CN116858195B - Existing railway measurement method based on unmanned aerial vehicle laser radar technology - Google Patents
Existing railway measurement method based on unmanned aerial vehicle laser radar technology Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C15/00—Surveying instruments or accessories not provided for in groups G01C1/00 - G01C13/00
- G01C15/002—Active optical surveying means
- G01C15/004—Reference lines, planes or sectors
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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
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
Abstract
The application discloses an existing railway measurement method based on unmanned aerial vehicle laser radar technology, which comprises the following steps: alternately arranging a plurality of target control point devices on two sides of a track of the railway line along the extending direction of the railway line, and obtaining actual measurement coordinates of the plurality of target control point devices; setting a flight path of the unmanned aerial vehicle according to the designed area range of the railway strip-shaped area, and setting flight parameters of the unmanned aerial vehicle according to the laser point cloud data density requirement; the unmanned aerial vehicle acquires the original data of the railway track according to a preset flight path and flight parameters, and corrects the original data according to the actual measurement coordinates of the target control point device to obtain laser point cloud data; and extracting the center line of the railway track based on the corrected laser point cloud data, calculating the coordinates of the track turnout center, and carrying out section extraction labeling and railway investigation measurement. The invention gets rid of the restriction of the upper way of the 'skylight point', greatly improves the existing line measurement efficiency, reduces the workload of manual upper way and obviously improves the level of the existing line measurement.
Description
Technical Field
The application relates to the technical field of laser radar scanning measurement, in particular to an existing railway measurement method based on unmanned plane laser radar technology.
Background
The railway line measurement work comprises unmanned aerial vehicle aerial photography measurement, control measurement, mileage measurement, line center line measurement, line cross section measurement, tunnel measurement and other work contents.
In the railway measurement process, certain system errors exist in point clouds among different routes of the unmanned plane laser radar, and the accuracy requirement for measuring the railway line at the present stage cannot be met. In addition, unmanned aerial vehicle laser radar point cloud coordinates of different orders are determined by base station coordinates, and the problem of inconsistent benchmarks also exists.
In the process of extracting the track center line and the track turnout center, the track is usually directly measured by a steel rule or the main line and the side line of the railway are measured by a total station, and the track center line is calculated and then intersected to obtain the railway. The traditional fork measurement needs manual on-road operation, has low automation degree, and has the problems of high labor cost, low operation efficiency, high safety risk and the like.
Disclosure of Invention
Aiming at least one defect or improvement requirement of the prior art, the invention provides the existing railway measurement method based on the unmanned plane laser radar technology, which gets rid of the restriction of the 'skylight point' to go up, greatly improves the existing line measurement efficiency, reduces the workload of manual going up, and remarkably improves the level of the existing line measurement. To achieve the above object, according to a first aspect of the present invention, there is provided an existing railway measurement method based on unmanned aerial vehicle lidar technology, the method comprising:
alternately arranging a plurality of target control point devices on two sides of a track of the railway line along the extending direction of the railway line, and acquiring actual measurement coordinates of the plurality of target control point devices;
acquiring railway design data, setting a flight path of the unmanned aerial vehicle according to the designed area range of the railway banded region, and setting flight parameters of the unmanned aerial vehicle according to the laser point cloud data density requirement;
acquiring original data of a railway track by the unmanned aerial vehicle laser radar according to a preset flight path and flight parameters, and correcting the original data according to actual measurement coordinates of the target control point device to obtain laser point cloud data;
and extracting the railway track center line based on the corrected laser point cloud data and calculating the track fork center coordinates.
Further, in the existing railway measurement method based on the unmanned aerial vehicle laser radar technology, the target control point device comprises a fixed measurement part and a target plate;
the fixed measuring part is fixed at a preset position on the ground and comprises a protruding cross silk hemisphere which is used for being positioned to acquire the three-dimensional coordinate of the center point of the cross silk;
the target plate is of a square plate-shaped structure, and a small hole is formed in the center of the plate surface of the target plate and can penetrate through the cross silk hemispheroids; dividing the whole surface of the target plate into four square areas with equal areas or four triangular areas with equal areas by taking the small holes as the centers, wherein the areas of adjacent sides are different in color, and the areas of opposite angles are same in color;
and the center point of the cross wire coincides with the center point of the plate surface of the target plate during measurement, and the center point of the cross wire is the target control point.
