CN112923869A - Subway tunnel clearance detection method based on point cloud - Google Patents
Subway tunnel clearance detection method based on point cloud Download PDFInfo
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- CN112923869A CN112923869A CN202110146367.4A CN202110146367A CN112923869A CN 112923869 A CN112923869 A CN 112923869A CN 202110146367 A CN202110146367 A CN 202110146367A CN 112923869 A CN112923869 A CN 112923869A
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- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
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Abstract
The invention relates to a subway tunnel clearance detection method based on point cloud, which uses three-dimensional laser scanning to obtain tunnel point cloud data and extracts or guides a tunnel rail surface central line; making contour lines and storing the contour lines as files with a specified format, such as carriage contour, equipment contour, building contour and the like; and (3) importing contour lines, matching the contour lines with point cloud ranges, calculating the postures of the contour lines at all positions by taking the center of the rail surface as a rotation center and the rotation angle of the rail surface as a rotation angle, and judging the relative position relation between the contour lines and the point cloud on the surface of the tunnel. The method can set the mileage interval of the detection section, customize the detection interval, analyze the tunnel point cloud limit detection state in batch, and assign different limit detection profiles for different mileage.
Description
Technical Field
The invention relates to the technical field of tunnel structure construction detection, in particular to a subway tunnel clearance detection method based on point cloud.
Background
The subway limit refers to a contour line for limiting subway operation and exceeding of facilities around a rail and is divided into a vehicle limit, an equipment limit and a building limit. In order to ensure the safe operation of the train in the tunnel, enough space is required in the tunnel; within the delimitation, other facilities than the subway and the equipment interacting with it must not intrude at all. Therefore, monitoring and analyzing the subway limit is an important work related to safe operation of the subway. There is therefore a need for a support guidance clearance, such as the outer contour of the cabin; different limits are set in different mileage ranges, and flexible allocation is realized; the tunnel limit detection method supports the generation of limit detection results by outputting different specified mileage or mileage intervals.
Disclosure of Invention
The invention aims to provide a subway tunnel clearance detection method based on point cloud, which can automatically detect the relative position relation between a contour line and tunnel surface point cloud within a specified mileage or mileage interval range, complete whole-line clearance detection, realize two-dimensional view display of an analysis result and support the output of a clearance detection result table of the detection result.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a subway tunnel clearance detection method based on point cloud is characterized in that: the method comprises the following specific steps:
step 5, judging the position relation of each point and the contour line at set intervals according to a ray method, wherein the intrusion is determined in the detection contour line;
step 6, displaying a limit detection result in a 2D view;
and 7, exporting the detection charts in batches.
And (3) scanning the point cloud data on the surface of the tunnel in the step 1 in the tunnel by using a three-dimensional laser scanning device.
In the step 5, whether the point cloud in each mileage interval is in the detection contour line is calculated through a ray method, and the method specifically comprises the following steps:
extending a tunnel surface point to be judged to one side of the tunnel to form a ray, wherein the number of intersection points of the ray and a contour line in the cross section of the tunnel structure where the ray is located is n, and when n is an even number, the point is not in the contour line, namely no invasion limit is formed; when n is odd, the boundary is in the contour, i.e. there is an intrusion limit.
And 5, the extending direction of the ray is parallel to the rail surface of the rail in the tunnel, and the extending direction of the ray is also vertical to the central line of the rail surface of the rail.
Step 6, marking the intrusion limit angle and length in the corresponding result graph at the position where the intrusion limit is generated in the limit detection result; locations that do not produce an infringement limit are marked in the corresponding outcome map with the location and distance closest to the contour.
The subway tunnel clearance detection method based on point cloud has the following beneficial effects that: the method can analyze mass point cloud data without intercepting the tunnel section, and saves the time for extracting the data. And during operation, the boundary invasion condition can be detected by designating any mileage or mileage interval, the boundary invasion angle and length are displayed, and the position and distance closest to the outline are displayed if the boundary invasion is not detected. And the import limit is supported, and the invasion calculation boundaries of different vehicle types, carriage outer contours, equipment contours or building equipment contours and the like are matched in different mileage intervals.
Drawings
Fig. 1 is a schematic diagram of a boundary violation detection result when no boundary violation occurs in the point cloud-based subway tunnel boundary detection method.
Fig. 2 is a schematic diagram of a boundary violation detection result when a boundary violation occurs in the point cloud-based subway tunnel boundary detection method.
