CN112465991B - Method for denoising tunnel point cloud and generating visual model - Google Patents
Method for denoising tunnel point cloud and generating visual model Download PDFInfo
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Abstract
The invention relates to a method for denoising tunnel point cloud and generating a visual model, which belongs to the technical field of graphics and visualization and comprises the steps of collecting tunnel three-dimensional point cloud and preprocessing tunnel point cloud data; denoising: dividing the noise points into noise points in the tunnel and noise points outside the tunnel according to the positions of the noise, dividing the noise points in the tunnel into outlier points caused by a moving target and satellite noise points caused by facilities in the tunnel according to the space distribution condition, carrying out denoising point processing according to the geometric characteristics of the existing point arrangement and the actual condition of the tunnel, and respectively processing the noise in the tunnel and the noise outside the tunnel; seam filling, face constructing and ground adding. The method and the device only rely on the point cloud data to generate the visual model, can divide the point cloud into a plurality of parts for processing, can meet the processing of a large amount of point cloud data, and have higher efficiency.
Description
Technical Field
The invention relates to a method for denoising tunnel point cloud and generating a visual model, belonging to the technical field of graphics and visualization.
Background
The tunnel is an important component of urban traffic system as traffic engineering penetrating mountain and river. And factors such as narrow space in the tunnel and poor illumination condition cause a certain difficulty in tunnel measurement. The three-dimensional point cloud laser scanning technology has the advantages of no illumination, non-contact measurement, high automation degree, capability of realizing comprehensive information acquisition, short operation period and the like as a novel mapping technology, and is widely applied to tunnel measurement.
At present, most of the point cloud information is visualized directly based on laser scanning, so that the effect is not ideal; the existing tunnel triangular mesh model visualization algorithm can only support mesh generation of a small amount of data.
In the process of visualization, the most important step is to remove noise points such as vehicles, pedestrians, lamp tubes and the like in the acquired data. Existing methods mostly depend on tunnel design; for tunnels with long-term drawing deletion, the adopted method is complex and has lower efficiency. How to provide a visual model which is simple and efficient and is only based on point cloud data becomes a technical problem to be solved.
Disclosure of Invention
Aiming at the defects of the prior art, the method for denoising the point cloud of the tunnel and generating the visual model is provided by the invention, the visual model is generated only by relying on the point cloud data, the point cloud can be divided into a plurality of parts for processing, a large amount of point cloud data processing can be satisfied, and the efficiency is higher.
Term interpretation:
1. the bounding box: the present invention refers to a virtual rectangular parallelepiped that can completely surround a tunnel.
The invention adopts the following technical scheme:
a method of denoising and generating a visualization model for a tunnel point cloud, comprising:
step 1, collecting a tunnel three-dimensional point cloud, and preprocessing tunnel point cloud data;
firstly, a three-dimensional laser scanner is used for collecting three-dimensional point clouds of a tunnel, a y axis of a coordinate system is defined as a direction of trend of the tunnel in the collecting process, a z axis is perpendicular to the ground, and an x axis obeys a right rule;
preprocessing the derived point cloud data, and deriving coordinate information of the point cloud when the point cloud data is derived;
the pretreatment process comprises the following steps:
1.1, generating point cloud slices: splitting the point cloud data according to the trend of the tunnel into point cloud slices with the length of 3-5 meters;
1.2, performing thinning treatment on each point cloud slice, wherein a plurality of point cloud slices are independently treated in a parallel mode during treatment:
the existing rasterization gravity center thinning method is adopted to carry out thinning treatment on each point cloud slice, the selection of the grid side length depends on the precision requirement on the final grid, the smaller the grid side length is, the more points remain, and the smoother the generated grid model is;
1.3, determining a slice bounding box: taking the maximum value of the slice point cloud coordinates to determine a rectangular bounding box;
step 2, denoising:
because the tunnel condition is complex, the three-dimensional laser scanner is adopted to scan and comprises interference objects, the noise points are divided into noise points in the tunnel and noise points outside the tunnel according to the noise positions, the noise points in the tunnel are divided into outlier points caused by moving targets and accessory noise points caused by facilities in the tunnel according to the space distribution condition, the noise point removing processing is carried out according to the geometric characteristics of the existing point arrangement and the actual condition of the tunnel, and the noise in the tunnel and the noise outside the tunnel are respectively processed.
