CN112164081B - Vehicle-mounted LiDAR point cloud railway cross section contour extraction method - Google Patents
Vehicle-mounted LiDAR point cloud railway cross section contour extraction method Download PDFInfo
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- CN112164081B CN112164081B CN202011061251.2A CN202011061251A CN112164081B CN 112164081 B CN112164081 B CN 112164081B CN 202011061251 A CN202011061251 A CN 202011061251A CN 112164081 B CN112164081 B CN 112164081B
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Abstract
The invention discloses a vehicle-mounted LiDAR point cloud railway cross section contour extraction method, which comprises the following steps of S1, carrying out joint calculation on GPS and IMU data acquired by a vehicle-mounted LiDAR system, and acquiring POS data of a mobile platform as POS lines; s2, forming a curve according to the data points on the POS line and the neighborhood thereof, and cutting the vehicle-mounted LiDAR point cloud to obtain a railway cross section; s3, extracting the profile of the railway cross section by adopting an Alpha Shapes algorithm. The scheme realizes the outline extraction of the point cloud data of the cross section of the railway based on the POS line extraction and the Alpha Shapes algorithm, the whole process does not involve a complex algorithm, the operation difficulty can be reduced, the data processing time can be prolonged, and the method has the characteristics of high precision and high reliability due to the data source and the truly acquired rail data.
Description
Technical Field
The invention belongs to the field of retesting of railway space information data, and particularly relates to a vehicle-mounted LiDAR point cloud railway cross section profile extraction method.
Background
The detection of railway facilities, such as railway rails, catenary, and other railway infrastructure, is critical. The cross section profile state of the steel rail is a main factor affecting the running safety of the train, the wheel-rail relationship, the maintenance quality of the steel rail and the service life of the steel rail. The method can be used for rapidly detecting the cross-sectional profile of the steel rail, evaluating and grasping the difference between the cross-sectional profile and the standard profile or the specified profile, and has direct guiding effect on the cross-sectional profile state of the steel rail and maintenance such as steel rail polishing.
Currently, non-contact measuring tools and contact measuring tools based on rollers are mainly used for measuring the cross-sectional profile of a steel rail and are based on electronic and optical instruments such as laser. Although the precision of the precise tools is higher than that of manual measuring instruments, the precise tools are complex to operate, have long measuring and data processing time, are difficult to rapidly evaluate the cross-section profile of the steel rail, and are high in manufacturing cost and inconvenient to carry.
Disclosure of Invention
Aiming at the defects in the prior art, the method for extracting the cross section profile of the vehicle-mounted LiDAR point cloud railway solves the problem of long time consumption in the conventional extraction of the cross section of the railway.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the invention provides a vehicle-mounted LiDAR point cloud railway cross section profile extraction method, which comprises the following steps:
s1, carrying out joint calculation on GPS and IMU data acquired by a vehicle-mounted LiDAR system, and acquiring POS data of a mobile platform as POS lines;
s2, forming a curve according to the data points on the POS line and the neighborhood thereof, and cutting the vehicle-mounted LiDAR point cloud to obtain a railway cross section;
s3, extracting the profile of the railway cross section by adopting an Alpha Shapes algorithm.
Further, the step S3 further includes:
s31, carrying out planar two-dimensional on point cloud data of a railway cross section to obtain a point cloud set S;
s32, selecting an unremoved point cloud P from the point cloud set S i And in the form of point cloud P i As the center of circle P o1 A preset threshold value is used as a radius to form a circle X, and all point clouds in the circle X are adopted to form a point set S 1 ；
S33, at point set S 1 Selecting an unexchanged point P j Constructing a passing point cloud P i Sum point P j A circle Y with a radius of 1/2 of a preset threshold value;
s34, judging whether a non-point set S exists in the circle Y 1 If yes, returning to the step S33; otherwise, step S35 is entered;
s35, point cloud P i Sum point P j Marking the outline points, judging whether the point clouds in the point cloud set S are traversed, and if yes, outputting the marked outline points; otherwise, the process returns to step S32.
The beneficial effects of the invention are as follows: in the profile extraction process, the scheme can directly adopt the data acquired by the vehicle-mounted LiDAR system arranged on the train, the data is easy to obtain, and expensive acquisition equipment is not required to be introduced; the cutting of the railway cross section can be rapidly realized through the POS line, a good data set is provided for the cross section profile extraction, and a new thought is provided for the point cloud data cross section cutting.
The contour extraction of the point cloud data of the cross section of the railway is realized based on the combination of the cross section of the railway and the Alpha Shapes algorithm, the whole process does not involve a complex algorithm, the operation difficulty can be reduced, the data processing time can be prolonged, and the characteristics of high precision and high reliability are realized by combining the data source with the truly acquired rail data.
