CN112967256B - Tunnel ovalization detection method based on spatial distribution - Google Patents
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Abstract
The application discloses a tunnel ovalization detection method based on spatial distribution in the technical field of tunnel detection, which comprises the following steps: (1) Scanning the outer edge of the tunnel by using a three-dimensional laser scanner, collecting coordinates of one data point every i mm, and forming a simulation data set by all collected data points; (2) Filtering out all data points with large errors to form a new data point set { M } 1 ,M 2 ,M 3 ,. Mg; (3) fitting an ellipse from the new set of data points; (4) calculating ellipticity according to the fitted elliptical parameters; wherein g is the number of data points in the data point set, M k The data point is the kth data point, and k is more than or equal to 1 and less than or equal to g; the application has the advantages of high detection speed, more stable detection and high detection precision.
Description
Technical Field
The application belongs to the technical field of tunnel detection, and particularly relates to a tunnel ovalization detection method based on spatial distribution.
Background
Along with the progress of urban mass development and the large-scale development of cities, the subway construction is being tightened in China to solve the problem of increasingly serious traffic jam. Due to the rapid expansion of the tunnel scale, the tunnel detection workload is increased suddenly, and the tunnel is wide in China, has huge geological structures from east to west and from south to north, has huge natural environment differences, changes of surrounding environments, vibration during train operation and other factors, and can be affected by various diseases, so that the deformation detection of a tunnel engineering structure is an extremely important work in the subway construction, operation and maintenance processes.
The tunnel cross section is theoretically required to be circular, but over time the tunnel tends to ovalize. The ovality is an important parameter for quantitatively calculating the ovalization of the tunnel, and the hidden danger of tunnel deformation is found in time to early warn tunnel defects, so that the detection of the ovality of the tunnel is increasingly important. The conventional tunnel deformation detection method comprises hanging plumb method measurement, precise leveling measurement, total station measurement, measurement robot measurement and the like, but the monitoring methods are slow in speed, high in manpower requirement, time-consuming and labor-consuming, meanwhile, measuring points are discrete and sparse, and the whole deformation condition of a tunnel section cannot be reflected.
Disclosure of Invention
The application aims to overcome the defects in the prior art, and provides a tunnel ovalization detection method based on spatial distribution, which solves the technical problem of low detection speed in the prior art.
The purpose of the application is realized in the following way: a tunnel ovalization detection method based on spatial distribution comprises the following steps:
(1) Scanning the outer edge of the tunnel by using a three-dimensional laser scanner, collecting coordinates of one data point every i mm, and forming a simulation data set by all collected data points;
(2) Filtering out all data points with large errors to form a new data point set { M } 1 ,M 2 ,M 3 ,...Mg}；
(3) Fitting an ellipse according to the new data point set;
(4) Calculating ellipticity according to the fitted elliptical parameters;
wherein g is the number of data points in the data point set, M k The data point is the kth data point, and k is more than or equal to 1 and less than or equal to g.
In order to further improve the detection accuracy, in the step (2), the step of filtering out the data points with large errors is specifically,
(201) The position of the laser generator is taken as an origin O, a tunnel end face plane right angle system xOy is established by taking a horizontal line and a vertical line passing through the origin as an x axis and a y axis respectively, and discrete data points are set as (x) α ，y α ) (α=1, 2,..g') calculating the centroid of the simulated dataset, and drawing a straight line, y, with the initial straight line sequentially at an angle ω counter-clockwise spacing, with the horizontal line passing through the centroid as the initial straight line centered on the centroid 1 、y 2 、,...y n N=180/ω, dividing the data point into a plurality of regions Ω 1 ,Ω 2 ,...Ω m ，m＝2n；
(202) Setting g' =g;
(203) Randomly selecting data points with eta proportion from each area, selecting p times altogether, fitting p ellipses, calculating parameters of each ellipse, calculating a parameter average value, taking the parameter average value as a preliminarily fitted ellipse parameter, calculating two focuses of the preliminarily fitted ellipse, calculating the sum of distances from each data point to the two focuses in a new data point set, and setting a distance threshold d;
(204) Setting k=0, i' =1;
(205)k’＝k+1；
(206) If k 'is less than or equal to g', adding 1 to k, and proceeding to step (207), otherwise proceeding to step (208);
(207) If M k‘ > d, then data point M k’ Removing from the new set of data points, adding i 'to 1, i "=i' -1, returning to step (205);
(208) g' =g-i ", j=0 is set;
(209)j’＝j+1，j’≤j setting up Turning to step (210), otherwise ending;
(210) Re-dividing the rest data points into areas, and sequentially rotating the straight line dividing the original area anticlockwise by theta j ' forming each new region, returning to step (203);
wherein,j setting up The iteration times are set.
