CN112581579B - Method for extracting point cloud data of magnetic suspension sliding surface - Google Patents
Method for extracting point cloud data of magnetic suspension sliding surface Download PDFInfo
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Abstract
The invention relates to a method for extracting point cloud data of a magnetic suspension sliding surface, which comprises the following steps: acquiring point cloud data; converting the point cloud data from a space coordinate system to an orbit coordinate system to obtain a fitting range of a sliding surface; constructing a solving model to obtain an initial plane; calculating the distance from all point cloud data in the fitting range to the initial plane, and removing the point cloud data beyond the distance according to a first set threshold value to obtain preprocessed point cloud data; calculating the mean value of the preprocessed point cloud data, calculating the residual error and the standard deviation of the preprocessed point cloud data, and removing the point cloud data of which the residual error is greater than a second set threshold or the standard deviation is greater than a third threshold to obtain single-segment point cloud data; and fitting the single-section point cloud data to obtain integral point cloud data of the magnetic suspension sliding surface. Compared with the prior art, the automatic extraction of the point cloud data of the magnetic suspension sliding surface is realized, the final noise of the point cloud data of the magnetic suspension sliding surface can be effectively reduced, and the defect that the fitting range cannot be matched with point cloud due to the complex environment of the magnetic suspension track is overcome.
Description
Technical Field
The invention relates to the field of magnetic suspension point cloud data extraction, in particular to a magnetic suspension sliding surface point cloud data extraction method.
Background
Magnetic levitation traffic has become an important development direction for rail traffic with the advantages of low noise, fast running speed, low maintenance cost, small running vibration, strong resistance to severe environment and the like. The magnetic suspension sliding surface is a functional surface positioned on two sides of the track, and has an important function for guaranteeing the stable operation of the magnetic suspension train. Due to the high-speed running of the maglev train, direct contact exists in a local area, and a maglev sliding surface can generate certain nonlinear deformation. Therefore, the method for detecting the magnetic suspension sliding surface regularly has extremely important significance for the safe operation of the magnetic suspension train. The magnetic suspension track is measured by adopting a mobile laser scanning mode, a large amount of three-dimensional point cloud information of the magnetic suspension track can be quickly and accurately acquired, and the point cloud information comprises a sliding surface, a guide surface, a stator surface and point cloud information of various structures. The problem of how to extract the point cloud of the sliding surface from the point cloud information of the sliding surface, the guide surface, the stator surface and various structures needs to be solved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for extracting point cloud data of a magnetic suspension sliding surface.
The purpose of the invention can be realized by the following technical scheme:
a method for extracting point cloud data of a magnetic suspension sliding surface comprises the following steps:
s1: acquiring point cloud data;
s2: converting the point cloud data from a space coordinate system to a track coordinate system, and obtaining a fitting range of a sliding surface according to design parameters of the magnetic suspension sliding surface;
s3: constructing a solving model according to the difference between a function value and an actual value of a space plane equation constructed by point cloud data in an orbit coordinate system to obtain an initial plane;
s4: calculating the distance from all point cloud data in the fitting range to the initial plane, and removing unreasonable point cloud data according to a first set threshold value to obtain preprocessed point cloud data;
s5: calculating the mean value of the preprocessed point cloud data, taking the mean value as a standard value, calculating the residual error and the standard deviation of the preprocessed point cloud data, and removing the point cloud data of which the residual error is greater than a second set threshold value or the standard deviation is greater than a third threshold value to obtain single-segment point cloud data;
s6: and fitting the single-section point cloud data to obtain the point cloud data of the magnetic suspension sliding surface.
In S1, the point cloud data is obtained through a laser scanner.
In S2, the track coordinate system comprises an axis I, an axis q and an axis h, wherein the axis I represents mileage, the axis q represents offset from a point to a center line, the axis h represents elevation, and an origin of the track coordinate system is a central point of an initial position.
And S3, normally distributing the elevations of the point cloud data in the fitting range, removing the point cloud data with lower confidence coefficient according to the elevations, and constructing a space plane equation by using the rest point cloud data.