Preferably, the process of removing noise points outside the tunnel is as follows:
for each point cloud slice, the range of the bounding box in the x-axis direction is known, the bounding box is divided into point cloud columns (the point cloud columns are preferably 0.5 m) in the decimeter level, the number of points in each column is counted, and fewer columns are removed (columns with the number of points less than 50 can be considered as fewer columns);
the same process is performed in the z-axis direction, namely, the columns are divided into point cloud columns in the z-axis by decimeter (the width of the point cloud column is preferably 0.5 m), the number of points in each column is counted, and fewer columns are deleted (columns with the number of points less than 50 can be considered as fewer columns);
preferably, the process of removing noise points in the tunnel comprises the following steps:
a. splitting each slice again, and splitting the slices into a plurality of rows and columns again on a xoz plane, so as to form a tunnel block, namely a point cloud block;
preferably, the widths of the rows and the columns are about 0.5m, and the rows and the columns are split into about 0.5m blocks, so that the blocks are not excessively large, and the fitting degree of a fitting plane obtained later is not enough;
b. and carrying out coordinate transformation on each tunnel block by adopting a Principal Component Analysis (PCA) method, and finding a fitting plane:
c, forming a matrix X of 3 rows and m columns of the original three-dimensional data in the point cloud block obtained by processing in the step a according to the columns, wherein m represents the points in the point cloud block;
zero-equalizing each row of matrix X (representing data in the same axial direction), i.e. subtracting the average value of this row;
solving covariance matrix
Wherein C is the covariance matrix obtained, m is the number of points in the point cloud block;
obtaining eigenvalues and corresponding eigenvectors of the covariance matrix;
the eigenvalue vectors are arranged into a matrix according to the corresponding eigenvalue size from top to bottom and are taken as the matrix
Y=px is the coordinate-converted data;
averaging the data of each row of Y to obtain a coordinate M;
feature vector x 1 ,x 2 The plane determined by the coordinates M is the fitting plane of the block, x 3 Is collinear with the normal vector direction of the fitting plane;
demarcating x 3 A threshold value of the direction, wherein the threshold value can be selected to be in the centimeter level (preferably 0.05 m) according to the row width selection in the step a; this step can effectively remove the satellite noise points that are relatively close to the tunnel wall.
c. The geometric shape of the tunnel is close to a semi-ellipse shape, G and F represent points in two adjacent tunnel blocks containing tunnel walls, angles alpha and beta are respectively included angles between projection of a fitting plane of the tunnel block where G, F is located on a xoz plane and an x axis, when the distance between G and F is small, the difference between the angles alpha and beta is small, so that an approximate value of alpha can be determined through beta, an approximate fitting plane of the tunnel block where G is located is obtained, a threshold value is defined in the normal vector direction of the fitting plane, a lamp rod can be effectively deleted, and outliers with a certain distance from the tunnel wall such as a vehicle and the like can be effectively deleted;
the following operations are performed for each tunnel block:
taking a tunnel block as O, and finding the tunnel block (the "previous block" may be the tunnel block on the left side of O or the tunnel block on the lower side of O because the tunnel block containing the tunnel wall is needed) of the tunnel block O, namely O 1 According to O 1 Matrix P of eigenvectors of (a) 0 Calculate the tunnel block O 1 Matrix Y after coordinate conversion 0 Demarcating x, similar to the procedure of step b 3 A threshold in the straight direction.
d. Because many tunnel floors comprise sidewalks, the floors have height differences with tunnels, and the algorithm in the step c is only suitable for data with more than three layers from the floors; for a portion near the ground, a data slope range of the portion is determined based on a fitting plane formed by data of the third layer, thereby completely separating the tunnel wall from the ground.
Preferably, the reforming data is performed after the denoising:
the tunnel is divided into left and right sides according to the x-axis direction of the bounding box, and the z-axis direction is divided into layers (preferably, the z-axis direction is unchanged or the z-axis direction is divided into layers with the width of 0.5 m) by the two sides, so that the tunnel is divided into layer data.