In order to further realize the fitting of the ellipse, in the step (3), the step of fitting the ellipse is specifically,
(301) An elliptical expression is defined and a set of elliptical expressions,
(302) According to the discrete data points { x } α ，y α -defining algebraic distances (α=1, 2,..g')
(303) Defining matrices ζ and θ: ,
θ＝(A,B,C,D,E,F) T (4)；
(304) The oval expression may be rewritten in a compact format,
(ξ,θ)＝0 (5)；
(305) Define xi alpha at a certain point (x α ，y α ) The matrix xi on the matrix xi, and then the algebraic distance is re-expressed,
the matrix M is a matrix of the matrix,
(306) The constraint condition of the ellipse is constructed and,
(θ,Nθ)＝c (8)；
(307) The minimum algebraic distance is set to min J, let
minJ＝(θ,Mθ)
Let (θ, nθ) =c (9);
(308) Solving for mθ=λnθ, finding the matrix N that minimizes covariance and bias,
wherein:
(x c ，y c ) As discrete data points { x α ，y α Centroid coordinates of } (α=1, 2,..g') f0 is a proportionality constant and c is a non-zero constant.
In order to further realize the detection of the ovalization of the tunnel, in the step (4), the step of calculating the ovality is specifically,
(401) Determining the center position of an ellipse
(402) Calculating the ellipse major half axis a, and the intersection point of the straight line L1 where the major axis is located and the ellipse is (x) 1 ,y 1 ) And (x) 2 ,y 2 )，
(403) The ellipticity T is calculated and,
wherein a is a long half shaft of the tunnel, b is a short half shaft of the tunnel, and D is the outer diameter of the tunnel.
According to the application, a plurality of detected tunnel outer edge data points form a simulation data set, the data points in the simulation data set are divided into a plurality of areas, each time, a certain number of ellipses are fitted, an initial ellipse is constructed through average analysis, original data with large errors are filtered, noise points are removed, iteration is repeated, a new data set of a final ellipse to be fitted is obtained, ellipse parameters are obtained by meshing of the ellipses by using the new data set, ellipse fitting is better achieved by combining a new point cloud denoising method and an ellipse fitting method, detection accuracy is improved, and the method has the advantages of being more accurate and stronger in robustness especially in a strong noise environment; obtaining an ovalization index of the tunnel through the fitted ovalization parameters, and judging an accumulated deformation value of the tunnel and judging a structural damage risk level according to the calculated ovalization index of the tunnel in practical application; the method can be applied to the work of tunnel detection.
Drawings
FIG. 1 is a block diagram of an algorithm for filtering noise points in the present application.
Fig. 2 is a graph of the primary divided regions in the present application.
Fig. 3 is a graph of the next divided area in the present application.
Fig. 4 is a simulated point cloud data diagram of a tunnel in the present application.
Fig. 5 is a partial enlarged view in the point cloud data.
Fig. 6 is an ellipse fitting image of the present application.
Fig. 7 is a close-up view of an ellipse fitting image in accordance with the present application.
Detailed Description
The application is further described below with reference to the accompanying drawings.
A tunnel ovalization detection method based on spatial distribution comprises the following steps:
(1) Scanning the outer edge of the tunnel by using a three-dimensional laser scanner, collecting coordinates of one data point every i mm, and forming a simulation data set by all collected data points;
(2) Filtering out all data points with large errors to form a new data point set { M } 1 ,M 2 ,M 3 ,...Mg}；
(3) Fitting an ellipse according to the new data point set;
(4) Calculating ellipticity according to the fitted elliptical parameters;
wherein g is the number of data points in the data point set, M k The data point is the kth data point, and k is more than or equal to 1 and less than or equal to g.
