LU504678B1 - Method for Measuring Tunnel Face Displacement based on three-dimensional laser scanning - Google Patents
Method for Measuring Tunnel Face Displacement based on three-dimensional laser scanning Download PDFInfo
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- G—PHYSICS
- 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
- G01B11/16—Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
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- G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
The present invention discloses a method for measuring the displacement of tunnel face based on three-dimensional laser scanning, which comprises the following steps: arranging a total station scanner in front of a newly excavated tunnel face to obtain the point cloud data of tunnel face in different periods; processing the obtained point cloud data of the tunnel face, separating the point cloud data of tunnel face, and converting the point cloud data of tunnel face in different periods into the same coordinate system according to the reference point; setting voxel blocks, and respectively dividing the point cloud data of tunnel face separated in different periods into square point cloud data; registering the square point cloud data by adopting a point cloud feature descriptor to obtain corresponding relationships of point cloud coordinates in different periods; and calculating the deformation and displacement of the tunnel face according to the corresponding relationship of the point cloud coordinates. The present invention mainly realizes the measurement of overall displacement and deformation of the tunnel face, without the need for setting target point, it can measure any multiple points on the tunnel face, and avoid direct contact between workers and the tunnel face in the initial construction period, thus greatly ensuring the personal safety of relevant workers.
Description
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METHOD FOR MEASURING TUNNEL FACE DISPLACEMENT BASED ON
THREE-DIMENSIONAL LASER SCANNING
The present invention relates to the technical field of tunnel face displacement monitoring, in particular relates to a method for measuring tunnel face displacement based on three-dimensional laser scanning
In the process of new tunnel excavation, the tunnel face serves as a direct working area, and its stability should be the key attention object, a large number of personnel and mechanical equipment are often concentrated near the tunnel face, once the tunnel face is unstable, serious consequences such as casualties, mechanical equipment damage and construction period lag can be brought, and therefore it is necessary to monitor the deformation of the tunnel face in real time.
Tunnel face deformation can directly reflect the stable state of the tunnel face, so it is of great significance to obtain tunnel face deformation in real time.
At present, due to the fact that the displacement monitoring difficulty of the tunnel face is large, the monitoring is rarely carried out in practice. The tunnel face displacement is monitored, the monitoring target can be arranged on the tunnel face, and the total station is used for monitoring, although the method 1s high in monitoring precision, a specific target needs to be placed at the measured position, whether the newly excavated tunnel face is stable or not is not determined, and personnel safety cannot be ensured when the measuring point target is installed; and meanwhile, related measurement 1s point measurement, and the overall deformation condition of the tunnel face cannot be obtained.
The three-dimensional laser scanning technology can be used for tunnel deformation non-contact measurement under certain conditions, and compared with common tunnel deformation measurement, displacement measurement of any surface position can be achieved, and certain technical advantages are achieved. The surface space information of the whole observation object can be obtained through three-dimensional laser scanning, the deformation condition of the structure can be obtained by analyzing the point cloud data, traditional fixed single-point analysis is expanded into overall analysis through deformation analysis, and overall deformation of the tunnel face can be obtained.
Due to the advantages of three-dimensional laser scanning, related workers carry out a large amount of work around three-dimensional laser scanning monitoring, and existing document 1 discloses the tunnel face deformation alarm method based on mobile three-dimensional laser scanning, and deformation monitoring based on specific monitoring points is achieved; the existing document 2 discloses a method and device for identifying local deformation in the tunnels, the relative deformation of the monitoring structure is obtained through unified large-carth coordinates; the existing document 3 discloses a method for measuring the convergence deformation of circular tunnels, circular tunnel point clouds in different periods are attributed to the unified coordinate system, and circular tunnel convergence deformation data are obtained through analysis through the fitting algorithm;
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The above methods are either used to measure specific points on the tunnel face or to analyze structural deformation, but at present, the method for measuring any point of the tunnel face is lacked.
Aiming at the defects in the prior art, the present invention provides a method for measuring tunnel face displacement based on three-dimensional laser scanning, which achieves accurate, real-time and comprehensive automatic measurement for detecting the dynamic deformation of surrounding rock of tunnel face.
In order to achieve the above purpose, the present invention adopts the following technical solution:
A method for measuring tunnel face displacement based on three-dimensional laser scanning, the method comprises the following steps:
Arranging a total station scanner in front of a newly excavated tunnel face to obtain the point cloud data of tunnel face in different periods;
Processing the obtained point cloud data of the tunnel face, separating the point cloud data of tunnel face, and converting the point cloud data of tunnel face in different periods into the same coordinate system according to the reference point;
Setting voxel blocks, and respectively dividing the point cloud data of tunnel face separated — 1n different periods into square point cloud data;
Registering the square point cloud data by adopting a point cloud feature descriptor to obtain corresponding relationships of point cloud coordinates in different periods;
Calculating the deformation and displacement of the tunnel face according to the corresponding relationship of the point cloud coordinates.
Beneficial effect
According to the present invention, the deformation of the tunnel face can be monitored and measured only by the inherent features of the tunnel face, the detection range can be significantly improved, and the comprehensive measurement of the tunnel face can be achieved; in addition, the present invention takes the spatial topology of the tunnel face as the registration target, and does not need to select or bury markers as measuring points on the tunnel main structure, and avoids the direct contact between workers and the tunnel face at the initial stage of construction, thus greatly ensuring the personal safety of relevant workers.
Fig. 1 is a flow diagram of a method for measuring tunnel face displacement based on three-dimensional laser scanning.