Further, the existing railway measurement method based on the unmanned aerial vehicle laser radar technology, wherein the flight parameters of the unmanned aerial vehicle are set according to the laser point cloud data precision requirement, specifically comprises the following steps:
the known parameters are: unmanned aerial vehicle laser radar equipment point frequency is p t The angle of view of the laser is theta t The angle of view of the camera is theta FOV Laser scanning rotation speed v scan Setting the unmanned aerial vehicle flight speed with unknown parameters as v flight Unmanned aerial vehicle flight altitude is h and unmanned aerial vehicle route interval S flight The following set of equations is constructed:
laser point cloud average point density ρ point Can be expressed as:
laser scanning rotation speed v scan Can be expressed as:
calculate unmanned aerial vehicle route interval S flight The method comprises the following steps:
wherein, the point cloud side overlap degree P PointOverlapt The method comprises the following steps:
wherein P is ImageOverlapt Is the degree of route overlap;
solving unmanned aerial vehicle flight speed v according to the equation set flight Unmanned aerial vehicle flight altitude h and unmanned aerial vehicle route interval S flight 。
Further, the existing railway measurement method based on the unmanned aerial vehicle laser radar technology further comprises the steps of constructing an existing line protection range constraint model and a terrain and ground object threat constraint model, and performing safety inspection on the flight path through the existing line protection range constraint model and the terrain and ground object threat constraint model; wherein,
the existing line protection range constraint model needs to satisfy the following formula:
wherein,
wherein S is i For all waypoints (x) i ,y i ) Offset distance, r n Is of the existingRadius of line protection range, L safe Is the safety distance omega between the unmanned plane and the protection range i Is threat weight;
further, in the existing railway measurement method based on the unmanned aerial vehicle laser radar technology, the terrain and ground object threat constraint model needs to satisfy the following formula:
wherein,
wherein, (x) m ,y m ,z m ) Represents the m-th ground object endpoint coordinate which can cause potential safety hazard, (x) j ,y j ,z j ) Representing three-dimensional space coordinates of closest point to the end point of the ground object in the route, r m Representing threat distance of terrain and ground object point, L safe Represents the safety distance omega between the unmanned plane and the terrain and between the unmanned plane and the ground feature point j Representing threat weights.
Further, in the existing railway measurement method based on the unmanned aerial vehicle laser radar technology, the unmanned aerial vehicle laser radar acquires original data of a railway track according to a preset flight path and flight parameters, and corrects the original data according to actual measurement coordinates of the target control point device to obtain laser point cloud data, and the method specifically comprises the following steps:
acquiring original data of a railway track by using an unmanned aerial vehicle laser radar according to a preset flight path and flight parameters;
preprocessing the original data, including track calculation and point cloud calculation, so as to obtain a first laser point cloud;
extracting point cloud coordinates and moments of target control points of each target control point device in the first laser point cloud, and obtaining coordinate errors of corresponding moments by differentiating the point cloud coordinates and the corresponding measured coordinates;
based on the coordinate errors and moments of all target control points, establishing a self-adaptive point cloud error time-varying model taking a navigation belt as a unit;
and correcting the first laser point cloud through the self-adaptive point cloud error time-varying model taking the navigation belt as a unit to obtain corrected laser point cloud data.
Further, the method for constructing the adaptive point cloud error time-varying model by taking the navigation belt as a unit comprises the following steps of:
taking a navigation belt as a unit, taking time as an independent variable, and establishing an error equation of each target control point and time:
all target control points are combined to construct a normal equation:
the least square method is solved to obtain:
X＝(B T B) -1 B T L
repeating the steps to solve three coordinate axis directions of the three-dimensional coordinates so as to obtain a self-adaptive point cloud error time-varying model taking the navigation belt as a unit in the three coordinate axis directions;
wherein alpha is i Error correction coefficient, t, representing corresponding target control point n Representing time when unmanned aerial vehicle flies through corresponding target control point, S k And representing the error between the extracted point cloud coordinates and the corresponding measured coordinates.
Further, the existing railway measurement method based on the unmanned aerial vehicle laser radar technology, wherein the method for extracting the railway track center line and calculating the track fork coordinates based on the corrected laser point cloud data specifically comprises the following steps:
and fitting the laser point cloud coordinates into four fit straight line expressions corresponding to the four steel rails by a least square method, and respectively calculating the track center line and the fork coordinates according to the fit straight line expressions.
Further, the existing railway measurement method based on the unmanned aerial vehicle laser radar technology further comprises the following steps of:
classifying the corrected laser point cloud data to separate out ground points;
constructing a terrain model, and cutting a section in the terrain model according to the section position required by design;
and marking the section according to the corrected laser point cloud data and the railway image data.
Further, the existing railway measurement method based on the unmanned aerial vehicle laser radar technology further comprises the following steps of:
and generating a railway vector diagram according to the corrected laser point cloud data and the railway image for subsequent railway investigation and measurement.
In general, the above technical solutions conceived by the present invention, compared with the prior art, enable the following beneficial effects to be obtained:
(1) According to the existing railway measurement method based on the unmanned plane laser radar technology, the target control point device is arranged, point cloud data of an iron track are collected, and the center line branch center coordinates of the track are extracted according to the point cloud data.