In order to further improve the detection accuracy, in the step (2), the step of filtering out the data points with large errors is specifically,
(201) The position of the laser generator is taken as an origin O, a tunnel end face plane right angle system xOy is established by taking a horizontal line and a vertical line passing through the origin as an x axis and a y axis respectively, and discrete data points are set as (x) α ，y α ) (α=1, 2,..g') calculating the centroid of the simulated dataset, and drawing a straight line, y, with the initial straight line sequentially at an angle ω counter-clockwise spacing, with the horizontal line passing through the centroid as the initial straight line centered on the centroid 1 、y 2 、,...y n N=180/ω, dividing the data point into a plurality of regions Ω 1 ,Ω 2 ,...Ω m ，m＝2n；
(202) Setting g' =g;
(203) Randomly selecting data points with eta proportion from each area, selecting p times altogether, fitting p ellipses, calculating parameters of each ellipse, calculating a parameter average value, taking the parameter average value as a preliminarily fitted ellipse parameter, calculating two focuses of the preliminarily fitted ellipse, calculating the sum of distances from each data point to the two focuses in a new data point set, and setting a distance threshold d;
(204) Setting k=0, i' =1;
(205)k’＝k+1；
(206) If k 'is less than or equal to g', adding 1 to k, and proceeding to step (207), otherwise proceeding to step (208);
(207) If M k‘ > d, then data point M k’ Removing from the new set of data points, adding i 'to 1, i "=i' -1, returning to step (205);
(208) g' =g-i ", j=0 is set;
(209)j’＝j+1，j’≤j setting up Transfer toStep (210), otherwise, ending;
(210) Re-dividing the rest data points into areas, and sequentially rotating the straight line dividing the original area anticlockwise by theta j ' forming each new region, returning to step (203);
wherein,j setting up The iteration times are set.
In order to further realize the fitting of the ellipse, in the step (3), the step of fitting the ellipse is specifically,
(301) An elliptical expression is defined and a set of elliptical expressions,
(302) According to the discrete data points { x } α ，y α -defining algebraic distances (α=1, 2,..g')
(303) Defining matrices ζ and θ: ,
θ＝(A,B,C,D,E,F) T (4)；
(304) The oval expression may be rewritten in a compact format,
(ξ,θ)＝0 (5)；
(305) Define xi alpha at a certain point (x α ，y α ) The matrix xi on the matrix xi, and then the algebraic distance is re-expressed,
the matrix M is a matrix of the matrix,
(306) The constraint condition of the ellipse is constructed and,
(θ,Nθ)＝c (8)；
(307) The minimum algebraic distance is set to min J, let
minJ＝(θ,Mθ)
Let (θ, nθ) =c (9);
(308) Solving for mθ=λnθ, finding the matrix N that minimizes covariance and bias,
wherein:
(x c ，y c ) As discrete data points { x α ，y α Centroid coordinates of } (α=1, 2,..g'), f 0 Is a proportionality constant and c is a non-zero constant.
In order to further realize the detection of the ovalization of the tunnel, in the step (4), the step of calculating the ovality is specifically,
(401) Determining the center position of an ellipse
(402) Calculating the ellipse major half axis a, and the intersection point of the straight line L1 where the major axis is located and the ellipse is (x) 1 ,y 1 ) And (x) 2 ,y 2 )，
(403) The ellipticity T is calculated and,
wherein a is a long half shaft of the tunnel, b is a short half shaft of the tunnel, and D is the outer diameter of the tunnel.
According to the application, a plurality of detected tunnel outer edge data points form a simulation data set, the data points in the simulation data set are divided into a plurality of areas, each time, a certain number of ellipses are fitted, an initial ellipse is constructed through average analysis, original data with large errors are filtered, noise points are removed, iteration is repeated, a new data set of a final ellipse to be fitted is obtained, ellipse parameters are obtained by meshing of the ellipses by using the new data set, ellipse fitting is better achieved by combining a new point cloud denoising method and an ellipse fitting method, detection accuracy is improved, and the method has the advantages of being more accurate and stronger in robustness especially in a strong noise environment; obtaining an ovalization index of the tunnel through the fitted ovalization parameters, and judging an accumulated deformation value of the tunnel and judging a structural damage risk level according to the calculated ovalization index of the tunnel in practical application; the method can be applied to the work of tunnel detection.