Fig. 2 is a schematic diagram of installation of the positioning prism in an example of the present invention;
Fig. 3 is a schematic diagram of point cloud registration in an example of the present invention;
Fig. 4 is a schematic diagram of construction of the point cloud coordinate axis in an example of the present invention;
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Fig. 5 1s a schematic diagram of the Point-to-Plane ICP algorithm in an example of the present invention.
As shown in Fig. 1, the example of the present invention provides a method for measuring tunnel face displacement based on three-dimensional laser scanning, the method comprises the following steps S1 to S5:
S1, arranging a total station scanner in front of a newly excavated tunnel face to obtain the point cloud data of tunnel face in different periods;
In an optional example of the present invention, three sets of prisms are installed on the secondary lining of the tunnel respectively, and their relative coordinates are obtained by using the total station function in the total station scanner to establish a reference point;
Wherein the positioning prism is arranged on the secondary lining, which should be as close as possible to the tunnel face and arranged in a staggered mode, the device is of a detachable structure, with two points as reference points and the other point as a rechecking point.
The present invention do not need to arrange monitoring points on the tunnel face, the installation position of the total station scanner is at a certain distance from the tunnel face, which improves the safety of monitoring technicians.
The present invention arranges the total station scanner in front of the newly excavated tunnel face, scans the reference point, and the positioning information of the total station scanner is obtained, then uses the scanning function for scanning the tunnel face to obtain the tunnel face point cloud data in different periods;
Wherein the installation position of the total station scanner is controlled between 20-40 m from the tunnel face. Before scanning the tunnel face, the known points should be scanned at all stations to obtain the basic state of the total station scanner; after the completion of tunnel excavation and roof finding, the tunnel face should be scanned as carly as possible, the scanning time of the tunnel face should be recorded, and the initial scanning point cloud is taken as the source point cloud, which is the benchmark for the comparative analysis of tunnel face deformation.
Then, according to the field situation and monitoring requirements, repeating the above steps to obtain multi-phase point cloud data.
The present invention has that advantage of short time for collecting the scan data of the tunnel face, large amount of scanned data, and improved efficiency of collecting the data of the tunnel face.
S2, processing the obtained point cloud data of the tunnel face, separating the point cloud data of tunnel face, and converting the point cloud data of tunnel face in different periods into the same coordinate system according to the reference point;
In an optional example of the present invention, the present invention processes the obtained point cloud data of the tunnel face, which specifically comprises:
According to the standard central axis data of the tunnel design and the mileage and coordinate data of the total station scanner, calculating the mileage at the tunnel face, and then dividing all the point cloud data to obtain point cloud data containing the tunnel face;
Statistical denoising and radius denoising are adopted to denoise the point cloud data including the working face;
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Downsampling method based on the distance from the voxel center is adopted to downsample the denoised point cloud data.
Specifically, the point cloud data is processed by using software Cloud-Compare, open3D extension package in python, and other programs;
Firstly, according to the standard central axis data of the tunnel design and the mileage and coordinate data of the total station scanner, calculating the mileage at the tunnel face, the
Cloud-Compare is used for separating the point cloud and obtain the point cloud data containing the tunnel face;
Secondly, statistical denoising and radius denoising are adopted to denoise the point cloud data including the working face;
Then, downsampling method based on the distance from the voxel center is adopted to downsample the denoised point cloud data.
The statistical denoising adopted by the present invention is to calculate the standard deviation of point cloud distances within the neighborhood, and use Gaussian distribution to remove points with an average distance greater than the threshold in the neighborhood, the specific method is as follows:
For the average distance from any point p in the point cloud to the point p;(1 = 1,2, …n) in its neighborhood, setting the coordinates of point p as {x, y, z}, the coordinates of point p; are {x;, Vi. Zi}, the distance from point p to point p; is d;, its average distance is d , so di =x = x)? + y)? zz? d — Dies d; n
Due to the discrete type of point cloud scanning, it can be assumed that the average distance d between the point in the false fixed point cloud and the point in its neighborhood meets the normal distribution in statistical significance, namely the distribution of N(u, 0”), then the formula for calculating the mathematical expectation u and standard deviation ¢ of the point cloud distance is as follows: _ Ed,
H m m (5 2 27, (di — 6) c= 1-=— m
Wherein d; is the average distance from the ith point in the point cloud to the point in its neighborhood, and m 1s the number of points in the point cloud.
Considering the principle of normal distribution, setting the distance threshold &, and removing the point noise whose distance from the point cloud to the nearest adjacent point in the neighborhood is greater than €: e=00+u
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Wherein 6 is a proportionality coefficient, and which is related to the number of points in the neighborhood.
The radius denoising method adopted by the present invention uses the distance threshold to cluster point cloud data, and removes the point cloud whose number of points in the cluster is 5 lower than the quantity threshold as the outlier, the specific method is as follows:
For any point p in the point cloud, all points of which the Euclidean distance in the neighborhood of the point cloud is smaller than the distance threshold value are searched, the adjacent points are marked as neighbor n; n=1, 2,..., n), the neighbors are still obtained according to the distance threshold value for n; until no new neighbors appear, and then the point 1s divided into one cluster. The clusters whose number of points is lower than the threshold value are removed as outliers by the quantity threshold, and denoising is completed.
The present invention adopts the downsampling method based on the distance from the voxel center to downsample the denoised point cloud data containing the tunnel face, which specifically comprises:
Constructing a plurality of cubic grids with set side lengths in the three-dimensional space, and voxelizing the point cloud data containing the tunnel face through grid division;
Removing the empty voxel grids from all divided voxel grids;
Taking the voxel grid as a unit, and downsampling the point cloud data in the voxel grid.