Step 3, seam and surface repairing:
3.1, seam filling: the point clouds of adjacent slices or adjacent layers mark 5% points at the boundaries as a repeated point set;
since the subsequent triangularization is carried out according to the layer-to-layer points, gaps are formed on the surfaces among the sections and the layers, the processing method is that the point clouds of the adjacent slices or the adjacent layers are marked with 5% points at the boundaries as repeated point sets, and the two adjacent parts have the repeated point sets, so that the surfaces are connected when the surfaces are constructed;
3.2, face supplementing: counting 10% of points on the upper and lower sides of the point cloud of each layer (the value can be set according to specific conditions, and errors are caused by inadequacy), determining whether the upper part and the lower part of the layer are missing when the number of the points of the part is less than 5% of the layer, searching the upper layer and the lower layer of the layer until non-missing parts are searched, and adding a repeated point set in the non-missing layer, so that after the next step of surface construction, the whole tunnel curved surface is kept complete and continuous;
the invention complements the missing places because of the missing point cloud caused by the noise points of the appendages with large areas such as the lamp sticks and the like, which causes the missing of the large areas after the tunnel construction face.
Step 4, constructing a surface, which specifically comprises the following steps:
4.1, reducing the dimension of the three-dimensional point coordinates in each layer of data into two-dimensional data by using a Principal Component Analysis (PCA);
the method further comprises the following steps: because the three-dimensional surface construction algorithm has higher complexity compared with two dimensions, the three-dimensional points of each layer are reduced into two-dimensional points according to the fitting plane based on the consideration of modeling efficiency, and the method is similar to the step b:
firstly, three-dimensional points of each layer are formed into a matrix X of 3 rows and n columns according to columns 1 Matrix X 1 Zero mean value for each row of (2)The mean value of this row is subtracted;
solving covariance matrix
Wherein C is 1 Representing the obtained covariance matrix, wherein n is the number of points in the layer;
obtaining eigenvalues and corresponding eigenvectors of the covariance matrix;
the eigenvalue vectors are arranged into a matrix according to the corresponding eigenvalue size from top to bottom and are taken as the matrix
Y 1 ＝P 1 X 1 Namely, the data after coordinate conversion;
solving a matrix Y after coordinate conversion of each layer of data 1 ，Y 1 The first two lines of data are coordinate set Y after dimension reduction 1 ’；
4.2 two-dimensional data Y obtained for each layer of data 1 Performing Delaunay triangular planing, corresponding the two-dimensional points to the three-dimensional coordinates before dimension reduction, generating a triangular grid model, and combining grid models of different slices of different layers.
In this step, the Delaunay triangular planing process may be performed by using the prior art, and will not be described herein.
since the ground information is not important, all the original information about the ground is discarded and the ground for each slice is constructed with a quadrilateral.
The invention is not exhaustive and can be carried out by adopting the prior art.
The beneficial effects of the invention are as follows:
1. the method is based on the point cloud data production model only, other information except the point cloud information is not needed to be known to serve as auxiliary references, such as the original tunnel design condition, and the like, and the method is simple and convenient and high in applicability.
2. The method is used for fitting in blocks, and has the advantages of high fitting degree, good noise removing effect and good effect on complex tunnels.
3. The denoising points and the structure planes are performed on a small independent point set, so that the time efficiency is high and the parallelism degree is high.
4. The plurality of point cloud slices are independently processed in a parallel mode, so that details and efficiency can be considered, and the processing method has a good processing effect on point cloud data with large data quantity.
Drawings
FIG. 1 is a schematic cross-sectional view of a tunnel;
FIG. 2 is an original tunnel point cloud slice;
FIG. 3 is a point cloud slice after noise removal;
FIG. 4 is a graph showing the effect of the non-patched face;
FIG. 5 is a diagram showing the effect after completion;
the specific embodiment is as follows:
in order to make the technical problems, technical solutions and advantages to be solved by the present invention more apparent, the following detailed description will be given with reference to the accompanying drawings and specific embodiments, but not limited thereto, and the present invention is not fully described and is according to the conventional technology in the art.