Fig. 3 is a detection schematic diagram of a ray method in the subway tunnel clearance detection method based on point cloud.
Detailed Description
The invention is further described below with reference to the drawings and specific preferred embodiments.
A subway tunnel clearance detection method based on point cloud is characterized in that: the method comprises the following specific steps:
step 5, judging the position relation of each point and the contour line at set intervals according to a ray method, wherein the intrusion is determined in the detection contour line;
step 6, displaying a limit detection result in a 2D view;
and 7, exporting the detection charts in batches.
In this embodiment, in step 1, the point cloud data on the surface of the tunnel is obtained by scanning in the tunnel through a three-dimensional laser scanning device. The point cloud of the tunnel structure can be acquired by adopting a static station scanning and target splicing mode, the point cloud of the tunnel structure can also be acquired by adopting a static station scanning and mobile scanning mode, and the rail surface central line data in the step 4 can be directly imported or extracted.
In this embodiment, as shown in fig. 3, in step 5, whether the point cloud in each mileage interval is within the detection contour line is calculated by a ray method, which includes the following specific steps:
extending a tunnel surface point to be judged to one side of the tunnel to form a ray, wherein the number of intersection points of the ray and a contour line in the cross section of the tunnel structure where the ray is located is n, and when n is an even number, the point is not in the contour line, namely no invasion limit is formed; when n is odd, the boundary is in the contour, i.e. there is an intrusion limit.
And 5, setting intervals to detect gradually according to the point cloud thickness of 0.01m, and realizing detection of all point clouds.
Further, the extending direction of the ray in the step 5 is parallel to the rail surface of the rail in the tunnel, and the extending direction of the ray is also perpendicular to the central line of the rail surface of the rail.
In this embodiment, as shown in fig. 1 and fig. 2, in step 6, the boundary detection result includes that the position generating the boundary violation is marked with the boundary violation angle and length in the corresponding result diagram; locations that do not produce an infringement limit are marked in the corresponding outcome map with the location and distance closest to the contour.
Further, in step 2 and step 3, for the design requirements and safety requirements of different mileage intervals, the used contour lines are different in the inner and outer areas, the operation area, the type of the located section and the like of the tunnel, and the used contour lines with different boundary types are used; for example, in a general straight tunnel section, a carriage profile is adopted as a contour line matched with a straight tunnel, in a turning tunnel section, because the carriage profile can generate a certain amount of deviation in the turning process, a plane geometric widening amount needs to be added on the basis of a straight line section limit model, the curvature radius is different, and the widening amount is different. The relationship between the radius of curvature and the amount of widening is generally as shown in table 1 below,
TABLE 1 relationship of radius of curvature to amount of broadening
| Definition of | R100 | R150 | R200 | R250 | R300 | R350 | R400 | R500 |
| Outside curve (mm) | 247 | 165 | 123 | 99 | 82 | 71 | 62 | 49 |
| Inner side of curve (mm) | 205 | 136 | 102 | 82 | 68 | 58 | 51 | 41 |
| Definition of | R600 | R700 | R800 | R1000 | R1200 | R1500 | R2000 | R3000 |
| Outside curve (mm) | 41 | 35 | 31 | 25 | 21 | 17 | 12 | 8 |
| Inner side of curve (mm) | 34 | 29 | 26 | 20 | 17 | 14 | 10 | 7 |
The subway tunnel clearance detection method based on point cloud can be applied to a related system of a rail transit structure measurement and disease detection system, after system software is opened, tunnel point cloud data are obtained by using three-dimensional laser scanning before deformation analysis and boundary intrusion detection are carried out, and a tunnel rail surface central line is extracted or led in; making contour lines and storing the contour lines as files with a specified format, such as carriage contour, equipment contour, building contour and the like;
and sequentially importing contour lines, matching the contour lines and the point cloud range, allocating a starting mileage and an ending mileage, calculating the postures of the contour lines at all positions by taking the center of the rail surface as a rotation center and the rotation angle of the rail surface as a rotation angle, judging the relative position relation between the contour lines and the point cloud on the surface of the tunnel, and generating a boundary intrusion detection result.
