CN113189564A - Train track point cloud extraction method - Google Patents
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- CN113189564A CN113189564A CN202110741902.0A CN202110741902A CN113189564A CN 113189564 A CN113189564 A CN 113189564A CN 202110741902 A CN202110741902 A CN 202110741902A CN 113189564 A CN113189564 A CN 113189564A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
- G06T2207/30252—Vehicle exterior; Vicinity of vehicle
- G06T2207/30256—Lane; Road marking
Abstract
The invention discloses a train track point cloud extraction method, which comprises the steps of obtaining POS data and original point cloud data through a vehicle-mounted laser radar system, then determining end points and extension directions of the POS data, wherein the end points comprise a first end point and a second end point, and extracting track point cloud through the end points, the extension directions and the original point cloud data, wherein two points with the longest distance in the POS data are used as the end points, the second end point is the end point far away from a train, the first end point is the end point close to the train, then a two-dimensional coordinate system is established, and the first end point and the second end point are both located in the two-dimensional coordinate system, so that the track point cloud can be extracted quickly and accurately under the condition of complex terrain.
Description
Technical Field
The invention belongs to the technical field of train track point cloud processing, and particularly relates to a train track point cloud extraction method.
Background
When the vehicle-mounted LiDAR (laser radar) acquires the railway information along the line, all the surrounding land and feature information which can reflect laser back, such as buildings, railway areas, vegetation, power lines and the like, can be recorded indiscriminately, so that the point cloud data volume is greatly increased, a large amount of redundant point cloud information is caused, and higher requirements are put forward on computer hardware and corresponding software. That is, if the geometric features of the track are directly extracted from a complex scene composed of a large amount of discrete point clouds, not only a large amount of computer storage space and calculation time are consumed, but also the accuracy of the final result is difficult to ensure. Therefore, before extracting the track central line, the point cloud data needs to be denoised and simplified, that is, the point cloud data of the track part is separated from the massive point cloud data by means of a point cloud filtering algorithm, and on the basis, the central line extraction and the subsequent processing work are performed, so that the production operation efficiency can be improved in time and space.
In the prior art, a slope-based filtering algorithm, a mathematical morphology-based filtering algorithm, a TIN-based filtering algorithm and a cloth simulation algorithm are generally adopted for processing point cloud data.
The terrain information can be well reserved by the gradient-based filtering algorithm, but the gradient threshold value and the size of the filtering window must be known in advance, different threshold value parameters need to be set for different terrains such as flat terrains, hilly areas and mountain areas, and the adaptability of the scheme is low.
The filtering algorithm based on mathematical morphology needs to organize original point cloud data by a regular grid, extract ground points in a point-by-point or grid-by-grid manner, interpolate discrete point cloud data, generate the regular grid, cause important details of the terrain to be lost, and influence the extraction accuracy of the track point cloud.
The TIN-based filtering algorithm can keep the accuracy of original data, but the method needs to continuously construct a triangular network, calculate the distance and the angle between a to-be-determined point and a triangular surface, and has large calculation amount and low speed.
The material distribution algorithm has high efficiency, but when aiming at different terrains, the selection and the setting of the parameters have uncertainty, and the use is inconvenient.
After the track point cloud is determined, extraction and modeling of rails, sleepers, turnouts and the like can be performed, extraction of track center lines can also be performed, the track point cloud extraction method is used for transformation of existing lines, large-scale rail adjustment and the like, the extraction of the track point cloud can help to weaken complexity of a point cloud environment to the maximum extent, and the point cloud data volume and subsequent processing time are reduced.
Therefore, how to extract the track point cloud rapidly and accurately under different terrains and improve the use experience of the user is a technical problem to be solved by technical personnel in the field.
Disclosure of Invention
The invention aims to solve the problem that the prior art can not quickly and accurately extract track point cloud under the condition of complex terrain, and provides a train track point cloud extraction method.
The technical scheme of the invention is as follows: a train track point cloud extraction method comprises the following steps:
s1, POS data and original point cloud data are obtained through a vehicle-mounted laser radar system, wherein the POS data are recording information of continuous position changes of the train;
s2, determining the end points and the extending direction of the POS data, wherein the end points comprise a first end point and a second end point;
s3, extracting the orbit point cloud through the end point, the extending direction and the original point cloud data.
Further, the step S2 specifically includes the following sub-steps:
s21, determining the distance between every two points of the POS data, and taking the two points with the longest distance as the first endpoint and the second endpoint of the POS data, wherein the second endpoint is the endpoint far away from the train, and the first endpoint is the endpoint close to the train;
s22, establishing a two-dimensional coordinate system through the POS data, wherein the first end point and the second end point are located in the same two-dimensional coordinate system;
and S23, determining the extending direction according to the coordinates of the first endpoint and the second endpoint.