In S3, the spatial plane equation is:
p(l,q,h)＝al+bq+ch+m＝0
wherein, a, b and c are normal vectors of a plane equation, l, q and h are mileage, offset and elevation respectively, and m is the distance from an origin to a space plane.
In S3, the expression of the solution model is:
wherein e is the difference between the function value and the actual value, and i represents the number of the point cloud data.
In S4, the first threshold is the prior distance t from the point cloud data to the initial plane.
In S5, the second threshold is a variance σ of the residual error.
The third threshold is three times the standard deviation.
And S6, fitting the single-segment point cloud data along the axis I of the track coordinate system.
Compared with the prior art, the invention has the following advantages:
(1) Based on the acquired point cloud data, the automatic extraction of the point cloud data of the magnetic suspension sliding surface can be quickly and efficiently realized; and abnormal point cloud data are removed for multiple times, so that the final noise of the point cloud data of the magnetic suspension sliding surface can be effectively reduced.
(2) The sliding surface is extracted by a sectional fitting mode, and the defect that the fitting range cannot be matched with point clouds due to the complex environment of the magnetic suspension track is overcome.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of point cloud data acquired by a laser scanner;
FIG. 3 is a schematic diagram of the orbital coordinate system of the present invention;
FIG. 4 is a comparison of the initial plane and a single segment point cloud data plane.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
The present embodiment provides a method for extracting point cloud data of a magnetic levitation sliding surface, as shown in fig. 1, the method includes the following steps:
s1: acquiring point cloud data;
s2: converting the point cloud data from a space coordinate system to a track coordinate system, and obtaining a fitting range of a sliding surface according to design parameters of the magnetic suspension sliding surface;
s3: constructing a solving model according to the difference between a function value and an actual value of a space plane equation constructed by point cloud data in an orbit coordinate system to obtain an initial plane;
s4: calculating the distance from all point cloud data in the fitting range to the initial plane, and removing unreasonable point cloud data according to a first set threshold value to obtain preprocessed point cloud data;
s5: calculating the mean value of the preprocessed point cloud data, taking the mean value as a standard value, calculating the residual error and the standard deviation of the preprocessed point cloud data, and removing the point cloud data of which the residual error is greater than a second set threshold value or the standard deviation is greater than a third threshold value to obtain single-segment point cloud data;
s6: and fitting the single-section point cloud data to obtain the point cloud data of the magnetic suspension sliding surface.
Specifically, the method comprises the following steps:
in S1, a high-precision laser scanner is adopted to dynamically scan the magnetic suspension track to obtain point cloud data.
In S2, in order to determine a fitting range, engineering coordinates (x, y, h) need to be converted into track coordinates (1, q, h), wherein a track coordinate system comprises an axis I, an axis q and an axis h, and the axis I represents mileage and is overlapped with a designed track surface central line; the q axis represents the offset from the point to the central line and is perpendicular to the central line of the rail surface; the h axis represents the elevation, and the origin of the track coordinate system is the central point of the initial position; the established orbital coordinate system is shown in fig. 3.
According to the design parameters of the magnetic levitation sliding surface, obtaining the offset range [ q ] from the boundary of two sides of the sliding surface to the central line under the design scheme 1 ',q 2 ']Given a certain threshold range, [ l ] 1 ',l 2 ']Ensuring the plane characteristic of the sliding surface in a small area range according to [ q ] 1 ',q 2 ']And [ l 1 ',l 2 ']And obtaining a fitting range.
S3, because the point cloud data in the area are basically concentrated near the sliding surface, the elevation of the point cloud data in the fitting range is normally distributed, and the height threshold range [ h ] is determined 1 ',h 2 ']And after removing the point cloud data with lower confidence coefficient according to the elevation, constructing a space plane equation by using the residual point cloud data.
In S3, the spatial plane equation is:
p(l,q,h)＝al+bq+ch+m＝0
wherein, a, b and c are normal vectors of a plane equation, l, q and h are mileage, offset and elevation respectively, and m is the distance from an origin to a space plane.