In an optional example of the present invention, the present invention separates the point cloud data of the tunnel face, which specifically comprises:
Selecting any point from the point cloud data of tunnel face and searching for all points that have a density comparable to that point through width first; if the point is the core object, marking it as the same set as the searched point; if the point is a boundary object, marking the searched point as noisy until a complete family is found;
Randomly reselecting one point from the point cloud data of tunnel face, and repeating the operation to obtain the next group until all points are marked.
Specifically, the included angle formed by the tunnel face and the central axis of the tunnel is large, while the curved surface of the peripheral area of the tunnel face is approximately parallel to the central axis of the tunnel, the included angle is small, and the point cloud is projected to the plane perpendicular to the central axis of the tunnel. A large number of projection areas of the tunnel face overlap, resulting in very dense point clouds at the tunnel contour, and sparse point clouds in the tunnel face area inside the contour, showing obvious characteristics; therefore, the present invention use a density-based clustering algorithm to separate the point cloud, and the specific method is as follows:
Step 1, starting from any individual 7, searching all individuals whose density is reachable with 7 by width first, and if the individual 7 is the core object, marking them as the same set; if the individual 7 is a boundary object, marking the searched points as noise until a complete family is found:
Step 2: randomly reselecting one new individual 7 and processing it to get the next family, and the algorithm continues until all individuals have been marked.
Direct density reachable: if 7 is a core object and j is in the neighborhood of 7, that is, the point j is in a circle range with a certain 7 as the circle center and R as the radius, then the direct density of 7 to j is reachable.
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Density reachable: Given individual Py, P2, ..., P„, any individual P;, wherein i=l, 2, ..., m, and there is a direct density reachable from P; to P;+1, then the Pi to P, density is reachable.
Finally, the present invention converts the two phase point clouds into the same coordinate system through coordinate unification based on the station building information.
S3, setting voxel blocks, and respectively dividing the point cloud data of tunnel face separated in different periods into square point cloud data;
In an optional example of the present invention, the present invention arranges a rectangular block with a voxel block size of 0.15x0.15x1 m, and divides the point cloud data of the tunnel face separated in different periods into square point cloud data of 0.15 x0.15.
S4, registering the square point cloud data by adopting a point cloud feature descriptor to obtain corresponding relationships of point cloud coordinates in different periods;
In an optional example of the present invention, the present invention registers the square point cloud data by adopting a point cloud feature descriptor to obtain corresponding relationships of point cloud coordinates in different periods, which specifically comprises:
According to any two points and corresponding normal in the neighborhood, the point with a small included angle between the straight line formed by two points and the normal line is taken as a source point, and the coordinate axis is established at the source point;
Calculating the features of (a, @,6) two points, statistics is carried out in the form of histogram to form a simplified point histogram SPFH as a point cloud feature descriptor;
Determining consistency matching point pair between source point cloud and target point cloud by adopting the point cloud feature descriptor, and performing global registration on the point cloud data;
Adopting Point to Plane ICP algorithm for precise registration of point cloud data;
According to the target transformation matrix of global registration and the rotation transformation matrix of precise registration, a final transformation matrix is obtained, the optimal registration state of the source point cloud and the target point cloud is calculated according to the final transformation matrix, and the corresponding relationship between the source point cloud and the target point cloud is obtained.
Wherein determining consistency matching point pair between source point cloud and target point cloud by adopting the point cloud feature descriptor, and performing global registration on the point cloud data; which specifically comprises:
The point cloud feature descriptor is used to determine the consistent matching point pair K = {(py, qx)} between the source point cloud P = {p;},i = 0,1,2 …,n and the target point cloud@ = {g;}i = 0,1,2 …,m;
Taking the minimum space distance between the matching point pairs as the objective function, and constructing the estimation function of the target transformation matrix;
EM =) plp-Tal) (p,q)ek pox? p(x) = Wt x? wherein E(T) is a target change matrix estimation function; p(x)is a robust optimizer; u is an optimizer parameter, and x is a control residual parameter; Tq is a transformed target corresponding point;
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An estimation function of the target transformation matrix is optimized and solved by using the Black-Rana Dual and linear decomposition method to obtain the globally registered target transformation matrix, which specifically comprises:
According to the linear relationship between the consistency point pairs, the estimation function of the target transformation matrix is optimized, which is expressed as
ECL =) lg lp=TalP+ > vla) (p,q)ek PDEK
Va) = (ia —1) wherein E(T, L) is the estimation function of the target transformation matrix 7, 7 is the solved target transformation matrix, [,,, is the linear relationship between consistency point pairs, Lg is the distance between the origin cloud and the corresponding point of the target point cloud, (p,q) is the corresponding points of the origin cloud and the target point cloud, p is the origin cloud, q is the target point cloud, K is the corresponding point cloud set, and (ly) is the limiting function;
The rotation component W and the translation component T line in the target transformation matrix T are decomposed into six vectors, which are expressed as & = (w,t) = (ahr tutyt,)
Wherein a is the rotation component along the X axis, § is the rotation component along the Y axis, y is the rotation component along the Z axis, t, is the displacement component in the X direction, t, is the displacement component in the Y direction, and ¢, is the displacement component in the Z direction;
The transformation matrix T can be expressed by Eby the following formula: 1 —y B a
Ta y 1 —a b 7 -f a 1 € 0 0 0 1
Wherein T* is the estimated transformation matrix of the last iteration;
The decomposition vector is solved by using the Gaussian-Newton method, which is expressed as:
JE = ir 0 -g9 gq —1 0 0 ef 0 gq 0 -1 —-4 do 0 0 0 —1
Wherein J! is a decomposition vector transposition, J, is its corresponding Jacobian matrix, and r is aresidual vector;
The target transformation matrix is solved according to the decomposition vector, and then an estimation function of the target transformation matrix is solved.