(2) According to the existing railway measurement method based on the unmanned aerial vehicle laser radar technology, the existing line protection range constraint model and the terrain and ground object threat constraint model are constructed, and aiming at the regional characteristics of the area in the strip shape in the railway laser radar data acquisition operation process, the safety inspection is carried out on the flight path through the existing line protection range constraint model and the terrain and ground object threat constraint model, so that the safety risk of the unmanned aerial vehicle line is evaluated quantitatively, and the safety execution of the aviation work can be ensured.
(3) According to the existing railway measurement method based on the unmanned aerial vehicle laser radar technology, point cloud coordinates and time of each target control point in first laser point clouds obtained by preprocessing unmanned aerial vehicle flight original data are extracted, the point cloud coordinates and actual measurement coordinates of the corresponding target control points are subjected to difference, and coordinate errors of the corresponding time are obtained; based on the coordinate errors and moments of all target control points, an adaptive point cloud error time-varying model taking a navigation belt as a unit is established, and the first laser point cloud is corrected through the adaptive point cloud error time-varying model, so that the accuracy of unmanned aerial vehicle laser radar measurement of a railway line is improved, and the plane and elevation accuracy of the unmanned aerial vehicle laser radar can be improved to 1cm.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an existing railway measurement method based on unmanned plane laser radar technology according to an embodiment of the present application;
fig. 2 is a schematic plan view of a fixed measurement part of a target control point device according to an embodiment of the present invention;
FIG. 3 is a schematic plan view of a target plate with small holes for a target control point device according to an embodiment of the present invention;
fig. 4 is a schematic diagram of unmanned aerial vehicle parameter labeling provided in an embodiment of the present application;
FIG. 5 is a diagram of the distribution of target control point devices on both sides of a railroad track provided by an embodiment of the present invention;
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
The terms first, second, third and the like in the description and in the claims of the application and in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
The application provides an existing railway measurement method based on unmanned aerial vehicle laser radar technology, referring to fig. 1, the method comprises the following steps:
(1) Alternately arranging a plurality of target control point devices on two sides of a track of the existing railway line along the extending direction of the existing railway line, and acquiring actual measurement coordinates of the plurality of target control point devices;
because the acquisition precision of the laser radar of the conventional unmanned aerial vehicle can not meet the requirements of the existing railway line on measuring plane and elevation precision, a target control point device is required to be arranged outside the railway line, and the measurement precision is improved through the arrangement of the target control point device. After the target control point device is arranged, the actual measurement coordinates of the target control point can be measured.
After the target plate in the prior art is laid on the ground, the target plate cannot move, and if the target plate is damaged or moved manually, the center coordinates of the old target plate cannot be used any more, so that the reusability of the target point is poor. In view of this, the invention designs a combined target control point device, thereby solving the technical problem that the target center point is not suitable for long-term preservation.
Referring to fig. 2 and 3, the target control point device mainly includes the fixed measuring part of fig. 2 and the target plate of fig. 3.
The fixed measuring part adopts a special measuring sign with a cover, the special measuring sign can be fixed on the ground, the diameter of a disc at the top of the fixed measuring part is 6cm, the disc has the functions of device identification and/or device warning, the diameter of a cross silk hemispherical shell (the cross silk of fig. 2 is a top view of the cross silk) at the center of the disc is 2cm, and the special measuring sign can be used for accurately acquiring the plane and the elevation of the top cross silk.
The target plate adopts square, the thickness is not more than 1cm, the surface is checkerboard-shaped square lattices with two alternate colors, and in order to increase the contrast, the preferable square lattices with checkerboard-shaped black-white alternate colors are made of PVC; the center of the target plate is provided with a circular hole, the diameter of the hole is 2cm, the diameter of the hole is consistent with the diameter of the cross silk hemispherical shell at the top of the fixed measuring part, and the circular hole which is provided with the center of the target plate can penetrate through the cross silk hemispherical shell.
The application improves the traditional target plate, combines with the common fixed measurement mark (fixed measurement part), fully plays the advantages of the ground fixed measurement mark that can be stored for a long time and is convenient for measurement, and improves the preservability of the whole device. By utilizing the combined device, even if the target plate is damaged, when the central coordinate of the old target plate cannot be used, the new target plate is only needed to be reloaded into a fixed measurement mark stored on the ground, and the work of measuring the central coordinate of the target is not needed to be repeatedly carried out, so that the reusability of the target control point device and the measurement precision of the center of the target plate are improved.
The layout requirements of the target control point device are specifically as follows:
(1) the target control points (cross wire center points of cross wire hemispherical shells) of the target control point device are alternately distributed along the railway line, and are distributed on two sides of the railway in a crossing manner, wherein the distance from the railway line track is not more than 50m, and the point distance between any two adjacent orthographic projection points of the target control points on the railway line track is not more than 200m.
(2) The cross silk hemispherical shell of the fixed measuring part is required to be laid on a cement ground with a relatively flat ground surface, a common level mark is buried, and a target plate with a hole in the center of 50cm x 50cm can be accommodated and sleeved into the cross silk hemispherical shell.
