CN106091972B - A kind of building change detecting method projecting dot density based on moving window - Google Patents
A kind of building change detecting method projecting dot density based on moving window Download PDFInfo
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Abstract
The invention discloses a kind of building change detecting methods projecting dot density based on moving window, including step：Two phase point cloud datas are acquired and are registrated；Dimension Reduction Analysis is carried out to stablizing metope；Survey station point is calculated in the subpoint of plane where fixed metope and establishes structure coordinate system；Two phase point cloud datas are transformed into structure coordinate system；Brick point cloud and mortar point cloud are obtained using K mean value sorting techniques；Mortar point cloud is projected respectively to Z-direction and Y-direction；Define stationary window length LfixWith moving window length Lmove, calculate and change along Z-direction and Y-direction point cloud line density；Horizontal and vertical cut-off rule between brick is sought, four angular coordinates of each brick and each brick center are calculated；It is changed detection using two phases corresponding brick center.The present invention automatically extracts each brick center deformation information of metope, and the degree of automation greatly improves, and initial data is fully excavated, under the premise of accuracy guarantee, it is ensured that the deformation information of each brick of masonry structure building can be obtained effectively.
Description
Technical Field
The invention designs a building change detection method, and particularly relates to a building change detection method based on a mobile window.
Background
In cities, buildings are main places of human activities, and the safety conditions of the buildings relate to human daily life and economic activities, so that the detection of the change of the buildings is very important, the detection and repair of the change of the buildings are based on, especially, the deformation and damage of the buildings caused by earthquakes are important research points in recent years, in the development history of the building structures, the masonry structure is widely used for the basic structure of the buildings from old times, a combination body built by various blocks such as bricks, stones, brickwork, adobes and mortar (mortar, clay slurry and the like) is used as the masonry structure, the masonry structure is widely used due to low manufacturing cost, fire resistance, durability and simple construction, but the masonry strength is lower, the shock resistance is poorer, so the research on the detection of the change of the buildings of the masonry structure has very important practical significance, the existing image-based change Detection method can be classified into pixel-based, feature-based And target-based change Detection according to the information processing level, change Detection of LiDAR (Light Detection And Ranging) data gradually starts to be researched in photogrammetry And computer vision, the image-based change Detection method is not high enough in precision, the target is not clear enough And is easily influenced by image quality, the obtained deformation information is not rich enough, the deformation information of each brick in a masonry structure building cannot be accurately obtained, And in addition, the change Detection efficiency is seriously influenced due to low automation degree.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a building change detection method based on the density of projection points of a moving window, aiming at the defects of the prior art.
The technical scheme is as follows: the invention discloses a building change detection method based on the density of projection points of a moving window, which comprises the following steps of:
(1) performing two-stage scanning on the same building by adopting a laser scanner system to obtain cloud data of building surface points, and setting m targets on the periphery of a changed building, wherein m is more than or equal to 3, and the observed values of the laser scanner system are three-dimensional coordinates and laser reflection intensity of the building surface points;
(2) calculating a two-stage point cloud coordinate conversion parameter Z by using the target set in the step 1), and registering point clouds;
(3) selecting fixed wall point cloud data, and performing dimensionality reduction analysis on the fixed wall point cloud data by using a principal component analysis method to obtain a feature vector (v)iI is 1,2,3), calculating the projection of the measuring station on the plane of the fixed wall surface, taking the projection as the origin of coordinates, establishing a structural coordinate system, and converting the point cloud data from the measuring station coordinate system to the structural coordinate system;
(4) classifying point clouds on the changing wall surface by adopting a K-means clustering method based on the intensity information of the point cloud data, and separating to obtain brick point clouds and mortar point clouds;
(5) projecting the point cloud coordinates to the Z direction and the Y direction by using the mortar point cloud obtained in the step (4), and defining the length L of a fixed windowfixAnd moving window length LmoveRespectively calculating the density change of the point cloud in the Z direction and the Y direction through a moving window;
(6) respectively solving transverse and longitudinal dividing lines among the bricks according to the linear density change obtained in the step (5), calculating coordinates of four corner points of each brick, and establishing a brick model;
(7) acquiring point clouds of all bricks according to the brick model obtained in the step (6), and calculating the centers of all the bricks;
(8) and (5) obtaining deformation information according to the three-dimensional coordinates of the centers of the bricks in the two phases obtained in the step (7).
Preferably, the target is fixed during the two-stage scanning in step (1).