Further, the step S23 specifically includes the following sub-steps:
s231, determining an absolute value of a horizontal coordinate difference value between the horizontal coordinate of the first end point and the horizontal coordinate of the second end point, and determining an absolute value of a vertical coordinate difference value between the vertical coordinate of the first end point and the vertical coordinate of the second end point;
s232, judging whether the absolute value of the horizontal coordinate difference is larger than the absolute value of the vertical coordinate difference, if so, determining that the extending direction is along the horizontal coordinate direction, and if not, determining that the extending direction is along the vertical coordinate direction.
Further, between the step S1 and the step S2, the following steps are further included:
and S1.5, performing down-sampling on the POS data according to a preset time interval.
Further, the step S3 specifically includes the following sub-steps:
s31, taking each point in the original point cloud data as an undetermined point;
s32, taking the point on the POS data, which is the shortest distance from the undetermined point, as a first near point, and taking the point on the POS data, which is the second shortest distance from the undetermined point, as a second near point;
s33, determining a middle point between the first near point and the second near point;
s34, judging whether the height difference and the slope distance between the undetermined point and the corresponding midpoint meet preset conditions, if so, taking the point as one point in the point cloud of the track, and if not, deleting the point;
s35, all undetermined points belonging to the orbit point cloud are obtained, the undetermined points are arranged according to the extending direction, the first end point is used as a starting point, the second end point is used as an end point, and therefore the orbit point cloud is determined.
Compared with the prior art, the invention has the following beneficial effects:
(1) according to the method, POS data are obtained through a vehicle-mounted laser radar system, end points and extension directions of the POS data are determined, the end points comprise a first end point and a second end point, and track point cloud is extracted through the end points, the extension directions and original point cloud data, wherein two points with the longest distance in the POS data are used as the end points, the second end point is an end point far away from a train, the first end point is an end point close to the train, then a two-dimensional coordinate system is established, and the first end point and the second end point are both located in the two-dimensional coordinate system, so that the track point cloud can be extracted quickly and accurately under the condition of complex terrain.
(2) According to the invention, POS data are down-sampled according to the preset time interval, so that the processing speed is increased while the extraction precision of the track point cloud is ensured.
Drawings
Fig. 1 is a schematic flow chart of a train track point cloud extraction method according to an embodiment of the present invention;
fig. 2 is a diagram illustrating the effect of the POS data presentation in the actual application scenario.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As described in the background art, in the prior art, when train track point cloud extraction is performed, the requirement of rapidly, efficiently and accurately extracting the track point cloud under the condition of a complex terrain cannot be met.
Therefore, the present application provides a train track point cloud extraction method, and as shown in fig. 1, the method is a schematic flow chart of the train track point cloud extraction method provided in the embodiment of the present application, and includes the following steps:
and step S1, POS data and original point cloud data are obtained through a vehicle-mounted laser radar system, wherein the POS data are recorded information of continuous position changes of the train.
The method comprises the steps of acquiring data of a Global Positioning System (GPS) and an Inertial Measurement Unit (IMU) through a vehicle-mounted laser radar System, wherein the IMU comprises three single-axis accelerometers and a gyroscope, acquiring three axial accelerations and relative course angular velocities in a motion process, resolving the postures of targets according to the accelerations and the relative course angular velocities, resolving the GPS data and the IMU data to obtain discrete POS data, and displaying the POS data as shown in the following table 1, wherein an effect diagram of the POS data in an actual application scene is shown in the following table 2.
TABLE 1
The POS data is formed by a discrete POS point, the points are data containing time and space position information, the POS data presents discrete distributed space points and can directly reflect continuous position change information of a scanning center, and for the POS data scanned on a train, the scanning center is basically positioned at the center of a track, so that the high-density POS data can completely describe the track trend.
And step S2, determining the end points and the extending direction of the POS data, wherein the end points comprise a first end point and a second end point.
In this embodiment, the step S2 specifically includes the following sub-steps:
s21, determining the distance between every two points of the POS data, and taking the two points with the longest distance as the first endpoint and the second endpoint of the POS data, wherein the second endpoint is the endpoint far away from the train, and the first endpoint is the endpoint close to the train;
s22, establishing a two-dimensional coordinate system through the POS data, wherein the first end point and the second end point are located in the same two-dimensional coordinate system;
and S23, determining the extending direction according to the coordinates of the first endpoint and the second endpoint.
Specifically, the actual layout of the railway track is linearly extended, and the roundabout characteristic is not generated in a certain distance, and when the vehicle-mounted laser radar acquires the POS data and the original point cloud data, corresponding coordinates are provided for each point, and all the points are located in the same coordinate system, so that two points with the farthest distance in the POS data can be regarded as end points, and the extending direction of the POS data, namely the extending direction of the point cloud of the railway track, can be determined.