During measurement, the on-line plane control network is used for joint measurement, the same type of Leica or Tianbao GNSS receiver is required to be selected for field measurement, a period of time is synchronously observed, the period of time is not less than 1h, the sampling interval is 15s, the accurate measuring instrument is high, and a photo is taken.
(2) Acquiring railway design data, setting a flight path of the unmanned aerial vehicle according to the designed area range of the railway banded region, and setting flight parameters of the unmanned aerial vehicle according to the laser point cloud data density requirement;
specifically, after receiving the unmanned aerial vehicle line laser Lei Dahang flight mission, immediately acquiring intersection point data of a railway design ledger or design data of other railways, generating a railway center line and a buffer zone in the aviation scope according to a relaxation curve, intersection point coordinates and other line parameters in the area scope of a railway banded region recorded by the data, and dividing the unmanned aerial vehicle aviation scope.
Preferably, the buffer zone has a buffer width greater than the sum of the regional range distance of the railway strip zone and the safe flight distance of the unmanned aerial vehicle.
In order to ensure the flight safety of the unmanned aerial vehicle laser radar and the quality of data acquisition, before the aviation work is executed, the flight parameters of the unmanned aerial vehicle are required to be set according to the precision requirement of the line laser point cloud data. The point cloud data collected by aviation should meet the following conditions:
the route is respectively distributed along the left and right sides of the railway, and the plane position of the route projected on the ground is positioned outside the railway safety protection area.
In a specific embodiment, the course overlap is not less than 50%, the double course coverage ground width is not less than 360 meters, and the obtained ground point cloud point density is not less than 1500 points per square meter.
Further, according to the relation among radar equipment point frequency, laser field angle, unmanned aerial vehicle flight speed, unmanned aerial vehicle navigational altitude, laser point cloud average point density, point cloud side overlapping degree, unmanned aerial vehicle route distance and laser scanning rotating speed, an equation set is constructed, and flight parameters of the unmanned aerial vehicle are solved.
Fig. 4 is a schematic diagram of unmanned aerial vehicle parameter labeling provided in the embodiment of the present application, specifically, known parameters are: unmanned aerial vehicle laser radar equipment point frequency is p t The angle of view of the laser is theta t The angle of view of the camera is theta FOV Laser scanning rotation speed v scan Setting the unmanned aerial vehicle flight speed with unknown parameters as v flight Unmanned aerial vehicle flight altitude is h and unmanned aerial vehicle route interval S flight The following set of equations is constructed:
laser point cloud average point density ρ point Can be expressed as:
laser scanning rotation speed v scan Can be expressed as:
calculate unmanned aerial vehicle route interval S flight The method comprises the following steps:
wherein, the point cloud side overlap degree P PointOverlapt The method comprises the following steps:
wherein P is ImageOverlapt For course overlap, in a particular embodiment, course overlap is set to 50%;
solving unmanned aerial vehicle flight speed v according to the equation set flight Unmanned aerial vehicle flight altitude h and unmanned aerial vehicle route interval S flight 。
In a specific embodiment, a warrior AA1400 unmanned aerial vehicle laser radar system is taken as an example, the field angle of laser and the field angle of a camera are fixed values, the point frequency of unmanned aerial vehicle laser radar equipment is set to be 1800kHz, the laser scanning rotating speed is 400 lines/S, known parameters are substituted into the equation set, and the unmanned aerial vehicle flight speed is 6m/S, the unmanned aerial vehicle flight altitude is 120m and the unmanned aerial vehicle line distance is 100m. Of course, other types of unmanned aerial vehicle lidar systems may be employed, and the present application is not particularly limited.
Further, a starting point of the unmanned aerial vehicle is set:
after the unmanned aerial vehicle flight parameters are obtained according to the point cloud precision indexes, the flight path of the unmanned aerial vehicle laser radar on the railway line is calculated, an aviation flight starting point is required to be set at first, and the aviation flight safety and the data acquisition range are considered in the setting of the starting point. The projection position of the starting point position of the first sub-unmanned aerial vehicle on the ground is outside the safety protection area of the railway line, and the point can collect laser radar data from the starting point of the area. The subsequent flight route of each frame and the flight route of the unmanned aerial vehicle of the previous frame need to be overlapped by a first overlapping distance.
In a specific embodiment, the first overlap distance may be set to 50m.
Setting a flight route of the unmanned aerial vehicle:
and (5) translating the railway central line to one half of the route distance of each unmanned aerial vehicle on both sides to obtain route reference lines. Because most railway lines are mild curves, the arrangement of the lines does not allow a coordinated turning mode to be used directly, a plurality of sections of linear lines are generated according to the line reference lines, and the plurality of sections of linear lines need to cover the whole band-shaped area range so as to ensure the data precision during the acquisition of a laser radar method.