The subway tunnel clearance detection method based on the point cloud can set a detection section mileage interval, customize a detection interval, analyze tunnel point cloud clearance detection states in batches and appoint different clearance detection profiles for different mileage.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (5)
1. A subway tunnel clearance detection method based on point cloud is characterized in that: the method comprises the following specific steps:
step 1, acquiring point cloud data of the surface of a tunnel;
step 2, extracting or importing a rail surface central line, importing a contour line to be detected and used, and completing point cloud mileage allocation;
step 3, dividing the tunnel to be detected into a plurality of mileage intervals according to the curvature radius range of the tunnel, and matching different mileage intervals with corresponding contour lines;
step 4, taking the center of the rail surface as a contour line posture rotation center, and taking the rotation angle of the center line of the rail surface as a dynamic rotation angle of the contour line;
step 5, judging the position relation of each point and the contour line at set intervals according to a ray method, wherein the intrusion is determined in the detection contour line;
step 6, displaying a limit detection result in a 2D view;
and 7, exporting the detection charts in batches.
2. The point cloud-based subway tunnel clearance detection method as claimed in claim 1, wherein: and (3) scanning the point cloud data on the surface of the tunnel in the step 1 in the tunnel by using a three-dimensional laser scanning device.
3. The point cloud-based subway tunnel clearance detection method as claimed in claim 1, wherein: in the step 5, whether the point cloud in each mileage interval is in the detection contour line is calculated through a ray method, and the method specifically comprises the following steps:
extending a tunnel surface point to be judged to one side of the tunnel to form a ray, wherein the number of intersection points of the ray and a contour line in the cross section of the tunnel structure where the ray is located is n, and when n is an even number, the point is not in the contour line, namely no invasion limit is formed; when n is odd, the boundary is in the contour, i.e. there is an intrusion limit.
4. The point cloud-based subway tunnel clearance detection method as claimed in claim 3, wherein: and 5, the extending direction of the ray is parallel to the rail surface of the rail in the tunnel, and the extending direction of the ray is also vertical to the central line of the rail surface of the rail.
5. The point cloud-based subway tunnel clearance detection method as claimed in claim 1, wherein: step 6, marking the intrusion limit angle and length in the corresponding result graph at the position where the intrusion limit is generated in the limit detection result; locations that do not produce an infringement limit are marked in the corresponding outcome map with the location and distance closest to the contour.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113343341A (en) * | 2021-07-02 | 2021-09-03 | 中国铁建电气化局集团北方工程有限公司 | Tunnel contact network parameter determination method and device, computer equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203567760U (en) * | 2013-10-22 | 2014-04-30 | 北京市地铁运营有限公司运营二分公司 | Subway running boundary detection system |
| CN109059792A (en) * | 2018-07-19 | 2018-12-21 | 汪俊 | Dynamic 3 D tunnel cross-section shape changing detection and analysis system, method and device |
| CN110411361A (en) * | 2019-05-15 | 2019-11-05 | 首都师范大学 | A kind of mobile tunnel laser detection data processing method |
| CN111322985A (en) * | 2020-03-25 | 2020-06-23 | 南京航空航天大学 | Tunnel clearance analysis method, device and system based on laser point cloud |
| CN111752308A (en) * | 2019-03-26 | 2020-10-09 | 上海京海工程技术有限公司 | Method for correcting moving scanning posture in circular shield tunnel |
-
2021
- 2021-02-03 CN CN202110146367.4A patent/CN112923869A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203567760U (en) * | 2013-10-22 | 2014-04-30 | 北京市地铁运营有限公司运营二分公司 | Subway running boundary detection system |
| CN109059792A (en) * | 2018-07-19 | 2018-12-21 | 汪俊 | Dynamic 3 D tunnel cross-section shape changing detection and analysis system, method and device |
| CN111752308A (en) * | 2019-03-26 | 2020-10-09 | 上海京海工程技术有限公司 | Method for correcting moving scanning posture in circular shield tunnel |
| CN110411361A (en) * | 2019-05-15 | 2019-11-05 | 首都师范大学 | A kind of mobile tunnel laser detection data processing method |
| CN111322985A (en) * | 2020-03-25 | 2020-06-23 | 南京航空航天大学 | Tunnel clearance analysis method, device and system based on laser point cloud |
Non-Patent Citations (1)
| Title |
|---|
| 张学华等: "地铁运营期隧道结构检测方法", 《现代城市轨道交通》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113343341A (en) * | 2021-07-02 | 2021-09-03 | 中国铁建电气化局集团北方工程有限公司 | Tunnel contact network parameter determination method and device, computer equipment and storage medium |
| CN113343341B (en) * | 2021-07-02 | 2023-03-21 | 中国铁建电气化局集团北方工程有限公司 | Method for acquiring assembly parameters of big data subway overhead line system in rail transit area |
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Application publication date: 20210608 |