Preferably, the three-dimensional coordinate transformation equations in the step (2) and the step (3) are specifically as follows:
assuming that the matrix A is the point cloud three-dimensional coordinate under the coordinate system A, the matrix B is the point cloud three-dimensional coordinate under the coordinate system B, and A, B the three-dimensional coordinate transformation equation of the two coordinate systems is as follows:
(Δ x, Δ y, and Δ z represent the amount of translation of the origin of coordinates, k is a scale factor, k is 0, and R is a rotation matrix from the a coordinate system to the B coordinate system.)
Preferably, the calculation of the transformation parameters registered by the two-phase data in step (2) is specifically as follows:
the coordinate transformation parameter Z is further written as ═ Δ x, Δ y, Δ Z, εx，εy，εz，1]The coordinate conversion parameter Z is subjected to parameter estimation by using a least square method, and the estimated value of the coordinate conversion parameter Z is obtained as follows:
Z＝(ATQ-1A)-1ATQB
wherein Q is a covariance matrix of m target coordinate measurement errors in a B coordinate system, and the form is as follows:
preferably, the specific method for principal component analysis dimension reduction and coordinate origin determination in the step (3) by converting the station coordinate system into the structural coordinate system comprises the following steps:
let three-dimensional coordinates { X) of scanning point Xi＝(xi,yi，zi) I ═ 1,2, …, n }, the corresponding covariance matrix is constructed:
wherein, for the barycentric coordinates of the point set, the principal component analysis is carried out on the matrix C to obtain three characteristic values lambda1、λ2、λ3In descending order to obtain lambda1≥λ2＞λ3＞0，λ3Corresponding feature vector v3And v is3Is a normal vector, v3Calculating the projection S '(X') of the coordinate S (0,0,0) of the measuring station on the plane of the fixed wall surfaces,ys,zs) Since this is taken as the origin of coordinates of the structural coordinate system, the translation vector (Δ x, Δ y, Δ z) — (x)s,-ys,-zs) After a translation parameter and a coordinate axis rotation parameter are established, rotating the point cloud data after the two-stage registration to a structural coordinate system;
preferably, the specific method for separating the wall bricks and the mortar by the strength information-based K-means clustering method in the step (4) comprises the following steps:
the clustering error sum of squares function E is used as a clustering criterion function, the point intensity information is used as a classification attribute, wherein,xijis the ith class jth sample, miIs the cluster center or centroid of class i, niThe number of the ith sample is, the K-means clustering algorithm finds K optimal clustering centers through repeated iteration, wherein K is 2, all n sample points are distributed to the nearest clustering center, so that the clustering error sum of squares E is minimum, and the process is as follows:
a, randomly appointing k clustering centers mi(i＝1,2,…,k)；
b, for each sample xiFinding the nearest clustering center to the cluster center and distributing the cluster center to the class;
and c, recalculating each cluster new center:Niis the ith clusterNumber of previous samples;
d, calculating the deviation of the measured data,
e, if the value of E is converged, returning mi(i ═ 1,2, …, k), the algorithm terminates, otherwise b is returned;
preferably, the mortar point cloud provided in the step (5) is used for calculating the density change of the point cloud line based on a window moving method, and the specific method is as follows:
let the average width of the mortar be a known quantity LmortarDefining a fixed window length LfixAnd moving window length LmoveThe three satisfy the following relation:
Lmortar≈Lwindow+2Lmove
respectively calculating the number of moving windows along the Z direction and the Y direction:
wherein [ ] is a rounding symbol,
the number of points in each window in the Z and Y directions is calculated separately:
nzi(i＝1,2,…,ny),nyi(i＝1,2,…,nz)
the line densities of the points in the Z and Y directions were calculated, respectively:
Density_z＝(nz(i-1)+nzi+nz(i+1))/(3Lfix)(i＝2,3,…,(nz-1))
Density_y＝(ny(i-1)+nyi+ny(i+1))/(3Lfix)(i＝2,3,…,(ny-1))
for each window in the Z direction and the Y direction, the line density change rate is calculated:
Grad(i,1)＝Density_y(i)-Density_y(i-1)
Grad(i,2)＝Density_y(i+1)-Density_y(i)
preferably, in the step (6), the transverse and longitudinal dividing lines between the bricks are calculated by using the line density change, and the specific method is as follows:
for the Z direction or Y direction of analysis, the density of point cloud lines is obviously higher at the mortar joint vertical to the direction than the non-joint area, therefore, the window average density in the direction is selected as a threshold value (ntotalAnd the total number of point clouds) is calculated, when the density of the point clouds in the window is greater than a threshold value and meets Grad (i,1) > 0 and Grad (i,2) < 0, the fixed window where the point clouds are located is the range of the mortar joint, the center line of the mortar joint is calculated through the point clouds in the range of the mortar joint, and then the coordinates of four corner points of the brick model are calculated through the center lines of the joints around the brick, so that the brick model is established.