Setting an unmanned aerial vehicle terminal point:
after finishing single aviation work, an aviation end point needs to be set. The terminal point is set to be similar to the starting point, the projection position of the terminal point on the ground is required to be outside the safety protection area of the railway line, and the terminal point can collect laser radar data from the starting point of the area. The last unmanned aerial vehicle terminal point should be capable of collecting laser radar data of the terminal position of the area.
Further, the unmanned aerial vehicle's course is set to an "8" word course.
In a specific embodiment, when the unmanned aerial vehicle performs long-line aviation operation, an 8-shaped route is added to the plane flight of about 1-1.5 km, and then the unmanned aerial vehicle continues to enter the route to perform operation tasks. In each flight mission, the distance between the unmanned aerial vehicle and the base station is preferably less than 5km, the base station is usually positioned at the center of a measurement area, and the length of a single flight line is preferably less than 10km.
Before entering the starting point, an 8-shaped route is needed to be added, and the unmanned aerial vehicle flies for a certain distance (not less than 500 m) after finishing the starting of the 8-shaped route and then enters the main route.
(3) Acquiring original data of a railway track by the unmanned aerial vehicle laser radar according to a preset flight path and flight parameters, and correcting the original data according to actual measurement coordinates of the target control point device to obtain laser point cloud data;
to ensure the resolution accuracy of the POS resolution, the key control parameters in the POS resolution process are referred to in table 1 below.
TABLE 1POS solution Key control parameter Table
The point cloud data calculation, the original data precision checking, the data precision optimization and the result data precision checking are all carried out in the point cloud preprocessing software. When the data is resolved, long straight navigation lines are selected for resolving.
After the POS is resolved, a first-pass laser point cloud is produced, target control points are identified in the first-pass laser point cloud, the POS is corrected by giving actual measurement coordinates of the identified target control points, a second-pass laser point cloud is produced again after the POS is corrected, and then the precision of the corrected laser point cloud is evaluated.
Constructing an adaptive point cloud error time-varying model taking a navigation belt as a unit: referring to fig. 5, the unmanned aerial vehicle navigates each routeFlying from t 0 Start at time t p The moment ends. Passing n (n is more than or equal to 2) target points in the process, comparing the accurate measured coordinates of the target control points with the point cloud coordinates of the target control points read from the first laser point cloud, and obtaining the time t when the unmanned aerial vehicle flies through each target control point k And coordinate errors in three directions of X/Y/Z (in S k Unified representation) size. In order to acquire error information of other moments (moments not passing through the target control points), taking a navigation belt as a unit, taking time as an independent variable, establishing a polynomial model of the target control points and the time, constructing an observation equation, and carrying out least square solution to acquire accurate error information so as to correct point clouds.
The corresponding relation between the fitting time of the higher order polynomial and the coordinate error is utilized, and the control points of each target are:
all target control points are combined, and a normal equation can be constructed:
the least square method is solved to obtain:
X＝(B T B) -1 B T L
and repeating the steps to solve the X/Y/Z directions respectively, so as to obtain the point cloud error time-varying model in the X/Y/Z directions.
Correcting the point cloud based on the adaptive point cloud error time-varying model: the first laser point cloud is corrected through the constructed self-adaptive point cloud error time-varying model taking the navigation belt as a unit, so that a corrected second laser point cloud is obtained, and the accuracy of laser radar measurement of the unmanned aerial vehicle on the existing railway line is improved.
(4) And extracting the railway track center line based on the corrected laser point cloud data and calculating the track fork center coordinates.
As an alternative embodiment, the extraction of the railway track center line may be performed using the following method:
dividing a first rectangular space containing two tracks at the starting point of a railway track, extracting point cloud data of the first rectangular space to respectively obtain a plurality of point cloud coordinates on the two tracks, fitting the plurality of point cloud coordinates into linear expressions corresponding to the two tracks by a least square method, calculating to obtain a plurality of sections of railway surface central lines, and carrying out railway central line fitting inspection;
specifically, an initial point of a track is selected firstly, wherein the initial point is a central point of a rail surface of a railway track initial section, namely a point positioned at the initial positions of two tracks, and the distance between the points and the two tracks is the same;
secondly, determining an initial direction as a first direction, wherein the first direction is the extending direction of the railway track and can be the track advancing direction;
the specified segment distance Dis, dis is the side length of the rectangular space in the first direction. Although the railway track has curvature, when the railway track is divided into several rectangular spaces, the track can be treated as a straight line within a short distance.
Wherein the side length of the rectangular space in the second direction is set to Wid, which is greater than or equal to the width of the railway track, wherein the second direction is perpendicular to the first direction. As an example, wid is typically 70mm, which should be specifically set according to the actual engineering rail face width.