Wherein adopting Point to Plane ICP algorithm for precise registration of point cloud data, which specifically comprises:
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The objective is to minimize the sum of squares of the distances between the corresponding points of the source point cloud and the target point cloud on the tangent plane, and the target loss function is established as ; , 2
Mope =argminy E:((M -p; — qi) ni)
M = T(t» Ly» t;) - R(a, 5s y) 1 0 0 17, 0 1 0 ¢
T(t1,21,)= y 0 0 1 1 0 0 0 1 1 0 0
Ra) =|0 cosa sina 0 —sina cosa cosB 0 —sinf
R(B) = | 0 1 0 sinB 0 cosf cosy siny 0
R(y) =|-siny cosy 0 0 0 1 hohe hs 0
R —R R R Nh m 3 0 (a, B.y)= (7) (8) (a) = 0
Far 3 133 0 0 0 1
T,1 = Cos y cos f
T,2 = — Siny cos a + cosy sin f sina,
T,3 = Siny sin a + cosy sin f cos a ry, = siny cos ß
Ty, = COS Y COS « + siny sin B sin a
Ty = — COS y sina + siny sin f cosa,
T3, = — sin f
T32 = cos sina
T33 = cos B cos a
Wherein Mopt 1s a target loss function, M is the rigid transformation matrix between the source point cloud and the target point cloud, 7 is a consistent corresponding point serial number,
T(t,, ty» t;) is a matrix transformation translation component, R(a, bs y) is a matrix change rotation component, R,(a), R,(B), R,(y) are rotation matrixes;
The rotation transformation matrix is approximated as
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R(a,B,y)= ~ =R(a, 8,7) —B a 1 0 -f a 1 0 0 0 0 1 0 0 0 1
Wherein Ra ,P,y) is an approximate rotation transformation matrix;
The target loss function 1s converted according to the approximate rotation transformation matrix, which is expressed as ~~ . ~~ 2
Mopr =argminy > ((m "Pi — di) : mi) i
Wherein M on is the converted target loss function, M is the transformed rigid transformation matrix, p; is the consistent pair point source point, q; 1s the consistent corresponding point target point, and n; is the target point tangent plane normal vector;
The converted target loss function is unfolded and expressed as:
PD ix 4; n, (Mes -d,)n, —| Wyx Py | | Dy | | y
PD iz qd; n, 1 1 0 = | (n.p, 7 Ny Pi Ja + (n,p. 7 N, Dix ) B + (2, Dr 7 N,Py) Y + nt, + ni, + nt, ] 7 [ 1,4, + ng, + n.q. 7 n, Dix - np, 7 N, Pi ] wherein pi, is the coordinate value of the corresponding point origin x, p;y is the coordinate value of the corresponding point origin y, p;, is coordinate value of the corresponding point origin z, q;, is the coordinate value of the corresponding point target point x, q;y is the coordinate value of the corresponding point target point y, q;, is the coordinate value of the corresponding point target point z, n;, 1s the x component of the target point tangent plane normal vector, n;y1s the y component of the target point tangent plane normal vector, n;, is the z component of the target point tangent plane normal vector, a, B,y are rotation components, t,. tyand t, are translation components;
Supposing there are N corresponding point pairs (p;, q;), 1 <i < N; through the above linear approximation processing, the target loss function of all point pairs is expressed as
Ax —b a, 1 ap, a); mn, N y mn, ay ay ay Nox n,, n,,
A=] . . . ; i ; ay 1 ay 2 ay 3 py ny fy ny,
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Air = NizPiy — NiyPiz
Uiz = NixPiz — NizPix
Ai3 = NiyPix — NixPiy x = (a BY t ty t7)"
Made + nq, LE —n,.p, 7 NP, —M,P1 b= nq. +n,,q,, +1n,.4q,, “Thilo —m,p,, M, Pa;
Nyy, + yd Tq, 7 Mn Pay 7 Ny, Py Ny, Py. wherein nyy is the corresponding point of x component of the tangent plane normal vector of the nth corresponding target point, ny, is the corresponding point of y component of the tangent plane normal vector of the nth corresponding target point, ny, is the corresponding point of z component of the tangent plane normal vector of the nth corresponding target point, py, 1s the x component of the nth corresponding source point, py, is the y component of the nth corresponding source point, and py, 1s the z component of the nth corresponding source point,
Gnx 1s the X component of the nth corresponding target point, gy, is the Y component of the nth corresponding target point and gy, is the Z component of the nth corresponding target point;
According to the matrix representation of the target loss function, the target loss function is converted into
Xope = Argmin, |Ax — b|*
Wherein Xopt is the target loss function of the conversion;
SVD decomposition is used for solving the converted target loss function to obtain a rotation transformation matrix.
Specifically, as shown in Fig. 3, the point cloud registration process seeks one rigid transformation including translation and rotation, so as to minimize the spatial position difference of point cloud data in different periods.
