CN108657222B - Railroad track gauge and horizontal parameters measurement method based on vehicle-mounted Lidar point cloud - Google Patents
Railroad track gauge and horizontal parameters measurement method based on vehicle-mounted Lidar point cloud Download PDFInfo
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- CN108657222B CN108657222B CN201810414013.1A CN201810414013A CN108657222B CN 108657222 B CN108657222 B CN 108657222B CN 201810414013 A CN201810414013 A CN 201810414013A CN 108657222 B CN108657222 B CN 108657222B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61K—AUXILIARY EQUIPMENT SPECIALLY ADAPTED FOR RAILWAYS, NOT OTHERWISE PROVIDED FOR
- B61K9/00—Railway vehicle profile gauges; Detecting or indicating overheating of components; Apparatus on locomotives or cars to indicate bad track sections; General design of track recording vehicles
- B61K9/08—Measuring installations for surveying permanent way
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
Abstract
The invention discloses a kind of railroad track gauges and horizontal parameters measurement method based on vehicle-mounted Lidar point cloud, and steps are as follows: S1. constructs model trajectory；S2. model trajectory and vehicle-mounted Lidar track point cloud are done into initial matching；S3. model trajectory and vehicle-mounted Lidar track point cloud are done into accurate matching, calculates separately the distance that left and right rail model working edge and upper tyre tread arrive corresponding vehicle-mounted Lidar point cloud, and according to the distance of rail model working edge to vehicle-mounted Lidar point cloud adjustment gauge；S4. step S3 is repeated, iteration carries out, until rail model working edge and upper tyre tread to the vehicle-mounted Lidar point cloud distance respectively less than threshold value of setting.The present invention realizes railroad track geometric parameter high-acruracy survey, realize the matching of the model trajectory Yu vehicle-mounted Lidar track point cloud of taking gauge and horizontal parameters into account, and gauge and horizontal parameters based on model trajectory measurement railroad track, technical support is provided to use vehicle-mounted Lidar technology to carry out railway track measurement.
Description
Technical field
The present invention relates to orbit measurement technical field, specially a kind of railroad track gauge based on vehicle-mounted Lidar point cloud
With horizontal parameters measurement method.
Background technique
Currently, railroad track gauge and horizontal parameters measurement rely primarily on the manual types upper track such as gauging rule, track detection car
Measurement.By 2017, Chinese Railway operating mileage more than 120,000 kilometers, traditional orbit measurement method measurement accuracy,
Operating efficiency, operation safety, operation interference etc. have been unable to meet the requirement of railway fast development.In this case,
By the technical change to field operation measurement data acquisition modes, urgent need seek it is efficient, safe, to the low interference of operation or zero interference
Completely new railroad survey technology and methods.Vehicle-mounted Lidar(Light Detection And Ranging, laser acquisition and survey
Amount) technology can provide a kind of high with quick obtaining railroad track high density, high-precision point cloud data for railway track measurement
Efficiency, high-precision measurement means.Since the rail point cloud obtained using vehicle-mounted Lidar is non-structured discrete one by one
Point, rail point cloud includes random error and much noise point and being blocked due to scanning causes part rail point cloud to lack, so such as
What is that vehicle-mounted Lidar technology is urgently to be resolved for railroad survey using the geometric parameter that rail point cloud at random obtains railroad track
The problem of and difficult point.
Chinese invention patent publication number CN104236499A discloses a kind of railway automatic measurement side based on point cloud data
Method, this method can obtain the gauge and horizontal parameters of railroad track from cloud, but have the disadvantage in that 1 to reduce point cloud
Present in noise and influence the problems such as Density inhomogeneity to measurement result, first each point in rail point cloud is led
A certain range correlation similar point in space is fitted to a more steady point by constituent analysis, then by up-sampling to every
Combine existing several click-through row interpolations to increase dot density in a neighborhood of a point space.This leads to point used in this method
Cloud is the rail point cloud obtained after over-fitting and interpolation, and not true rail original point cloud, this can be to orbit geometry parameter
The authenticity of measurement result has an impact.2 be the gauge and horizontal parameters for obtaining track, using plane fitting algorithm to rail side
Millet cake cloud is fitted, then the farthest point of selected distance fit Plane is as rail level highest point, then choose rail level highest point with
Point at lower 1.6cm is as gauge measurement point.Since gage line is made of a plane and multiple curved surfaces, so only making
Orbit measurement required precision is unsatisfactory for the precision that plane fitting algorithm obtains gage line.And it is determined since rail has
Geometry, there is specific spatial relation, but this method institute for measuring the point of track level and gauge in rail
Specific spatial relation is not present in the track level and gauge measurement point of acquisition, so measuring resulting rail using this method
Road level and gauge parameters precision are unsatisfactory for the requirement of orbit measurement.3 railroad tracks or so rail is parallel, but this method is used and put down
The left and right gage line plane that face fitting algorithm obtains is not parallel, so the gauge measurement point based on the acquisition of working edge plane is not
Accurately.