Selecting point cloud data in a first rectangular space, as a specific embodiment, for the first rectangular space in a plurality of rectangular spaces, finding out the lowest point of the point cloud data in the first rectangular space, and calculating the vertical distance of each point cloud data relative to the lowest point to obtain the relative elevation of each point cloud coordinate;
and selecting the point cloud data with the relative elevation larger than the preset relative elevation threshold value TH and the point cloud data positioned in the first direction of the railway track as the rail surface point cloud. And performing point cloud filtering according to the elevation threshold TH and the first direction. As an example, the rail height is typically 176mm. Considering the errors, TH may be set to 170mm in general, and specific parameters may be set according to actual engineering rail elevations.
And extracting the rail surface point cloud coordinates, fitting the point cloud coordinates into linear expressions corresponding to the two rails through a least square method, and calculating to obtain the rail surface center line of the first rectangular space.
Further, a rail centerline fitting test is performed: generating a second rectangular space with the side length Dis in the first direction and the side length Wid in the second direction according to the central line of the rail surface; and if the coincidence rate of the first rectangular space and the second rectangular space is lower than 95%, adjusting the first rectangular space to be a region covered by the second rectangular space, and extracting the point cloud data again until the coincidence rate is higher than 95%.
Generating a next rectangular space along the railway track in a self-adaptive way, extracting the central line of the rail surface of the section of railway, and repeating the steps until the railway track is completely divided into the rectangular space;
further, the initial point of the next rectangular space coincides with the end point of the previous rectangular space. The end point of the previous rectangular space is the point of the side length of the previous rectangular space in the second direction, and the point corresponds to the initial point of the previous rectangular space and falls on the central line of the rail surface.
The rectangular space is divided by adopting an iteration method, the direction of the rectangular space can be adaptively corrected according to the direction and the curvature of the railway track, the acquired point cloud data is comprehensive, and coordinate points on the railway track can not be omitted.
Further, the extraction of the rail center line is performed for the next rectangular space, and the extraction steps are the same as those of the first rectangular space, and are not described herein.
And splicing the generated rail surface central lines of the sections to obtain a final rail central line.
Since the end point of each section centerline is the start point of the next section centerline, connecting all points is the preliminary rail centerline. The direct connection of the segmented extraction results in an uneven center line, so that the extraction results can be smoothed by a 5-order uneven rational B-spline curve to obtain a final smoothed center line extraction result.
Further, the rail center line is measured based on the track gauge, the distances between points on the rail center line and two rails are obtained, the track gauge is compared to obtain an error value, and if the error value is smaller than a preset fault tolerance threshold value, the rail center line is successfully extracted.
As a specific embodiment, the distance between two rails of the track is usually 1.435m, for each node on the central line of the rail, the perpendicular line is intersected with the other rail according to the direction of the straight line where the node is located, the distance between the node and the intersection point is set to be D, D represents the distance between the two rails at the node, if |D-1.435| > Td, the central line extraction result is wrong, and the correction needs to be manually performed. Where Td is the set fault tolerance threshold.
Further, the rail centerline is detected based on the segment longitudinal section:
and drawing points within 10cm of the central line of the rail by taking the second direction of the rail in each rectangular space as a horizontal axis and the elevation as a vertical axis to obtain a longitudinal section result of the central line of the rail, and analyzing the longitudinal section result of the central line of the rail to locate coordinate points with abnormal elevations. The vertical section can be checked in a man-machine interaction mode, so that points with elevation anomalies can be positioned quickly, and the central line extraction result can be detected.
As another alternative embodiment, the railway centerline and the fork coordinates are extracted by the following method:
further, the coordinate data are divided through a K-means clustering algorithm, and coarse differences are removed through RANSAC preprocessing.
As an example, the point coordinates acquired by the rail L1 areThe coordinates of the L2 point of the rail are->The coordinates of the L3 point of the rail areRail L4 point coordinates are->
Further, fitting the point cloud data into four fit linear expressions corresponding to the four steel rails respectively by a least square method, and calculating initial values of the track turnout coordinates and initial values of the track included angles;
fitting a plurality of coordinate points into four fitting linear expressions corresponding to four steel rails respectively by a least square method, wherein the method specifically comprises the following steps:
the expression of four straight lines corresponding to the four steel rails is set as follows:
y＝k i x+b i ,i＝1,2,3,4
the initial value can be obtained according to a least squares fitting formula:
wherein,
the rail spacing is |b i -b i+1 The included angle between the central lines of the two tracks is arctan k 1 -k 3 There is a sign problem of absolute value values. In order to simplify the calculation, the sign problem can be directly reflected on the signs of the distance and the included angle, so that the sign values of the rail distance rho and the included angle alpha of the track center line in the subsequent calculation can be determined through initial values. If k occurs i If the calculation is impossible, it proves that two parallel rails are perpendicular to the x-axis or infinitely close to the x-axis, and the linear formula can be set as x=a, so that the calculation is simpler, and only the case that the slope can be solved is discussed below.