A method of denoising and generating a visualization model for a tunnel point cloud, comprising:
step 1, collecting three-dimensional point clouds of a tunnel, namely slicing the point clouds of the original tunnel as shown in fig. 2, and preprocessing the data of the point clouds of the tunnel;
the three-dimensional laser scanner used in the invention is a TrimbleSX10 image scanner, the tunnel in the example is a Jinan open-cell tunnel, firstly, a three-dimensional point cloud of the tunnel is acquired by the three-dimensional laser scanner, in the acquisition process, the y-axis of a coordinate system is defined as the trend direction of the tunnel, the z-axis is vertical to the ground, and the x-axis obeys the right rule, as shown in fig. 1;
preprocessing the derived point cloud data, and deriving coordinate information of the point cloud when the point cloud data is derived;
the pretreatment process comprises the following steps:
1.1, generating point cloud slices: splitting the point cloud data according to the trend of the tunnel into point cloud slices with the length of 3 meters, and recording the maximum value of each slice coordinate axis in the splitting process as the boundary of the bounding box;
1.2, performing thinning treatment on each point cloud slice, wherein a plurality of point cloud slices are independently treated in a parallel mode during treatment:
performing thinning treatment on each point cloud slice by adopting the existing rasterization gravity center thinning method, wherein the grid side length is 0.02m;
1.3, determining a slice bounding box: taking the maximum value of the slice point cloud coordinates to determine a rectangular bounding box;
step 2, denoising:
because the tunnel condition is complex, the three-dimensional laser scanner is adopted to scan and comprises interference objects, the noise points are divided into noise points in the tunnel and noise points outside the tunnel according to the noise positions, the noise points in the tunnel are divided into outlier points caused by moving targets and accessory noise points caused by facilities in the tunnel according to the space distribution condition, the noise point removing processing is carried out according to the geometric characteristics of the existing point arrangement and the actual condition of the tunnel, and the noise in the tunnel and the noise outside the tunnel are respectively processed.
Preferably, the process of removing noise points outside the tunnel is as follows:
for each point cloud slice, the range of the bounding box in the x-axis direction is known, the bounding box is divided into columns with the width of 0.5m in the x-axis direction, the number of points in each column is counted, and the columns with the number of points less than 50 are deleted, so that the range of the data in the x-axis direction is reduced;
the same treatment is carried out in the z-axis direction, so that noise points outside the tunnel are removed, namely, the noise points are divided into point cloud columns in the z-axis direction according to 0.5m, the number of points in each column is counted, and columns with the number of points less than 50 are deleted;
preferably, the process of removing noise points in the tunnel comprises the following steps:
a. splitting each slice again, and splitting the slices into a plurality of rows and columns on a xoz plane again to form blocks of 0.5m x 0.5m, thereby forming tunnel blocks, namely point cloud blocks;
b. and carrying out coordinate transformation on each tunnel block by adopting a Principal Component Analysis (PCA) method, and finding a fitting plane:
c, forming a matrix X of 3 rows and m columns of the original three-dimensional data in the point cloud block obtained by processing in the step a according to the columns, wherein m represents the points in the point cloud block;
zero-equalizing each row of matrix X (representing data in the same axial direction), i.e. subtracting the average value of this row;
solving covariance matrix
Wherein C is the covariance matrix obtained, m is the number of points in the point cloud block;
obtaining eigenvalues and corresponding eigenvectors of the covariance matrix;
the eigenvalue vectors are arranged into a matrix according to the corresponding eigenvalue size from top to bottom and are taken as the matrix
Y=px is the coordinate-converted data;
averaging the data of each row of Y to obtain a coordinate M;
feature vector x 1 ,x 2 The plane determined by the coordinates M is the fitting plane of the block, x 3 Is collinear with the normal vector direction of the fitting plane;
demarcating x 3 And d, selecting a threshold value of the direction, wherein the threshold value is 0.05m according to the row width selection in the step a, and the step can effectively remove the noise point of the accessory close to the tunnel wall.