Chinese invention patent publication number CN107792115A disclose it is a kind of using three-dimensional laser point cloud automatically extract it is existing
Line rail crest level method, this method are first segmented the elevation maximum value of each section of rail three-dimensional laser point cloud of statistics, then with segmentation
Elevation maximum filters out a certain range of three-dimensional laser point cloud of left and right rail rail surface or more as rail level point cloud, and extracts rail level
Edge line finally extracts rail top middle line neighborhood rail level point cloud for determining rail top middle line, calculates the elevation average value of neighborhood point.It should
Method can not obtain the gauge parameter of track, and since this method can only obtain the elevation of left and right rail top of steel rail, not build
The corresponding relationship of vertical left and right rail central point, so being unable to measure high-precision track horizontal parameters.
Summary of the invention
It is surveyed in view of the above-mentioned problems, can be realized railroad track geometric parameter high-precision the purpose of the present invention is to provide one kind
The railroad track gauge and horizontal parameters measurement method based on vehicle-mounted Lidar point cloud of amount.Technical solution is as follows:
A kind of railroad track gauge and horizontal parameters measurement method based on vehicle-mounted Lidar point cloud, comprising the following steps:
S1: the three-dimensional model trajectory including left and right rail is constructed according to standard gauge, and is marked respectively on model trajectory
Determine in the working edge, upper tyre tread and rail top center and working edge of left and right rail away from the position at the 16mm of rail top surface；
S2: vehicle-mounted Lidar rail point cloud and model trajectory are done into initial matching, the position and posture for adjusting model trajectory make
It is consistent with vehicle-mounted Lidar rail point cloud；
S3: establishing the working edge of model trajectory or so rail and the corresponding relationship of upper tyre tread and vehicle-mounted Lidar rail point cloud,
And match parameter is solved according to least square method；The posture of model and position are adjusted according to match parameter, and calculated:
Left side rail model working edge corresponding position distance d1 in vehicle-mounted Lidar rail point cloud,
Tyre tread corresponding position distance d2 in vehicle-mounted Lidar rail point cloud on the rail model of left side,
Right side rail model working edge corresponding position distance d3 in vehicle-mounted Lidar rail point cloud,
Tyre tread corresponding position distance d4 in vehicle-mounted Lidar rail point cloud on the rail model of right side；
The gauge adjustment parameter of model trajectory is corrected according to the mean value of d1 and d3, so that model gauge is close to true gauge；
S4: repeating step S3, and iteration carries out, until d1, d2, d3 and d4 are respectively less than the threshold value set；
S5: the Z coordinate difference at the left and right rail top of steel rail center of the model trajectory after the completion of matching is the horizontal parameters of track；
Distance away from the position at the 16mm of rail top surface in the left and right gage line of model trajectory after the completion of matching is gauge parameter.
Further, the S2 method particularly includes:
S21: the barycentric coodinates of vehicle-mounted Lidar track point cloud and model trajectory are calculated separately；
S22: the principal direction based on rail model on the left of left side rail top center calculation in rail model, and it is based on PCA algorithm
Calculate the principal direction of left side rail point cloud in vehicle-mounted Lidar rail point cloud；
S23: translation track model is overlapped its center of gravity and the center of gravity of vehicle-mounted Lidar track point cloud, and swing-around trajectory model
Keep the principal direction of left side rail model consistent with the left side principal direction of rail point cloud in vehicle-mounted Lidar track point cloud.
Further, the model trajectory length is 1m, and the track primary of left and right rail is away from for 1435mm.
