain the profile of the tunnel.

2. The tunnel profile change detection method based on mobile laser scanning according to claim 1 is characterized in that: The filtering method in step S2 adopts through filtering and spatial statistical filtering.

3. The tunnel profile change detection method based on mobile laser scanning according to claim 1 is characterized in that: The neighborhood density of the feature point is specifically the number of laser points in the cross section whose distance from the feature point is less than a first set threshold distance.

4. The tunnel profile change detection method based on mobile laser scanning according to claim 3 is characterized in that: The first set threshold distance is 20 centimeters.

5. The tunnel profile change detection method based on mobile laser scanning according to claim 1 is characterized in that: The preconfigured threshold length is the maximum length of the equipment installed on the inner wall of the tunnel along the axial direction of the tunnel.

6. The tunnel profile change detection method based on mobile laser scanning according to claim 1 is characterized in that: In the process of obtaining the length and profile of each segmented tunnel, the length of the segmented tunnel is calibrated based on the milepost in the tunnel.

7. The tunnel profile change detection method based on mobile laser scanning according to claim 1 is characterized in that: In the process of obtaining the length and contour of each segmented tunnel, the process of obtaining the contour of the segmented tunnel specifically includes: Step S9-1: selecting point clouds of multiple sections within a segmented tunnel section; Step S9-2: Projecting the selected point cloud along a direction parallel to the axis of the segmented tunnel onto a plane perpendicular to the axis of the segmented tunnel to obtain a cross-sectional projection diagram; Step S9-3: In the cross-sectional projection diagram, select any two projection points; Step S9-4: draw two circles with a pre-configured radius through the two selected projection points. If no other projection points exist in any of the drawn circles, the two selected projection points are used as boundary points. Step S9-5: Determine whether all projection point combinations have been traversed. If yes, execute step S9-6. Otherwise, select another two projection point combinations that have not been traversed and return to step S9-4. Step S9-6: Connect each boundary point in sequence in a clockwise or counterclockwise direction to obtain the outline of the segmented tunnel.

8. The method for detecting tunnel profile changes based on mobile laser scanning according to claim 7, characterized in that: In step S9-1, the selected multiple sections are distributed at equal distances.

9. A tunnel profile change detection device based on mobile laser scanning, comprising a memory, a processor, and a program stored in the memory, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

10. A storage medium having a program stored thereon, characterized in that: When the program is executed, the method according to any one of claims 1 to 8 is implemented.

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