Has the advantages that: the building change detection method based on the density of the projection points of the movable window has the advantages that on one hand, the automation degree is high, on the other hand, the original data are fully utilized, the precision is high, and on the premise of guaranteeing the precision, the deformation information of each brick of a masonry structure building can be effectively obtained.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic view of a coordinate system of the invention;
FIG. 3 is a schematic diagram of a structural coordinate system of the present invention;
fig. 4 is a schematic view of a block model of the present invention;
FIG. 5 is a K-means classification result of a certain wall selected by the present invention;
FIG. 6 is a histogram of density variations of point cloud projection points according to the present invention;
FIG. 7 is a schematic view of the center points of the bricks extracted by the present invention;
fig. 8 shows three-dimensional deformation information of the brick obtained by the present invention.
Detailed Description
The technical solution of the present invention is described in detail below, but the scope of the present invention is not limited to the embodiments.
As shown in fig. 1, a method for detecting building change based on the density of projection points of a moving window includes the following steps:
(1) the method comprises the steps that a laser scanner system is adopted to conduct two-stage scanning on the same building to obtain cloud data of building surface points, m targets are arranged in a fixed area around a changed building, generally, m is larger than or equal to 4, the automatic leveling function of the laser scanner is considered, and the instrument is in a horizontal state during each measurement, so that m is larger than or equal to 3, and the observed values of the laser scanner system are three-dimensional coordinates and laser reflection intensity of the building surface points;
(2) calculating a two-stage point cloud coordinate conversion parameter Z by using the target set in the step (1), and registering point clouds;
(3) selecting fixed wall point cloud data, and performing dimensionality reduction analysis on the fixed wall point cloud data by using a principal component analysis method to obtain a feature vector (v)iI is 1,2,3), calculating the projection of the measuring station on the plane of the fixed wall surface, taking the projection as a coordinate origin O, establishing a structural coordinate system, and converting the point cloud data from the measuring station coordinate system to the structural coordinate system;
(4) classifying point clouds on the changing wall surface by adopting a K-means clustering method based on the intensity information of the point cloud data, and separating to obtain brick point clouds and mortar point clouds;
(5) projecting the point cloud coordinates to the Z direction and the Y direction by using the mortar point cloud obtained in the step (4), and defining the length L of a fixed windowfixAnd moving window length LmoveRespectively calculating the density change of the point cloud in the Z direction and the Y direction through a moving window;
(6) respectively solving transverse and longitudinal dividing lines among the bricks according to the linear density change obtained in the step (5), calculating coordinates of four corner points of each brick, and establishing a brick model;
(7) acquiring point clouds of all bricks according to the brick model obtained in the step (6), and calculating the centers of all the bricks;
(8) and (5) obtaining deformation information according to the three-dimensional coordinates of the centers of the bricks in the two phases obtained in the step (7).