In S3, the expression of the solution model is as follows:
wherein e is the difference between the function value and the actual value, i represents the number of point cloud data, and the initial plane equation coefficient is obtained by solving the model
The initial plane obtained by the method is a sliding plane in an ideal state, and in fact, each coordinate direction has a certain error, so that the initial plane has certain errorsAnd cannot be used as a best-fit plane for the sliding surface.
In S4, the first threshold is the prior distance t from the point cloud data to the initial plane; in S5, the second threshold is a variance σ of the residual error.
The third threshold is three times the standard deviation.
In S6, the plane parameter of the single-segment point cloud data is represented as (a) i ,b i ,c i ) In fig. 4, the left side is an initial plane, and the right side is a plane of single-segment point cloud data; when the magnetic suspension line is designed, the influence of the factors of superelevation, curvature and gradient is caused, the real state of the track plane cannot be simulated by the fitting of the integral point cloud data, the way of piecewise fitting the plane is adopted, the fitting of single-section point cloud data is carried out along the axis I of the track coordinate system, the stepping distance is delta 1 every time, and then the step S3 is carried out until the extraction of the integral sliding surface point cloud data is completed.
The method for extracting the point cloud data of the magnetic suspension sliding surface can effectively eliminate abnormal values, realize accurate extraction of the information of the magnetic suspension sliding surface and provide a basis for detection and evaluation of the magnetic suspension track sliding surface.
Claims (7)
1. A method for extracting point cloud data of a magnetic suspension sliding surface is characterized by comprising the following steps:
s1: acquiring point cloud data;
s2: converting the point cloud data from a space coordinate system to a track coordinate system, and obtaining a fitting range of a sliding surface according to design parameters of the magnetic suspension sliding surface;
s3: constructing a solving model according to the difference between a function value and an actual value of a space plane equation constructed according to the point cloud data in an orbit coordinate system to obtain an initial plane;
s4: calculating the distance from all point cloud data in the fitting range to the initial plane, and removing unreasonable point cloud data according to a first set threshold value to obtain preprocessed point cloud data;
s5: calculating the mean value of the preprocessed point cloud data, taking the mean value as a standard value, calculating the residual error and the standard deviation of the preprocessed point cloud data, and removing the point cloud data of which the residual error is greater than a second set threshold value or the standard deviation is greater than a third threshold value to obtain single-segment point cloud data;
s6: fitting the single-section point cloud data to obtain point cloud data of a magnetic suspension sliding surface;
in S2, the track coordinate system comprises an axis I, an axis q and an axis h, wherein the axis I represents mileage, the axis q represents offset from a point to a center line, the axis h represents elevation, and an origin of the track coordinate system is a central point of an initial position;
in S3, the spatial plane equation is:
p(l,q,h)＝al+bq+ch+m＝0
wherein a, b and c are normal vectors of a plane equation, l, q and h are mileage, offset and elevation respectively, and m is the distance from an original point to a space plane;
in S3, the expression of the solution model is:
wherein e is the difference between the function value and the actual value, and i represents the number of the point cloud data.
2. The method for extracting the cloud data of the magnetic levitation sliding surface as claimed in claim 1, wherein in S1, the cloud data is obtained by a laser scanner.
3. The method for extracting the point cloud data of the magnetic levitation sliding surface according to claim 1, wherein in S3, the elevation of the point cloud data in the fitting range is normally distributed, and after the point cloud data with lower confidence coefficient is removed according to the elevation, a spatial plane equation is constructed by using the rest point cloud data.
4. The method for extracting point cloud data of a magnetic levitation sliding surface as claimed in claim 1, wherein in S4, the first set threshold is a prior distance t from the point cloud data to an initial plane.
5. The method as claimed in claim 1, wherein in S5, the second threshold is a variance σ of the residual error.
6. The method as claimed in claim 1, wherein the third threshold is three times the standard deviation.
7. The method for extracting point cloud data of a magnetic levitation sliding surface as claimed in claim 1, wherein in S6, fitting of a single segment of point cloud data is performed along an axis l of a track coordinate system.
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