Further, calculating the central lines of the two parallel straight lines in the first direction and the central lines of the two parallel straight lines in the second direction according to the four fitting straight line expressions, wherein the intersection point of the two central lines is the initial value of the track fork coordinates. The first direction is the direction of one track on any two crossed tracks, and the second direction is the direction of the other track.
Calculating an initial value of the track included angle through the following steps:
and acquiring railway turnout data and determining the turnout type. And according to the turnout coordinates and turnout types, referring to the track standing book, and determining the accurate value rho of the distance between the two tracks. Because the turnout types are many, the initial angle value needs to be calculated first, and then the standing account data is consulted to determine that the accurate value of the included angle between the central lines of the two intersection tracks is alpha.
Further, adding constraint conditions to carry out integral least square, and correcting fitting coefficients to obtain four corrected linear expressions; in particular, the method comprises the steps of,
s1, an observation equation is built, and a plurality of collected coordinate points are all theoretically on a fitting straight line, so that the observation equation is as follows:
in the middle of
Wherein dk i 、db i Slope correction values and intercept correction values of four straight lines respectively.
S2, constructing a constraint equation, and constructing the constraint equation according to the parallel of the two fitting straight lines in the first direction and the distance between the two fitting straight lines in the first direction.
L1 is parallel to L2, and the distance ρ is:
similarly, for L3 and L4:
the included angle of the central lines of the two tracks is alpha, and then:
the expansion using the taylor formula is available:
s3, constructing a normal equation by combining the observation equation and the constraint equation:
wherein, the size of the B array isdX array size is 8×1, L array size +.>
Using least squares solution to calculate:
dX＝(B T B) -1 B T L
performing iterative solution when |dX 0 Stopping resolving when the I is less than or equal to delta; otherwise, repeating the steps S2-S3, wherein:
X 0 ＝X 0 +dX
delta is generally taken to the next position where the switch point position requires accuracy, for example, where the position accuracy is required to be 0.01m, delta=0.001.
After the solution is finished, the final fitting coefficient is:
X＝X 0 +dX
further, corrected track fork coordinates are calculated according to the corrected four straight line expressions.
And respectively calculating central lines of L1, L2, L3 and L4 according to the final fitting coefficients:
according to the formula, the straight line intersection point is obtained, namely the plane coordinate of the turnout point:
as an alternative embodiment, the extraction of the railway track center line can be performed by using the CoProcess software, the center line of the railway track is inspected from three directions (namely, the first direction, the second direction and the direction parallel to the first direction and the second direction) of the longitudinal direction, the transverse direction and the horizontal direction after the track center line is extracted according to the left and right rail track center line data (the warp distance, the weft distance and the elevation distance can be set according to the needs) of each track extracted by the point cloud data, and editing and correcting can be performed for automatically extracting the points with errors.
(6) Extracting a railway section:
classifying the corrected laser point cloud data to separate out ground points;
constructing a terrain model, and cutting a section in the terrain model according to the section position required by design;
and marking the section according to the corrected laser point cloud data and railway image data, wherein the section comprises plots of ballast shoulders, ballast feet, side ditches, platforms, roadbed slope change points, embankment slope feet, cutting tops, annunciators, contact networks (rods), line centers, rail tops, road shoulders and the like, and special feature points such as houses, roads, filling channels, river edges and the like.
(7) Investigation and measurement are carried out on railways:
and generating a railway vector diagram according to the corrected laser point cloud data and the railway image for subsequent railway investigation and measurement. Specifically, by combining high-density laser point cloud data and image data, various ground features can be clearly distinguished, vectors are drawn out in the point cloud and orthographic images according to professional investigation requirements and are exported as CAD format data, and the CAD data are imported into design software.
The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Embodiments of the present disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (7)
1. An existing railway measurement method based on unmanned aerial vehicle laser radar technology is characterized by comprising the following steps:
alternately arranging a plurality of target control point devices on two sides of a track of the railway line along the extending direction of the railway line, and acquiring actual measurement coordinates of the plurality of target control point devices;
acquiring railway design data, setting a flight path of the unmanned aerial vehicle according to the designed area range of the railway banded region, and setting flight parameters of the unmanned aerial vehicle according to the laser point cloud data density requirement;
the unmanned plane laser radar acquires the original data of a railway track according to a preset flight path and flight parameters, corrects the original data according to the actual measurement coordinates of the target control point device to obtain laser point cloud data, and specifically comprises the following steps:
acquiring original data of a railway track by using an unmanned aerial vehicle laser radar according to a preset flight path and flight parameters;
preprocessing the original data, including track calculation and point cloud calculation, so as to obtain a first laser point cloud;
extracting point cloud coordinates and moments of target control points of each target control point device in the first laser point cloud, and obtaining coordinate errors of corresponding moments by differentiating the point cloud coordinates and the corresponding measured coordinates;
based on the coordinate errors and moments of all target control points, establishing a self-adaptive point cloud error time-varying model taking a navigation belt as a unit;
correcting the first laser point cloud through the self-adaptive point cloud error time-varying model taking the navigation belt as a unit to obtain corrected laser point cloud data;
the construction method of the self-adaptive point cloud error time-varying model taking the navigation belt as a unit comprises the following steps:
taking a navigation belt as a unit, taking time as an independent variable, and establishing an error equation of each target control point and time:
all target control points are combined to construct a normal equation:
the least square method is solved to obtain:
X＝(B T B) -1 B T L
repeating the steps to solve three coordinate axis directions of the three-dimensional coordinates so as to obtain a self-adaptive point cloud error time-varying model taking the navigation belt as a unit in the three coordinate axis directions;
wherein alpha is i Error correction coefficient, t, representing corresponding target control point n Representing time when unmanned aerial vehicle flies through corresponding target control point, S k Representing an error between the extracted point cloud coordinates and the corresponding measured coordinates;
extracting a railway track center line and calculating track fork coordinates based on the corrected laser point cloud data, and specifically comprises the following steps:
and fitting the laser point cloud coordinates into four fit straight line expressions corresponding to the four steel rails by a least square method, and respectively calculating the track center line and the fork coordinates according to the fit straight line expressions.