Firstly, feature matching is characterized by identifying features between different local point clouds, the most similar area is obtained, the two point cloud centroid are taken as corresponding homonymous points with the same name, the present invention uses the feature description as the
FPFH (Fast Point Feature Histogram) descriptor, and the point cloud descriptor is characterized by calculating (a, @, 0) three features of the point and its neighboring points directly connected with it, the above features are counted in the form of histogram to form a simplified point histogram
SPFH(Simple Point Feature Histograms).
The feature descriptor is calculated as follows:
For any two pointsp;, p; in the neighborhood and the corresponding normal n; and n;, two points p,and p, are selected, the point with a small included angle between the straight line formed by the two points and the normal line is taken as an origin, and the coordinate axis is established from the origin as shown in Fig. 4.
Secondly, the known source point cloud S = {A;}, i = 0,1,2 …,n and the target point cloud
T ={A;},i = 0,1,2 …,m, there is an initial rigid transformation matrix M, so that S and T are aligned in the three-dimensional space. The consistency matching point pairs (Correspondence sets) K = {(px, qx)} between the source point cloud and the target point cloud are preliminarily
BL-5716
LU504678 determined through the spatial point cloud FPFH descriptor. Then the target transformation T can minimize the spatial distance between the matched point pairs after registration. Define the estimation function for the target transformation matrix:
EM =) pllp-Tal (p,q)ek 2 _ x pP (x) TT u + x2
Wherein E(T) is a target change matrix estimation function; p(x) is a robustness optimizer, which is used for improving the calculation efficiency and avoiding sampling, verifying, deleting or recalculating the corresponding relationship in the process of optimizing the target transformation.
Due to the fact that optimization solution is difficult to directly carry out on the above mode,
Black-Rana Dual is used between the robust estimation and the linear process of the algorithm, and the method is specifically as follows:
Supposing L = {La} as the linear relation between the consistency point pairs, and then the estimation function of the target transformation T is optimized as follows: 5 ECL =) lg lp=TalP+ > vla) (p,q)ek (p,q)ek 2
Y(lpq) =n (na - 1)
When E(T,L) is minimum, the function deflection meets the following formula: oF > Vig — 1
Su = Ip — Tall? + pe = 0 pa Ving
The method is solved: . pa (He)
Pa \u+llp — Tall?
The target transformation matrix T can be solved by substituting the upper formula into the estimation function of the target transformation matrix.
Considering the complexity of the calculation, and the algorithm is optimized on the basis of the calculation steps: (1) Linearly decomposing of the rotation component w and the translation component t in the target transformation matrix T into six vectors: §=Ww,t)=(a,B,v,a,b,0)
The transformation matrix Tmay be represented by €: 1 —y B a 1 —a b
Tæ| " 1" -f a 1 € 0 0 0 1
T* in the above formula is an estimated transformation matrix of the last iteration; (2) Using Gaussian-Newton method to solve €:
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LU504678
JE = fr 60 -¢ a -1 0 0
J = Ly q2 0 —q 0 —-1 0 —qq qo 0 0 0 —1
In the above formula, r is a residual vector, and J, is a Jacobian corresponding to the residual vector; (3) From the above formula, substituting & into T*, solving T, and substituting T back into formula (8) to calculate E(T,L);
Then, adopting Point to Plane ICP algorithm for precise registration of point cloud data on the basis of global registration, and the specific method is as follows:
Given the source point cloud P and the target point cloud Q, the corresponding relationship between points in the source point cloud after initial transformation and the target point cloud, and the corresponding point pair is set as (p;, q;). The Point-to-Plane ICP takes the sum of squares of the distances from p; to the tangent plane where g;the is located as the evaluation standard, as shown in Fig. 5, the algorithm objective of the Point-to-Plane ICP is to minimize the distance.
Firstly, the target loss function is defined: ; 2
Mopt = argming > (an ‘pi qi® n;) i
Wherein p; represents the point on the source point cloud, q; represents the corresponding point of p; on the target point cloud, n; 1s the normal vector of the tangent plane where q; 1s located, and M is the rigid transformation between the source point cloud and the target point cloud. M comprises translation and rotation of the point cloud in space, which is expressed in a matrix form:
M = T (ty Ly» t;) -R(a, 5s y) 1 0 0 17, 0 1 0 ¢
T(t1,21,)= 0 0 1 1 0 0 0 1 1 0 0
Ra) =|0 cosa sina 0 —sina cosa cosB 0 —sinf
R(B) = | 0 1 0 sinB 0 cosf cosy siny 0
R(y) =|-siny cosy 0 0 0 1
BL-5716
LU504678 hw hh hs 0
R _—R R R NP m 3 0 (a,B,7)= (7) ,(B)- (a) = ry Ta Ta 0 0 0 0 1
T,1 = Cos y cos f
T,2 = — Siny cos a + cosy sin f sina,
T,3 = Siny sin a + cosy sin f cos a ry, = siny cos ß
Ty, = COS Y COS « + siny sin B sin a
Ty = — COS y sina + siny sin f cosa,
T3, = — sin f
T32 = cos sina
T33 = cos B cos a
In order to reduce the solving complexity, the nonlinear solving problem is linearly approximated, and the method is as follows:
When the angle 9 is approximately equal to 0, sin = 0,cos 0 = 1, and therefore, when a, 3,v = 0, the rotation transformation can be approximated as follows: 1 af-y ay+ß O y afy+l py-a 0
Ra, B, 7) = —P ao 1 0 0 0 0 1 1 — B O y 1 —a 0 R = = a, >
Ba 10 (@.5.7) 0 0 0 1
Then, the optimal transformation can be approximated as: 1 TŸ BS f, y l —a 1
M=T(t t,t) Ra, By) = y (66,4) REBNE "0 a 1 à 0 0 0 1
Substituting the above formula into the target loss function to obtain the target loss function:
Pa 7 Pa 2
Mopt = argminy > ((m "bi — di) ; mi) i
The linear approximation of solving the nonlinear least square problem is completed by unfolding the above formula:
BL-5716
LU504678
PD ix dx n, (Mes -d,)n, —| Wyx Py | | Dy | | y
PD iz di n, 1 1 0 = | (n.p, 7 Ny Pi Ja + (n,p. 7 N, Dix ) B + (2, Dr 7 N,Py) Y + nt, + ni, + nt, ] 7 [ 1,4, + ng, + n.q. 7 n, Dix - np, 7 N, Pi ]