c. As shown in fig. 1, the geometric shape of the tunnel is close to a semi-ellipse, G and F represent points in two adjacent tunnel blocks containing tunnel walls, two broken lines in the figure represent projections of a fitting plane of the tunnel block where G, F is located on a xoz plane respectively, and when the distance between G and F is small, the difference between the angles alpha and beta is small, so that an approximate value of alpha can be determined through beta, thereby obtaining an approximate fitting plane of the tunnel block where G is located, and by defining a threshold value in the normal vector direction of the fitting plane, outliers of a certain distance between a lamp rod and the vehicle and the tunnel wall can be effectively deleted;
the following operations are performed for each tunnel block:
taking a tunnel block as O, and finding the tunnel block (the "previous block" may be the tunnel block on the left side of O or the tunnel block on the lower side of O because the tunnel block containing the tunnel wall is needed) of the tunnel block O, namely O 1 According to O 1 Matrix P of eigenvectors of (a) 0 Calculate the tunnel block O 1 Matrix Y after coordinate conversion 0 Demarcating x, similar to the procedure of step b 3 The threshold in the straight line direction, namely the normal vector threshold, is 0.1m, when the condition that the tunnel wall is missing is met, namely the block in which G is located is missing as shown in fig. 1, the threshold is accumulated by 0.05m until the block in which G is located is updated to the place where the block in which G is not missing is restored to 0.1 m.
d. Because many tunnel floors comprise sidewalks, the floors have height differences with tunnels, and the algorithm in the step c is only suitable for data with more than three layers from the floors; for a portion near the ground, a data slope range of the portion is determined based on a fitting plane formed by data of the third layer, thereby completely separating the tunnel wall from the ground.
Preferably, the reforming data is performed after the denoising:
the tunnel is equally divided into a left side and a right side according to the size of the X-axis direction of the bounding box, and the left side and the right side are not changed in the Z-axis direction, or the right side and the left side are divided into layer data according to layers with the width of 0.5 m.
FIG. 3 is a point cloud slice after noise removal;
step 3, seam and surface repairing:
FIG. 4 is a graph showing the effect of the joint and face repair without joint repair, specifically
3.1, seam filling: the point clouds of adjacent slices or adjacent layers mark 5% points at the boundaries as a repeated point set;
since the subsequent triangularization is carried out according to the layer-to-layer points, gaps are formed on the surfaces among the sections and the layers, the processing method is that the point clouds of the adjacent slices or the adjacent layers are marked with 5% points at the boundaries as repeated point sets, and the two adjacent parts have the repeated point sets, so that the surfaces are connected when the surfaces are constructed;
3.2, face supplementing: counting 10% of points on the upper and lower sides of the point cloud of each layer (the value can be set according to specific conditions, and errors are caused by inadequacy), determining whether the upper part and the lower part of the layer are missing when the number of the points of the part is less than 5% of the layer, searching the upper layer and the lower layer of the layer until non-missing parts are searched, and adding a repeated point set in the non-missing layer, so that after the next step of surface construction, the whole tunnel curved surface is kept complete and continuous;
the invention complements the missing places because of the missing point cloud caused by the noise points of the appendages with large areas such as the lamp sticks and the like, which causes the missing of the large areas after the tunnel construction face.
Step 4, constructing a surface, which specifically comprises the following steps:
4.1, reducing the dimension of the three-dimensional point coordinates in each layer of data into two-dimensional data by using a Principal Component Analysis (PCA);
the method further comprises the following steps: because the three-dimensional surface construction algorithm has higher complexity compared with two dimensions, the three-dimensional points of each layer are reduced into two-dimensional points according to the fitting plane based on the consideration of modeling efficiency, and the method is similar to the step b:
firstly, three-dimensional points of each layer are formed into a matrix X of 3 rows and n columns according to columns 1 Matrix X 1 Zero-equalizing, i.e. subtracting the average value of the line;
solving covariance matrix
Wherein C is 1 Representing the obtained covariance matrix, wherein n is the number of points in the layer;
obtaining eigenvalues and corresponding eigenvectors of the covariance matrix;
the eigenvalue vectors are arranged into a matrix according to the corresponding eigenvalue size from top to bottom and are taken as the matrix
Y 1 ＝P 1 X 1 Namely coordinate conversionPost data;
solving a matrix Y after coordinate conversion of each layer of data 1 ，Y 1 The first two lines of data are coordinate set Y after dimension reduction 1 ’；
4.2 two-dimensional data Y obtained for each layer of data 1 Performing Delaunay triangular planing, corresponding the two-dimensional points to the three-dimensional coordinates before dimension reduction, generating a triangular grid model, and combining grid models of different slices of different layers.
since the ground information is not important, all the original information about the ground is discarded, and for the ground, it is sufficient to construct the ground for each slice with a quadrilateral.