In addition, the extraction method enriches the variety of the railway track information extraction algorithm, and can be further expanded to be applied to information extraction of other various railway works and electric equipment.
Drawings
Fig. 1 is a flowchart of a vehicle-mounted LiDAR point cloud railway cross-section profile extraction method.
Fig. 2 is a diagram showing a spatial positional relationship between POS line data and point cloud data used in the present invention.
FIG. 3 is a schematic diagram of the principle of the Alpha Shapes algorithm in the scheme; wherein, (a) is an Alpha Shapes model schematic diagram, and (b) is a construction schematic diagram of a circle Y.
Fig. 4 is a diagram of contour extraction results of a cross-sectional point cloud under different parameters in the present solution.
Fig. 5 is a graph of the extraction results of the 10 transverse contours extracted experimentally in this protocol.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
Referring to fig. 1, fig. 1 illustrates a vehicle-mounted LiDAR point cloud railway cross-sectional profile extraction method; as shown in fig. 1, the method includes steps S1 to S3.
In step S1, GPS and IMU data acquired by a vehicle-mounted LiDAR system are jointly calculated, and POS data of a mobile platform are obtained to be used as POS lines; the POS data spatially exhibits a characteristic of being approximately parallel to the train travel path, with its projection on the horizontal plane being close to the road center line, as shown in fig. 2.
In step S2, a curve is formed according to data points on the POS line and the neighborhood thereof, and the vehicle-mounted LiDAR point cloud is cut to obtain a railway cross section; when the railway cross-sectional point cloud is cut thin enough, it can be approximated as a two-dimensional planar data.
In implementation, the step S2 preferably further includes:
s21, selecting any data point on a POS line, adopting the data point and a neighborhood thereof (delta neighborhood of a point a, wherein delta is a positive number, an open interval (a-delta, a+delta) is called delta neighborhood of the point a, the point a is called the center of the neighborhood, delta is called the radius of the neighborhood), and forming a curve S, wherein the expression of the curve S is as follows:
s22, obtaining tangential vectors (1, dF/dx, dG/dx) of the data points on the curve by solving the partial derivatives (specifically, solving the partial derivatives on the data points to x), and extracting normal vectors of the railway cross section by adopting the tangential vectors;
s23, generating a space cross section by adopting a normal vector, searching point cloud data with a threshold value set before and after the space cross section, and taking the searched point cloud data as a railway cross section.
When the method is implemented, the set threshold value is preferably half of the thickness of the preset railway cross section; when the railway cross section is formed by cutting, the adjacent railway cross sections are arranged at equal intervals.
In step S3, the contour of the railway cross section is extracted by adopting an Alpha Shapes algorithm.
For the Alpha Shapes model, a point set S contains n points, and n (n-1) line segments can be formed among the n points, as shown in fig. 3 (a); the Alpha Shapes algorithm principle is as follows: a circle with a radius a rolls outside the point set S, and when the value of a is large to a certain extent, the circle cannot reach the inside of S, and the travelling track of the circle is the outline of the point set S. Two cases of extraction result:
as the value of a decreases and tends to 0, each point is independently a subset, the subset contour being each point itself; as a increases and tends to infinity, all points are a set (i.e., point set S) whose contour, alpha Shape, is the convex hull of S. And only when the point density in the point set S is relatively uniform, the extraction of the inner contour line and the outer contour line of the point set S can be completed by taking a proper value of a.
In one embodiment of the present invention, the step S3 further includes:
s31, carrying out planar two-dimensional on point cloud data of a railway cross section to obtain a point cloud set S;
s32, selecting an unremoved point cloud P from the point cloud set S i And in the form of point cloud P i As the center of circle P o1 A preset threshold value is used as a radius to form a circle X, and all point clouds in the circle X are adopted to form a point set S 1 ；
S33, at point set S 1 Selecting an unexchanged point P j Constructing a passing point cloud P i Sum point P j And (2) andcircle Y with radius 1/2 of preset threshold is shown in fig. 3 (b).
In implementation, the scheme is preferably that when the circle Y is constructed, the circle center P of the circle Y o2 The calculation formula of (2) is as follows:
wherein, (x) 0 、y 0 ) Is the center of circle P o2 Coordinates of (c); (x) 1 ，y 1 ) And (x) 2 ，y 2 ) Respectively, are point clouds P i Sum point P j Coordinates of (c);
is a point cloud P i Sum point P j Is a spatial distance of (2); a is a 1/2 preset threshold; h is an intermediate parameter.
In practice, the value of a is preferably in the range of 0.01 to 1.