Supposing that there are n pairs of corresponding point pairs (p;, q;)» 1 < i < N; by means of the linear approximation processing, the target loss function of all point pairs is expressed as a matrix
Ax —b
Wherein : a 1 a, a5 LE ny y mn,
A — ay ay ys U n,, n,, ay 1 ay 2 ay 3 Rye ny, fy Ry,
Air = NizPiy — NiyPiz iz = NixPiz 7 NizPix
Ai3 = NiyPix — NixPiy x=(@ BY tk ty LL) n.q, + nq, +n.q, —n,p, 7 NP, AP b= nq. +n,,q9,, +1,.9,, =D, —m,p,, — MPa; ny Gy, + Nyy,Iny +R Une Ty Pns 7 Ry, Pry Ny, Py,
Therefore, the solution of the target loss function can be converted into the following form:
Xope = Argmin, |Ax — b|*
The above formula is solved through SVD decomposition:
Xopt =A xb
Finally, according to the target transformation matrix of global registration and the rotation transformation matrix of precise registration, a final transformation matrix is obtained, and the corresponding relation of the two point clouds can be obtained by obtaining the optimal registration state of the two-stage point cloud.
S6, calculating the deformation displacement of the tunnel face according to the corresponding relation of the point cloud coordinates.
In an optional example of the present invention, the Euclidean distance between the two point clouds is calculated according to the point cloud corresponding relationship, and the distance is the deformation value of the tunnel face in the range; and the corresponding relation between the point
BL-5716
LU504678 clouds in different periods can be known according to the registration result, that is: the average
Euclidean distance between point cloud A(x; v;,z;) and the point cloud A(x, y;, Z;) 1S x | a — x)” + (yı — y) + (z: — 5)" /n, wherein the distance is the deformation value of the tunnel face in the region.
In an optional example of the present invention, the present invention further comprises visual processing and information arrangement on tunnel face deformation, which specifically comprises:
By dividing the deformation values proportionally and taking the allowable deformation values in the structural design as the limit values, point clouds with different deformation values are colored, if the deformation is less than the allowable design deformation value, the color changes from blue to red, if the deformation exceeds the allowable design value, gray is used as a label to determine the corresponding relationship between the deformation values and colors, making the deformation visible;
Counting the number of deformation areas greater than a certain deformation value and compare them with the deformation limit value to display the safety status information of the palm face.
The present invention can perform displacement analysis on more tunnel face feature points, so that overall displacement analysis of the tunnel face is achieved, and the tunnel face displacement analysis effect is enhanced.
In conclusion, through the scheme of the present invention, the monitoring measurement of the deformation of the tunnel face can be realized, and the measurement precision also meets the standard requirement while the safety of related workers is guaranteed. According to the method, the space topology of the tunnel face is used as a registration target, a marker does not need to be selected or buried on the tunnel main body structure as a measuring point, so that the direct contact between workers and the tunnel face in the initial construction stage is avoided, and the personal safety of related workers is greatly guaranteed.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to examples of the present invention. It should be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, a special purpose computer, an embedded processor, or other programmable data processing device to generate a machine such that instructions executed by a processor of the computer or other programmable data processing device generate means for implementing functions specified in one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a product that includes an instruction device that implements the functions specified in one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions may also be loaded onto a computer or other programmable data processing device, such that a series of operating steps are performed on a
BL-5716
LU504678 computer or other programmable device to produce a computer-implemented process, such that the instructions executed on the computer or other programmable device provide steps for implementing functions specified in one or more flows of the flowchart and/or one or more blocks of the block diagram.
Claims (10)
1. A method for measuring the displacement of tunnel face based on three-dimensional laser scanning, characterized in that the method comprises the following steps: Arranging a total station scanner in front of a newly excavated tunnel face to obtain the point cloud data of tunnel face in different periods; Processing the obtained point cloud data of the tunnel face, separating the point cloud data of tunnel face, and converting the point cloud data of tunnel face in different periods into the same coordinate system according to the reference point; Setting voxel blocks, and respectively dividing the point cloud data of tunnel face separated in different periods into square point cloud data; Registering the square point cloud data by adopting a point cloud feature descriptor to obtain corresponding relationships of point cloud coordinates in different periods; Calculating the deformation and displacement of the tunnel face according to the corresponding relationship of the point cloud coordinates.
2. The method for measuring the displacement of tunnel face based on three-dimensional laser scanning according to claim 1, characterized in that processing the obtained point cloud data of the tunnel face, which specifically comprises: According to the standard central axis data of the tunnel design and the mileage and coordinate data of the total station scanner, calculating the mileage at the tunnel face, and then dividing all the point cloud data to obtain point cloud data containing the tunnel face; Statistical denoising and radius denoising are adopted to denoise the point cloud data including the working face; Downsampling method based on the distance from the voxel center is adopted to downsample the denoised point cloud data.