2. The unmanned aerial vehicle laser radar technology-based existing railway measurement method of claim 1, wherein the target control point device comprises a fixed measurement part and a target plate;
the fixed measuring part is fixed at a preset position on the ground and comprises a protruding cross silk hemisphere which is used for being positioned to acquire the three-dimensional coordinate of the center point of the cross silk;
the target plate is of a square plate-shaped structure, and a small hole is formed in the center of the plate surface of the target plate and can penetrate through the cross silk hemispheroids; dividing the whole surface of the target plate into four square areas with equal areas or four triangular areas with equal areas by taking the small holes as the centers, wherein the areas of adjacent sides are different in color, and the areas of opposite angles are same in color;
and the center point of the cross wire coincides with the center point of the plate surface of the target plate during measurement, and the center point of the cross wire is the target control point.
3. The existing railway measurement method based on unmanned aerial vehicle laser radar technology as claimed in claim 1, wherein the flying parameters of the unmanned aerial vehicle are set according to the laser point cloud data precision requirement, and specifically comprises the following steps:
the known parameters are: unmanned aerial vehicle laser radar equipment point frequency is p t The angle of view of the laser is theta t The angle of view of the camera is theta FOV Laser scanning rotation speed v scan Setting the unmanned aerial vehicle flight speed with unknown parameters as v flight Unmanned aerial vehicle flight altitude is h and unmanned aerial vehicle route interval S flight The following set of equations is constructed:
laser point cloud average point density ρ point Can be expressed as:
laser scanning rotation speed v scan Can be expressed as:
calculate unmanned aerial vehicle route interval S flight The method comprises the following steps:
wherein, the point cloud side overlap degree P Point Overlapt The method comprises the following steps:
wherein P is Image Overlapt Is the degree of route overlap;
solving for drone according to the above equation setFlying speed v of aircraft flight Unmanned aerial vehicle flight altitude h and unmanned aerial vehicle route interval S flight 。
4. The existing railway measurement method based on unmanned aerial vehicle laser radar technology as claimed in claim 1, further comprising constructing an existing line protection range constraint model and a terrain and ground object threat constraint model, and performing security inspection on the flight path through the existing line protection range constraint model and the terrain and ground object threat constraint model; wherein,
the existing line protection range constraint model needs to satisfy the following formula:
wherein,
wherein S is i For all waypoints (x) i ，y i ) Offset distance, r n Is the radius of the existing line protection range, L safe Is the safety distance omega between the unmanned plane and the protection range i Is threat weight.
5. The existing railway measurement method based on unmanned aerial vehicle lidar technology of claim 4, wherein the terrain and ground threat constraint model is required to satisfy the following formula:
wherein,
wherein, (x) m ，y m ，z m ) Represents the m-th ground object endpoint coordinate which can cause potential safety hazard, (x) j ，y j ，z j ) Representing three-dimensional space coordinates of closest point to the end point of the ground object in the route, r m Representing threat distance of terrain and ground object point, L safe Represents the safety distance omega between the unmanned plane and the terrain and between the unmanned plane and the ground feature point j Representing threat weights.
6. The existing railway measurement method based on unmanned aerial vehicle laser radar technology as claimed in claim 1, further comprising the steps of extracting a railway section:
classifying the corrected laser point cloud data to separate out ground points;
constructing a terrain model, and cutting a section in the terrain model according to the section position required by design;
and marking the section according to the corrected laser point cloud data and the railway image data.
7. The unmanned aerial vehicle laser radar technology-based existing railway measurement method of claim 1, further comprising, performing survey measurements on a railway:
and generating a railway vector diagram according to the corrected laser point cloud data and the railway image for subsequent railway investigation and measurement.
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