In this embodiment, the step S23 specifically includes the following sub-steps:
s231, determining an absolute value of a horizontal coordinate difference value between the horizontal coordinate of the first end point and the horizontal coordinate of the second end point, and determining an absolute value of a vertical coordinate difference value between the vertical coordinate of the first end point and the vertical coordinate of the second end point;
s232, judging whether the absolute value of the horizontal coordinate difference is larger than the absolute value of the vertical coordinate difference, if so, determining that the extending direction is along the horizontal coordinate direction, and if not, determining that the extending direction is along the vertical coordinate direction.
In the embodiment of the present application, between the step S1 and the step S2, the following steps are further included:
and S1.5, performing down-sampling on the POS data according to a preset time interval.
Step S3, extracting the orbit point cloud through the end point, the extending direction and the original point cloud data.
In this embodiment, the step S3 specifically includes the following sub-steps:
s31, taking each point in the original point cloud data as an undetermined point;
s32, taking the point on the POS data, which is the shortest distance from the undetermined point, as a first near point, and taking the point on the POS data, which is the second shortest distance from the undetermined point, as a second near point;
s33, determining a middle point between the first near point and the second near point;
s34, judging whether the height difference and the slope distance between the undetermined point and the corresponding midpoint meet preset conditions, if so, taking the point as one point in the point cloud of the track, and if not, deleting the point;
s35, all undetermined points belonging to the orbit point cloud are obtained, the undetermined points are arranged according to the extending direction, the first end point is used as a starting point, the second end point is used as an end point, and therefore the orbit point cloud is determined.
Specifically, the undetermined point may be
The first near point may be
The second near point may be
The midpoint may be
The coordinate system is a coordinate system automatically generated by the vehicle-mounted lidar system at the time of acquisition.
Specifically, the preset condition is that the absolute value of the height difference is not greater than a first threshold, the slope distance is not greater than a second threshold, and the first threshold is
The second threshold value is
The preset condition is determined by the following formula:
in the formula (I), the compound is shown in the specification,
for setting up the instrument during data acquisition
，
For erecting the instrument
Half of the distance from the track
The square of the sum of the squares of (c),
the calculation formula is as follows:
wherein d is the track pitch.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (5)
1. A train track point cloud extraction method is characterized by comprising the following steps:
s1, POS data and original point cloud data are obtained through a vehicle-mounted laser radar system, wherein the POS data are recording information of continuous position changes of the train;
s2, determining the end points and the extending direction of the POS data, wherein the end points comprise a first end point and a second end point;
s3, extracting the orbit point cloud through the end point, the extending direction and the original point cloud data.
2. The train track point cloud extraction method of claim 1, wherein the step S2 specifically comprises the following substeps:
s21, determining the distance between every two points of the POS data, and taking the two points with the longest distance as the first endpoint and the second endpoint of the POS data, wherein the second endpoint is the endpoint far away from the train, and the first endpoint is the endpoint close to the train;
s22, establishing a two-dimensional coordinate system through the POS data, wherein the first end point and the second end point are located in the same two-dimensional coordinate system;
and S23, determining the extending direction according to the coordinates of the first endpoint and the second endpoint.
3. The train track point cloud extraction method of claim 2, wherein the step S23 specifically comprises the following substeps:
s231, determining an absolute value of a horizontal coordinate difference value between the horizontal coordinate of the first end point and the horizontal coordinate of the second end point, and determining an absolute value of a vertical coordinate difference value between the vertical coordinate of the first end point and the vertical coordinate of the second end point;
s232, judging whether the absolute value of the horizontal coordinate difference is larger than the absolute value of the vertical coordinate difference, if so, determining that the extending direction is along the horizontal coordinate direction, and if not, determining that the extending direction is along the vertical coordinate direction.
4. The train track point cloud extraction method of claim 1, wherein between the step S1 and the step S2, further comprising the steps of:
and S1.5, performing down-sampling on the POS data according to a preset time interval.
5. The train track point cloud extraction method of claim 1, wherein the step S3 specifically comprises the following substeps:
s31, taking each point in the original point cloud data as an undetermined point;
s32, taking the point on the POS data, which is the shortest distance from the undetermined point, as a first near point, and taking the point on the POS data, which is the second shortest distance from the undetermined point, as a second near point;
s33, determining a middle point between the first near point and the second near point;
s34, judging whether the height difference and the slope distance between the undetermined point and the corresponding midpoint meet preset conditions, if so, taking the point as one point in the point cloud of the track, and if not, deleting the point;
s35, all undetermined points belonging to the orbit point cloud are obtained, the undetermined points are arranged according to the extending direction, the first end point is used as a starting point, the second end point is used as an end point, and therefore the orbit point cloud is determined.
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