3. The method for measuring the displacement of tunnel face based on three-dimensional laser scanning according to claim 2, characterized in that the downsampling method based on the distance from the voxel center is adopted to downsample the denoised point cloud data, which specifically comprises: Constructing a plurality of cubic grids with set side lengths in the three-dimensional space, and voxelizing the point cloud data containing the tunnel face through grid division; Removing the empty voxel grids from all divided voxel grids; Taking the voxel grid as a unit, and downsampling the point cloud data in the voxel grid.
4. The method for measuring the displacement of tunnel face based on three-dimensional laser scanning according to claim 1, characterized in that separating the point cloud data of tunnel face, which specifically comprises: Selecting any point from the point cloud data of the tunnel face and searching for all points that have a density comparable to that point through width first; if the point is the core object, marking it as the same set as the searched point; if the point is a boundary object, marking the searched point as noise until a complete family is found;
BL-5716 LU504678 Randomly reselecting one point from the point cloud data of tunnel face, and repeating the operation to obtain the next group until all points are marked.
5. The method for measuring the displacement of tunnel face based on three-dimensional laser scanning according to claim 1, characterized in that registering the square point cloud data by adopting a point cloud feature descriptor to obtain corresponding relationships of point cloud coordinates in different periods, which specifically comprises: According to any two points p;, p; and corresponding normal mn; and n; in the neighborhood, the point with a small included angle between the straight line formed by two points and the normal line is taken as a source point py, the other point is p;, whose normal lines are n, and n, respectively, and the coordinate axis is established at the source point; Calculating the following parameters according to the established coordinate system: u=n,u=ux PEP) W=UXV lp: — sll Calculating the features of (a, D, 8) of two points according to the above parameters, wherein Œ=vV-Mm, @=v, 9 =arctan(w -u,u - nı), statistics is carried out in the form of histogram to form a simplified point histogram SPFH as a point cloud feature descriptor; Determining consistency matching point pair between source point cloud and target point cloud by adopting the point cloud feature descriptor, and performing global registration on the point cloud data; Adopting Point to Plane ICP algorithm for precise registration of point cloud data; According to the target transformation matrix of global registration and the rotation transformation matrix of precise registration, a final transformation matrix is obtained, the optimal registration state of the source point cloud and the target point cloud is calculated according to the final transformation matrix, and the corresponding relationship between the source point cloud and the target point cloud is obtained.
6. The method for measuring the displacement of tunnel face based on three-dimensional laser scanning according to claim 5, characterized in that determining consistency matching point pair between source point cloud and target point cloud by adopting the point cloud feature descriptor, and performing global registration on the point cloud data, which specifically comprises: The point cloud feature descriptor is adopted to determine the consistent matching point pair K = {(py, qx)} between the source point cloud P = {p;},i = 0,1,2 …,n and the target point cloud Q ={q;},i=0,12...,m; Taking the minimum space distance between the matching point pairs as the objective function, and constructing the estimation function of the target transformation matrix; EM =) plp-Tal) (p,q)ek pox? p(x) = Wt x? Wherein E(T) is a target change matrix estimation function; p(x)is a robust optimizer, u 1s an optimizer parameter, and x is a control residual parameter; Tq is a transformed target corresponding point;
BL-5716 LU504678 An estimation function of the target transformation matrix is optimized and solved by using the Black-Rana Dual and linear decomposition method to obtain the globally registered target transformation matrix.
7. The method for measuring the displacement of tunnel face based on three-dimensional laser scanning according to claim 6, characterized in that estimation function of the target transformation matrix is optimized and solved by adopting the Black-Rana Dual and linear decomposition method to obtain the globally registered target transformation matrix, which specifically comprises: According to the linear relationship between the consistency point pairs, the estimation function of the target transformation matrix is optimized, which is expressed as ECL =) lg lp=TalP+ > vla) (p,q)ek PDEK Va) = (ia —1) Wherein E(T, L) is the estimation function of the target transformation matrix 7, 7 is the solved target transformation matrix, L,g is the linear relationship between consistency point pairs, l,, ; 1s the distance between the origin cloud and the corresponding point of the target point cloud, (p,q) is the corresponding points of the origin cloud and the target point cloud, p is the origin cloud, q is the target point cloud, K is the corresponding point cloud set, and (ly) is the limiting function; The rotation component W and the translation component T line in the target transformation matrix T are decomposed into six vectors, which are expressed as E = (w,t) = (abv tutyt,) Wherein a is the rotation component along the X axis, § is the rotation component along the Y axis, y is the rotation component along the Z axis, t, is the displacement component in the X direction, t, is the displacement component in the Y direction, and t, is the displacement component in the Z direction; The transformation matrix T can be expressed by Eby the following formula: 1 —y B a 7 x y 1 —a b 7 -f a 1 € 0 0 0 1 Wherein T* is the estimated transformation matrix of the last iteration; The decomposition vector is solved by adopting the Gaussian-Newton method, which is expressed as: JE = ir Wherein J! is a decomposition vector transposition, J, is its corresponding Jacobian matrix, and r 1s aresidual vector; 0 -g9 gq -1 0 0 ef 0 —gq 0 -1 —-4 do 0 0 0 —1
BL-5716 LU504678 According to the decomposition vector &, solving the target transformation matrix T, and then using the estimation function E(T, L)of the target transformation matrix to obtain the coarse registration transformation matrix.