S34, judging whether a non-point set S exists in the circle Y 1 If yes, returning to the step S33; otherwise, step S35 is entered;
s35, point cloud P i Sum point P j Marking the outline points, judging whether the point clouds in the point cloud set S are traversed, and if yes, outputting the marked outline points; otherwise, the process returns to step S32.
In order to verify the effect of the Alpha Shapes algorithm, the following experiment is designed to illustrate the effect of the Alpha Shapes algorithm:
and acquiring test data, carrying out railway cross section interception on the test data, and according to the set parameter value, intercepting 10 POS point data and 10 railway cross section point cloud data on the railway point cloud data with the length of 100m at the same time, wherein when the railway cross section point cloud is cut to be thin enough, the railway cross section point cloud data can be approximately seen as two-dimensional plane data.
By continuously adjusting the value of a, profile extraction is performed on the track cross section, and experiments are performed by selecting 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.25, 0.5 and 1 as the value of a, and analysis and evaluation are performed by using the cross section profile extraction effect, and the results are shown in fig. 4. When the cross-sectional profile extraction result is judged by visual analysis and a is found to be 0.01, the cross-sectional profile extraction effect is the best, and for this embodiment, a is preferably selected to be 0.1.
By adjusting the value of a, the extracted profile can reflect structural characteristics of steel rails, ballast shoulders, road shoulders and the like on the cross sections, a=0.1 is selected as an optimal parameter according to the experimental result of a, and profile extraction is carried out on 10 railway cross sections, wherein the extraction result is shown in fig. 5.
As can be seen from FIG. 5, when the optimal value of a is taken, the railway cross-sectional profile can be obtained more clearly and completely by adopting the scheme to extract the railway cross-sectional profile.
Claims (4)
1. The vehicle-mounted LiDAR point cloud railway cross section profile extraction method is characterized by comprising the following steps of:
s1, carrying out joint calculation on GPS and IMU data acquired by a vehicle-mounted LiDAR system, and acquiring POS data of a mobile platform as POS lines;
s2, forming a curve according to the data points on the POS line and the neighborhood thereof, and cutting the vehicle-mounted LiDAR point cloud to obtain a railway cross section;
s3, extracting the profile of the railway cross section by adopting an Alpha Shapes algorithm;
the step S3 further includes:
s31, carrying out planar two-dimensional on the point cloud data of the railway cross section to obtain a point cloud setS；
S32, in-point cloud collectionSSelecting one non-traversed point cloudP i And by point cloudP i As the center of a circleP o1 A preset threshold value is used as a radius to form a circle X, and all point clouds in the circle X are adopted to form a point setS 1 ；
S33, in-point setS 1 Selecting an unremoved pointP j Constructing an overdrivingPoint cloudP i Sum pointP j A circle Y with a radius of 1/2 of a preset threshold value;
s34, judging whether a non-point set exists in the circle YS 1 If yes, returning to the step S33; otherwise, step S35 is entered;
s35, point cloud is generatedP i Sum pointP j Marking as contour points, and judging a point cloud setSWhether the point clouds in the map are traversed or not, if so, outputting marked contour points; otherwise, returning to the step S32;
when constructing the circle Y, the center of the circleP o2 The calculation formula of (2) is as follows:
wherein, the method comprises the following steps ofx 0 、y 0 ) As the center of a circleP o2 Coordinates of (c); (x 1 ，y 1 ) And%x 2 ，y 2 ) Respectively, point cloudsP i Sum pointP j Coordinates of (c);
is a point cloudP i Sum pointP j Is a spatial distance of (2);apresetting a threshold value for 1/2;His an intermediate parameter.
2. The method for extracting the cross-sectional profile of the vehicle-mounted LiDAR point cloud railway according to claim 1, wherein the step S2 further comprises:
s21, selecting any data point on a POS line, and adopting the data point and a neighborhood thereof to form a curve;
s22, obtaining tangential vectors of data points on a curve through deviation derivation, and extracting normal vectors of a railway cross section by adopting the tangential vectors;
s23, generating a space cross section by adopting a normal vector, searching point cloud data with a threshold value set before and after the space cross section, and taking the searched point cloud data as a railway cross section.
3. The method for extracting the cross-sectional profile of the vehicle-mounted LiDAR point cloud railway according to claim 2, wherein the set threshold value is half of the preset cross-sectional thickness of the railway; when the railway cross section is formed by cutting, the adjacent railway cross sections are arranged at equal intervals.
4. The method for extracting the cross-sectional profile of the vehicle-mounted LiDAR point cloud railway, which is characterized in that,athe value range of (2) is 0.01-1.
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| CN113189564B (en) * | 2021-07-01 | 2021-09-07 | 西南交通大学 | Train track point cloud extraction method |
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