Fig. 5 is an effect diagram after the treatment by the present invention is completed.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (7)
1. A method for denoising and generating a visualization model for a tunnel point cloud, comprising:
step 1, collecting a tunnel three-dimensional point cloud, and preprocessing tunnel point cloud data;
firstly, a three-dimensional laser scanner is used for collecting three-dimensional point clouds of a tunnel, a y axis of a coordinate system is defined as a direction of trend of the tunnel in the collecting process, a z axis is perpendicular to the ground, and an x axis obeys a right rule;
preprocessing the derived point cloud data, and deriving coordinate information of the point cloud when the point cloud data is derived;
step 2, denoising:
because the tunnel condition is complex, the three-dimensional laser scanner is adopted to scan the tunnel and comprises interference objects, the noise points are divided into noise points in the tunnel and noise points outside the tunnel according to the noise positions, the noise points in the tunnel are divided into outlier points caused by moving targets and accessory noise points caused by facilities in the tunnel according to the space distribution condition, the noise point removing treatment is carried out according to the geometric characteristics of the existing point arrangement and the actual condition of the tunnel, and the noise in the tunnel and the noise outside the tunnel are respectively treated;
step 3, seam and surface repairing;
step 4, constructing a surface;
step 5, adding ground:
constructing the ground of each slice by using a quadrilateral;
in step 1, the pretreatment process comprises the following steps:
1.1, generating point cloud slices: splitting the point cloud data according to the trend of the tunnel into point cloud slices with the length of 3-5 meters;
1.2, performing thinning treatment on each point cloud slice, wherein a plurality of point cloud slices are independently treated in a parallel mode during treatment:
the existing rasterization gravity center thinning method is adopted to carry out thinning treatment on each point cloud slice, the selection of the grid side length depends on the precision requirement on the final grid, the smaller the grid side length is, the more points remain, and the smoother the generated grid model is;
1.3, determining a slice bounding box: and taking the maximum value of the slice point cloud coordinates to determine a rectangular bounding box.
2. The method for denoising and generating a visualized model according to claim 1, wherein in step 2, the process of removing the noise points outside the tunnel is:
for each point cloud slice, dividing the range of the bounding box in the x-axis direction into point cloud columns according to the decimeter level in the direction, counting the number of points in each column, and deleting the columns with the number of points less than 50, wherein the number of points in each column is 0.5 meter;
the same process is performed in the z-axis direction, namely, the columns are divided into point cloud columns in the z-axis according to decimeter level, the width of the point cloud columns is 0.5m, the number of points in each column is counted, and the columns with the number of points less than 50 are deleted.