In the embodiment, ω=45°, i is 4, m is 8, the dividing area in step (201) is shown in fig. 2, and the next dividing area is shown in fig. 3; j is set to be 6, η=60% and p=200; assuming that the major axis of the ovalized tunnel is 5.5m, the minor axis is 5.4m, the center coordinates of the ellipse are (0, 0), and the lower half minor axis is 4.7m and i is 2 because the tunnel measurement can only measure data points above the foundation, disturbance noise points are added to the data point set simulation to simulate the actual tunnel point cloud, as shown in fig. 4 and 5.
In addition, other detection methods are adopted to carry out comparison test with the detection method in the application, and the detection methods are respectively as follows:
I. directly fitting ellipse to the simulation data set by adopting HyperLS;
II, adopting LS to directly fit ellipse to the simulation data set;
thirdly, adopting RANSAC & HyperLS to directly fit ellipse to the simulated data set;
IV, adopting RANSAC & LS to directly fit ellipse to the analog data set;
v. the detection method in the application;
after a new data set is obtained by using the method, an LS method is used for fitting ellipse to the new data set.
As can be seen from fig. 7, the line V is closest to the standard data point, the fitting effect of the line VI is inferior, and the fitting effect of the conventional detection method is poor.
The application is not limited to the above embodiments, and based on the technical solution disclosed in the application, a person skilled in the art may make some substitutions and modifications to some technical features thereof without creative effort according to the technical content disclosed, and all the substitutions and modifications are within the protection scope of the application.
Claims (2)
1. The tunnel ovalization detection method based on spatial distribution is characterized by comprising the following steps of:
(1) Scanning the outer edge of the tunnel by using a three-dimensional laser scanner, collecting coordinates of one data point every i mm, and forming a simulation data set by all collected data points;
(2) Filtering out all data points with large errors to form a new data point set { M } 1 ,M 2 ,M 3 ,...Mg}；
(3) Fitting an ellipse according to the new data point set;
(4) Calculating ellipticity according to the fitted elliptical parameters;
wherein g is the number of data points in the data point set, M k The data point is the kth data point, and k is more than or equal to 1 and less than or equal to g;
in the step (2), the step of filtering out the data points with large errors is specifically,
(201) The position of the laser generator is taken as an origin O, a tunnel end face plane right angle system xOy is established by taking a horizontal line and a vertical line passing through the origin as an x axis and a y axis respectively, and discrete data points are set as (x) α ，y α ) (α=1, 2,..g') calculating the centroid of the simulated dataset, and drawing a straight line, y, with the initial straight line sequentially at an angle ω counter-clockwise spacing, with the horizontal line passing through the centroid as the initial straight line centered on the centroid 1 、y 2 、,...y n N=180/ω, dividing the data point into a plurality of regions Ω 1 ,Ω 2 ,...Ω m ，m＝2n；
(202) Setting g' =g;
(203) Randomly selecting data points with eta proportion from each area, selecting p times altogether, fitting p ellipses, calculating parameters of each ellipse, calculating a parameter average value, taking the parameter average value as a preliminarily fitted ellipse parameter, calculating two focuses of the preliminarily fitted ellipse, calculating the sum of distances from each data point to the two focuses in a new data point set, and setting a distance threshold d;
(204) Setting k=0, i' =1;
(205)k’＝k+1；
(206) If k 'is less than or equal to g', adding 1 to k, and proceeding to step (207), otherwise proceeding to step (208);
(207) If M k‘ > d, then data point M k’ Removing from the new set of data points, adding i 'to 1, i "=i' -1, returning to step (205);
(208) g' =g-i ", j=0 is set;
(209)j’＝j+1，j’≤j setting up Turning to step (210), otherwise ending;
(210) Re-dividing the rest data points into areas, and sequentially rotating the straight line dividing the original area anticlockwise by theta j ' structureReturning to step (203) as each new region;
wherein,j setting up Setting the iteration times;
in the step (3), the step of fitting the ellipse is specifically,
(301) An elliptical expression is defined and a set of elliptical expressions,
(302) According to the discrete data points { x } α ，y α -defining algebraic distances (α=1, 2,..g')
(303) The matrices xi and theta are defined and,
θ＝(A,B,C,D,E,F) T (4)；
(304) The oval expression may be rewritten in a compact format,
(ξ,θ)＝0 (5)；
(305) Define xi alpha at a certain point (x α ，y α ) The matrix xi on the matrix xi, and then the algebraic distance is re-expressed,
the matrix M is a matrix of the matrix,
(306) The constraint condition of the ellipse is constructed and,
(θ,Nθ)＝c (8)；
(307) The minimum algebraic distance is set to min J, let
minJ＝(θ,Mθ)
Let (θ, nθ) =c (9);
(308) Solving for mθ=λnθ, finding the matrix N that minimizes covariance and bias,
wherein:
(x c ，y c ) As discrete data points { x α ，y α Centroid coordinates of } (α=1, 2,..g'), f 0 Is a proportionality constant and c is a non-zero constant.