The invention is further explained by taking the detection of the change of a masonry structure building in a certain experimental field before and after an earthquake as an example:
{1} utilizing a Leica C10 laser scanner system to scan a building, erecting an instrument at a position shown in FIG. 5, laying 4 targets in a stable region outside an experimental building, respectively scanning before and after an earthquake test, and acquiring two-stage building surface laser point cloud data, wherein observed values comprise two types: three-dimensional coordinates, laser reflection intensity;
{2} calculating a two-stage point cloud coordinate conversion parameter Z by using the target set in the step {1} and registering the point cloud coordinate after the earthquake test to a point cloud station coordinate system before the earthquake test, wherein the station is taken as a center and the vertical direction is taken as a Z axis as shown in FIG. 2, and the two-stage point cloud is in the same coordinate system so as to be convenient for deformation analysis at the later stage;
{3} as shown in fig. 3, three coordinate axes are respectively parallel to three adjacent faces of the building with a certain corner point as the center, fixed wall face point cloud data are selected, principal component analysis is utilized for dimension reduction analysis, a fixed wall face normal vector is obtained, namely, a structural coordinate system X-axis unit vector is [ 0.93480.35510.0011 ], a structural coordinate system Z-axis unit vector is [ 001 ], so that a Y-axis unit vector is [ 0.3551-0.93480 ], a projection point from a coordinate origin of a station coordinate system to the fixed wall face is [ 8.27663.14420.0095 ], and the point cloud data are converted from the station coordinate system to the structural coordinate system according to the parameters and the conversion relation;
{4} classifying point clouds of two phases of wall surfaces by adopting a K-means clustering method according to the intensity information of the point cloud data and separating to obtain brick point clouds and mortar point clouds as shown in FIG. 5;
{5} the average width of mortar between blocks, as measured and surveyed in situ, was 10-12mm, and thus, according to Lmortar≈Lwindow+2LmoveDefining a fixed window length Lfix0.008m and a moving window length LmoveUsing the mortar point cloud obtained in the step {4}, projecting the point cloud coordinates to the Z direction and the Y direction, and respectively calculating the density change of the cloud lines of the points along the Z direction and the Y direction through a moving window, wherein the result is shown in fig. 6;
{6} respectively obtaining longitudinal and transverse dividing lines among the bricks according to the linear density change obtained in the step {5}, calculating coordinates of four corner points of each brick, and establishing a brick model shown in FIG. 4;
{7} as shown in fig. 7, acquiring point cloud data of each brick according to the brick model obtained in the step {6}, and calculating the center of each brick;
{8} as shown in fig. 8, deformation information is obtained according to the three-dimensional coordinates of the center of the brick corresponding to the two phases obtained in the step {7 }.
Claims (7)
1. A building change detection method based on the density of projection points of a moving window is characterized in that: the method comprises the following steps:
1) performing two-stage scanning on the same building by adopting a laser scanner system to obtain cloud data of building surface points, and setting m targets on the periphery of a changed building, wherein m is more than or equal to 3, and the observed values of the laser scanner system are three-dimensional coordinates and laser reflection intensity of the building surface points;
2) calculating a two-stage point cloud coordinate conversion parameter Z by using the target set in the step 1), and registering point clouds;
3) selecting fixed wall point cloud data, and performing dimensionality reduction analysis on the fixed wall point cloud data by using a principal component analysis method to obtain a feature vector viCalculating the projection of the measuring station on the plane of the fixed wall surface, taking the projection as a coordinate origin, establishing a structural coordinate system, and converting the point cloud data from the measuring station coordinate system to the structural coordinate system;
4) classifying point clouds on the changing wall surface by adopting a K-means clustering method based on the intensity information of the point cloud data, and separating to obtain brick point clouds and mortar point clouds;
5) utilizing the mortar point cloud obtained in the step 4), wherein the average width of the mortar is not set to be LmortarProjecting the point cloud coordinates to the Z direction and the Y direction to define the length L of the fixed windowfixAnd moving window length LmoveAnd satisfies the following conditions: l ismortar≈Lwindow+2Lmove(ii) a Determining the number of moving windows according to the maximum value and the minimum value of the point cloud midpoint coordinate component Z or Y, and respectively calculating the density change of point cloud lines along the Z direction and the Y direction through the moving windows;
6) step 5) changing the linear density of the obtained point cloud, wherein for the analyzed Z direction or Y direction, the linear density of the point cloud is obviously higher than the non-joint area at the mortar joint vertical to the direction, so the average density of the window in the direction is selected as a threshold valuentotalWhen the cloud density of points in the window is greater than a threshold value and meets Grad (i,1) > 0 and Grad (i,2) < 0, the fixed window where the points are located is the range of a mortar joint, the center line of the mortar joint is calculated through the point clouds in the range of the mortar joint, and the coordinates of four corner points of a brick model are calculated through the center lines of the peripheral joints of bricks, so that a brick model is established;
7) acquiring each brick point cloud according to the brick model obtained in the step 6), solving the geometric center of the brick point cloud, and acquiring the center of each brick;
8) and obtaining deformation information according to the three-dimensional coordinates of the centers of the bricks in the two phases obtained in the step 7).
2. The method for detecting building changes based on moving window proxel density of claim 1, wherein: step 1) in the two-stage scanning process, the target is fixed.
3. The method for detecting building changes based on moving window proxel density of claim 1, wherein: the three-dimensional coordinate transformation equation in the step 2) and the step 3) is specifically as follows:
assuming that the matrix A is the point cloud three-dimensional coordinate under the coordinate system A, the matrix B is the point cloud three-dimensional coordinate under the coordinate system B, and A, B the three-dimensional coordinate transformation equation of the two coordinate systems is as follows:
where Δ x, Δ y, and Δ z represent the amount of translation of the coordinate origin, k is a scale factor, k is 0, and R is a rotation matrix from the a coordinate system to the B coordinate system.