8. The method for measuring the displacement of tunnel face based on three-dimensional laser scanning according to claim 5, characterized in that adopting Point to Plane ICP algorithm for precise registration of point cloud data, which specifically comprises: The objective is to minimize the sum of squares of the distances between the corresponding points of the source point cloud and the target point cloud on the tangent plane, and the target loss function is established as ; , 2 Mope =argminy E:((M -p; — qi) ni) M = T (ty Ly» t;) -R(a, 5s y) 1 0 0 17, 0 1 0 ¢ T(t1,21,)= y 0 0 1 1 0 0 0 1 1 0 0 Ra) =|0 cosa sina 0 —sina cosa cosB 0 —sinf R(B) = | 0 1 0 sinB 0 cosf cosy siny 0 R(y) =|-siny cosy 0 0 0 1 hw 1m M 0 R —R R R Nh m 3 0 (a, B.y)= (7) (8) (a) = rv, Fr, Fk, O 31 32 33 0 0 0 1 T,1 = Cos y cos f T,2 = — Siny cos a + cosy sin f sina, T,3 = Siny sin a + cosy sin f cos a ry, = siny cos ß Ty, = COS Y COS « + siny sin B sin a Ty = — COS y sina + siny sin f cosa, T3, = — sin f T32 = cos sina T33 = cos B cos a Wherein Mopt 1s a target loss function, M is the rigid transformation matrix between the source point cloud and the target point cloud, 7 is a consistent corresponding point serial number,
BL-5716 LU504678 T (ty ty, t;) IS a matrix transformation translation component, R (a Bs y) Is a matrix change rotation component, R,(a), R,(B), R,(y) are rotation matrixes; The rotation transformation matrix is approximated as 1 aBb-y ay+ß © 1 — ß O y afy+l By-a 0 y 1 —a O0 R(a,B,y)= ~ =R(a, 8,7) —B a 1 0 -f a 1 0 0 0 0 1 0 0 0 1 Wherein R(a, 5s y) 1s an approximate rotation transformation matrix; The target loss function is converted according to the approximate rotation transformation matrix, which is expressed as
~~ . ~~ 2 Mopr =argminy > ((m "Pi — di) : mi) i Wherein Moe is the converted target loss function, Mis the transformed rigid transformation matrix, p; is the consistent pair point source point, q; is the consistent corresponding point target point, and n; is the target point tangent plane normal vector; The converted target loss function 1s unfolded and expressed as : (Ars, - d, Vn, =A TT ETT iy 9 ) 7 Pa Gi i ; ; A. = H A Ps 7 LN Ja + (LP. = Ay Pr } f + {rg Pr 7 Ha Ps } y + Ad, + HF, + aL -| 4. +R, fa + NF 7 Ho Pu 7 Fo Da 7 Hy Pi Wherein p;jx is the coordinate value of the corresponding point origin x, P;y is the coordinate value of the corresponding point origin y, p;, is coordinate value of the corresponding point origin z, q;y is the coordinate value of the corresponding point target point x, q;y is the coordinate value of the corresponding point target point y, q;, is the coordinate value of the corresponding point target point z, n;, 1s the x component of the target point tangent plane normal vector, n;yis the y component of the target point tangent plane normal vector, n;;, is the z component of the target point tangent plane normal vector, a, B,y are rotation components, t,. tyand tare translation components; The target loss function of all point pairs is expressed as the matrix Ax —b
BL-5716 LU504678 a, 1 ap, a); mn, N y mn, a= CR CRU US UE ay 1 ay 2 ay 3 py ny fy ny, Air = NizPiy — NiyPiz Uiz = NixPiz — NizPix Ai3 = NiyPix — NixPiy x=(@ B y t ty LL) Made + nq, LE —n,.p, 7 NP, —M,P1 b= nq. +n,,q,, +1n,.4q,, “Thilo —m,p,, M, Pa; Pyne TP ny Tne; = Pye Poe — Bay Pry = oz Po: Wherein ny, 1s the corresponding point of x component of the tangent plane normal vector of the nth corresponding target point, ny, is the corresponding point of y component of the tangent plane normal vector of the nth corresponding target point, ny, is the corresponding point of z component of the tangent plane normal vector of the nth corresponding target point, py, 1s the x component of the nth corresponding source point, py, is the y component of the nth corresponding source point, and py, is the z component of the nth corresponding source point, Gnx 1s the X component of the nth corresponding target point, gy, is the Y component of the nth corresponding target point and gy, is the Z component of the nth corresponding target point; According to the matrix representation of the target loss function, the target loss function is converted into Xope = Argmin, |Ax — b|* Wherein Xopt is the target loss function of the conversion; SVD decomposition is adopted for solving the converted target loss function to obtain a rotation transformation matrix.
9. The method for measuring the displacement of tunnel face based on three-dimensional laser scanning according to claim 1, characterized in that the method further comprises visual processing and information arrangement on tunnel face deformation.
10. The method for measuring the displacement of tunnel face based on three-dimensional laser scanning according to claim 9, characterized in that the visual processing and information arrangement on tunnel face deformation, which specifically comprises: By dividing the deformation values proportionally and taking the allowable deformation values in the structural design as the limit values, point clouds with different deformation values are colored, if the deformation is less than the allowable design deformation value, the color changes from blue to red, if the deformation exceeds the allowable design value, gray is used as a label to determine the corresponding relationship between the deformation values and colors, making the deformation visible;
BL-5716 LU504678 Counting the number of deformation areas greater than a certain deformation value and compare them with the deformation limit value to display the safety status information of the palm face.
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