2. The tunnel ellipsometry method of claim 1, wherein the method comprises: in the step (4), the step of calculating ellipticity is specifically,
(401) Determining the center position of an ellipse
(402) Calculating the intersection point of the ellipse and the straight line L1 where the major axis is positioned and the ellipse as an ellipse major axis a(x 1 ,y 1 ) And (x) 2 ,y 2 )，
(403) The ellipticity T is calculated and,
wherein a is a long half shaft of the tunnel, b is a short half shaft of the tunnel, and D is the outer diameter of the tunnel.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1993018386A1 (en) * | 1992-03-13 | 1993-09-16 | Leica Industrieverwaltung Gmbh | Method and apparatus for determining the optical properties of a lens |
| US5563808A (en) * | 1993-05-03 | 1996-10-08 | General Electric Company | Pilger mill mandrel measuring device |
| CN103077315A (en) * | 2013-01-11 | 2013-05-01 | 西北工业大学 | Method for rebuilding front and back oval edges of aerial blade based on error control |
| CN109000782A (en) * | 2018-09-27 | 2018-12-14 | 哈尔滨工程大学 | A kind of ellipse fitting non-linear error calibration method based on Kalman filtering |
| CN109408909A (en) * | 2018-09-30 | 2019-03-01 | 中南大学 | A kind of three-dimensional rough surface micro-bulge approximating method |
| CN110163905A (en) * | 2019-05-08 | 2019-08-23 | 武汉惟景三维科技有限公司 | A kind of high-precision ellipse fitting method |
| CN111336991A (en) * | 2020-03-27 | 2020-06-26 | 南京航空航天大学 | Tunnel ellipticity analysis method, device and system based on laser point cloud |
| CN111402316A (en) * | 2020-03-09 | 2020-07-10 | 东华大学 | Method for rapidly detecting ellipse in image based on false link removal |
-
2021
- 2021-03-09 CN CN202110257311.6A patent/CN112967256B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1993018386A1 (en) * | 1992-03-13 | 1993-09-16 | Leica Industrieverwaltung Gmbh | Method and apparatus for determining the optical properties of a lens |
| US5563808A (en) * | 1993-05-03 | 1996-10-08 | General Electric Company | Pilger mill mandrel measuring device |
| CN103077315A (en) * | 2013-01-11 | 2013-05-01 | 西北工业大学 | Method for rebuilding front and back oval edges of aerial blade based on error control |
| CN109000782A (en) * | 2018-09-27 | 2018-12-14 | 哈尔滨工程大学 | A kind of ellipse fitting non-linear error calibration method based on Kalman filtering |
| CN109408909A (en) * | 2018-09-30 | 2019-03-01 | 中南大学 | A kind of three-dimensional rough surface micro-bulge approximating method |
| CN110163905A (en) * | 2019-05-08 | 2019-08-23 | 武汉惟景三维科技有限公司 | A kind of high-precision ellipse fitting method |
| CN111402316A (en) * | 2020-03-09 | 2020-07-10 | 东华大学 | Method for rapidly detecting ellipse in image based on false link removal |
| CN111336991A (en) * | 2020-03-27 | 2020-06-26 | 南京航空航天大学 | Tunnel ellipticity analysis method, device and system based on laser point cloud |
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