3. The method for denoising tunnel point cloud and generating visual model according to claim 2, wherein the process of removing noise points in the tunnel comprises the steps of:
a. splitting each slice again, and splitting the slices into a plurality of rows and columns again on a xoz plane, so as to form a tunnel block, namely a point cloud block;
b. and carrying out coordinate conversion on each tunnel block by adopting a principal component analysis method, and finding out a fitting plane:
c, forming a matrix X of 3 rows and m columns of the original three-dimensional data in the point cloud block obtained by processing in the step a according to the columns, wherein m represents the points in the point cloud block;
zero-equalizing each row of the matrix X, namely subtracting the average value of the row;
solving covariance matrix
Wherein C is the covariance matrix obtained, m is the number of points in the point cloud block;
obtaining eigenvalues and corresponding eigenvectors of the covariance matrix;
the eigenvalue vectors are arranged into a matrix according to the corresponding eigenvalue size from top to bottom and are taken as the matrix
Y=px is the coordinate-converted data;
averaging the data of each row of Y to obtain a coordinate M;
feature vector x 1 ,x 2 The plane determined by the coordinates M is the fitting plane of the block, x 3 Is collinear with the normal vector direction of the fitting plane;
demarcating x 3 A threshold value of the direction, wherein the threshold value is selected according to the row and column width selection in the step a, and is selected to be 0.05m in centimeter level;
c. the geometric shape of the tunnel is close to a semi-ellipse shape, G and F represent points in two adjacent tunnel blocks containing tunnel walls, angles alpha and beta are respectively included angles between projection of a fitting plane of the tunnel block where G, F is located on a xoz plane and an x axis, when the distance between G and F is small, the difference between the angles alpha and beta is small, so that an approximate value of alpha is determined through beta, an approximate fitting plane of the tunnel block where G is located is obtained, a threshold value is defined in the normal vector direction of the fitting plane, a lamp rod is effectively deleted, and an outlier with a certain distance from the tunnel wall such as a vehicle is removed;
the following operations are performed for each tunnel block:
taking a tunnel block as O, and finding the tunnel block containing tunnel wall before the tunnel block O, namely O 1 According to O 1 Matrix P of eigenvectors of (a) 0 Calculate the tunnel block O 1 Matrix Y after coordinate conversion 0 Demarcating x, similar to the procedure of step b 3 A threshold value in the straight line direction;
d. because many tunnel floors comprise sidewalks, the floors have height differences with tunnels, and the algorithm in the step c is only suitable for data with more than three layers from the floors; for a portion near the ground, a data slope range of the portion is determined based on a fitting plane formed by data of the third layer, thereby completely separating the tunnel wall from the ground.
4. The method for denoising and generating a visualized model according to claim 3, wherein in step a, the widths of the rows and columns are about 0.5m, and the rows and columns are split into blocks of 0.5m by 0.5 m.
5. The method of denoising and generating a visualization model of a tunnel point cloud of claim 4, wherein the denoising is followed by reforming data:
dividing the tunnel into left and right sides according to the size of the bounding box in the x-axis direction, and dividing the z-axis direction into layers by the two sides: the z-axis direction is unchanged, or the layer data is divided according to the layer with the width of 0.5 m.
6. The method of denoising and generating a visualization model of tunnel point cloud according to claim 5, wherein step 3 is further:
3.1, seam filling: the point clouds of adjacent slices or adjacent layers mark 5% points at the boundaries as a repeated point set;
since the subsequent triangularization is carried out according to the layer-to-layer points, gaps are formed on the surfaces among the sections and the layers, and the processing method is that the point clouds of the adjacent slices or the adjacent layers are marked with 5% points at the boundaries as repeated point sets, and the two adjacent parts have the repeated point sets, so that the surfaces are connected when the surfaces are constructed;
3.2, face supplementing: and counting 10% of points on the upper and lower sides of the point cloud of each layer, determining whether the upper part and the lower part of the layer are missing when the number of the points of the part is less than 5% of the layer, searching the upper and lower layers of the layer until the non-missing part is searched, and adding a repeated point set in the non-missing layer, so that the whole tunnel curved surface is kept complete and continuous after the next step of surface construction.
7. The method of denoising and generating a visualization model of tunnel point cloud according to claim 6, wherein step 4 is further:
4.1, dimension reduction is carried out on three-dimensional point coordinates in each layer of data into two-dimensional data by using a principal component analysis method:
firstly, three-dimensional points of each layer are formed into a matrix X of 3 rows and n columns according to columns 1 Matrix X 1 Zero-equalizing, i.e. subtracting the average value of the line;
solving covariance matrix
Wherein C is 1 Representing the obtained covariance matrix, wherein n is the number of points in the layer;
obtaining eigenvalues and corresponding eigenvectors of the covariance matrix;
the eigenvalue vectors are arranged into a matrix according to the corresponding eigenvalue size from top to bottom and are taken as the matrix
Y 1 ＝P 1 X 1 Namely, the data after coordinate conversion;
solving for each layer of data after coordinate conversionMatrix Y 1 ，Y 1 The first two lines of data are coordinate set Y after dimension reduction 1 ’；
4.2 two-dimensional data Y obtained for each layer of data 1 Performing Delaunay triangular planing, corresponding the two-dimensional points to the three-dimensional coordinates before dimension reduction, generating a triangular grid model, and combining grid models of different slices of different layers.
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