4. The method for detecting building changes based on moving window proxel density of claim 1, wherein: step 2) the calculation of the conversion parameter by the two-stage data registration is specifically as follows:
the coordinate transformation parameter Z is further written as ═ Δ x, Δ y, Δ Z, εx，εy，εz，1]And performing parameter estimation on the coordinate conversion parameter Z by using a least square method, wherein the estimated value of the coordinate conversion parameter Z is as follows:
Z＝(ATQ-1A)-1ATQB
wherein Q is a covariance matrix of m target coordinate measurement errors in a B coordinate system, and the form is as follows:
5. the method for detecting building changes based on moving window proxel density of claim 1, wherein: the specific method for converting the coordinate system of the measuring station into the structural coordinate system to analyze and reduce the dimension and determine the origin of coordinates comprises the following steps:
let three-dimensional coordinates { X) of scanning point Xi＝(xi,yi,zi) I ═ 1,2, …, n }, the corresponding covariance matrix is constructed:
wherein, for the barycentric coordinates of the point set, the principal component analysis is carried out on the matrix C to obtain three characteristic values lambda1、λ2、λ3In descending order to obtain lambda1≥λ2＞λ3＞0，λ3Corresponding feature vector v3And v is3Is a normal vector, v3Calculating the projection S '(X') of the coordinate S (0,0,0) of the measuring station on the plane of the fixed wall surfaces,ys,zs) Since this is taken as the origin of coordinates of the structural coordinate system, the translation vector (Δ x, Δ y, Δ z) — (x)s,-ys,-zs) After the translation parameter and the coordinate axis rotation parameter are established, the point cloud data after the two-stage registration is rotated to a structure coordinate system.
6. The method for detecting building changes based on moving window proxel density of claim 1, wherein: the specific method for separating the wall bricks and the mortar by the strength information-based K-means clustering method in the step 4) comprises the following steps:
the clustering error sum of squares function E is used as a clustering criterion function, the point intensity information is used as a classification attribute, wherein,xijis the ith class jth sample, miIs the i-th class of clusters or centroids, niThe number of the ith sample is, the K-means clustering algorithm finds K optimal clustering centers through repeated iteration, wherein K is 2, all n sample points are distributed to the nearest clustering center, so that the clustering error sum of squares E is minimum, and the process is as follows:
step 1, randomly appointing k clustering centers mi(i＝1,2,…,k)；
Step2, for each sample xiFinding the nearest clustering center to the cluster center and distributing the cluster center to the class;
step 3, recalculating each cluster new center:Niis the current sample number of the ith cluster;
and Step 4, calculating the deviation,
step 5, if the E value converges, return mi(i ═ 1,2, …, k), the algorithm terminates, otherwise it returns to Step 2.
7. The method for detecting building changes based on moving window proxel density of claim 1, wherein: calculating the density change of the point cloud line by using the mortar point cloud and based on a window moving method, wherein the specific method comprises the following steps:
respectively calculating the number of moving windows along the Z direction and the Y direction:
wherein]To round the symbol, ymaxAnd yminRespectively the maximum value and the minimum value of the Y coordinate component of the point cloud midpoint; z is a radical ofmaxAnd zminRespectively the maximum value and the minimum value of the Z coordinate component of the point cloud midpoint;
the number of points in each window in the Z and Y directions is calculated separately:
nzi(i＝1,2,…,ny),nyi(i＝1,2,…,nz)
the line densities of the points in the Z and Y directions were calculated, respectively:
Density_z＝(nz(i-1)+nzi+nz(i+1))/(3Lfix)(i＝2,3,…,(nz-1))
Density_y＝(ny(i-1)+nyi+ny(i+1))/(3Lfix)(i＝2,3,…,(ny-1))
for each window in the Z direction and the Y direction, the line density change rate is calculated:
Grad(i,1)＝Density_y(i)-Density_y(i-1)
Grad(i,2)＝Density_y(i+1)-Density_y(i)。
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Application publication date: 20161109 Assignee: ANHUI JINHE SOFTWARE Co.,Ltd. Assignor: HOHAI University Contract record no.: X2020980010569 Denomination of invention: Building change detection method based on projection point density of moving window Granted publication date: 20180921 License type: Common License Record date: 20210104 |