The beneficial effects of the present invention are: the railroad track gauge and level proposed by the present invention based on vehicle-mounted Lidar point cloud
Measurement method of parameters is carried out by the track threedimensional model entirety that will include two parallel steel rails with vehicle-mounted Lidar track point cloud
Matching is solved using left and right rail in track threedimensional model obtained by single steel rail model and vehicle-mounted Lidar rail point cloud matching
Not parallel and gauge and the inaccurate problem of horizontal parameters, to use vehicle-mounted Lidar rail point cloud to measure railroad track geometric parameters
Number is laid a good foundation.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow charts of the railroad track gauge of vehicle-mounted Lidar point cloud and horizontal parameters measurement method.
Fig. 2 is vehicle-mounted Lidar track point cloud.
Fig. 3 be length be 1m, point spacing be 0.5mm, the track threedimensional model that gauge is 1.435m.
Fig. 4 is rail model partial structurtes explanation.
Fig. 5 is the final matching results comprising vehicle-mounted Lidar track point cloud and track threedimensional model.
In figure: 1- model trajectory；2- working edge；The upper tyre tread of 3-；4- rail top center；Away from rail top surface in 5- gage line
Position at 16mm.
Specific embodiment
The present invention is described in further details in the following with reference to the drawings and specific embodiments.It is proposed by the present invention based on vehicle-mounted
The railroad track gauge and horizontal parameters measurement method final goal of Lidar point cloud are obtained according to vehicle-mounted Lidar track point cloud
The gauge and horizontal parameters of railroad track, detailed process are as shown in Figure 1.The vehicle-mounted Lidar track point cloud that the present embodiment uses is such as
Shown in Fig. 2, the present embodiment establishes track threedimensional model according to 60 rail standard shapes.
S1: the model trajectory 1 including left and right rail is constructed according to standard gauge 1435mm, and demarcates the work of left and right rail
Side 2 and 3 part of upper tyre tread and the rail top center 4 of left and right rail, away from the position 5 at the 16mm of rail top surface in gage line, such as
Shown in Fig. 4.
S11: the model trajectory comprising two straight rails is established according to standard rail cross-sectional shape.As shown in figure 3, rail
Left and right rail is parallel in road model, the length of model trajectory about 1m(2 sleepers distance), left and right rail is initial in model trajectory
Gauge is 1435mm, and has a gauge adjustment parameter.
S12: corresponding mark is set separately in the working edge of left and right rail and upper tread portions in model trajectory.
S13: away from the position at the 16mm of rail top surface in the rail top center of left and right rail and gage line in model trajectory
Special mark is respectively set.
S2: vehicle-mounted Lidar track point cloud and track threedimensional model are done into initial matching, adjust the position of track threedimensional model
It is allowed to almost the same with vehicle-mounted Lidar rail point cloud with posture.
S21: the barycentric coodinates of vehicle-mounted Lidar track point cloud and track threedimensional model are calculated separately.
S22: the principal direction based on rail model on the left of rail top center calculation in the rail model of left side, and it is based on PCA
(principal components analysis principal component analysis) algorithm principle calculates left side in vehicle-mounted Lidar rail point cloud
The principal direction of rail point cloud.
S23: translation track threedimensional model makes the center of gravity of track threedimensional model and the center of gravity weight of vehicle-mounted Lidar track point cloud
It closes, and swing-around trajectory threedimensional model makes left side rail point cloud in the principal direction and vehicle-mounted Lidar track point cloud of left side rail model
Principal direction it is consistent.
S3: vehicle-mounted Lidar track point cloud and track threedimensional model are done into accurate matching, and according to work in track threedimensional model
Make side and adjusts gauge at a distance from corresponding vehicle-mounted Lidar rail point cloud.
Step S31: being based on ICP algorithm principle, track threedimensional model matched with vehicle-mounted Lidar track point cloud, and
Solve match parameter.The point in working edge and upper tread portions and rail Lidar point cloud is determined according to model surface normal direction
Corresponding relationship, the rest part of model be not involved in corresponding relationship foundation.
Step S32: it after being adjusted according to match parameter to the posture of model and position, calculates separately:
Left side rail model working edge corresponding position distance d1 in vehicle-mounted Lidar rail point cloud,
Tyre tread corresponding position distance d2 in vehicle-mounted Lidar rail point cloud on the rail model of left side,
Right side rail model working edge corresponding position distance d3 in vehicle-mounted Lidar rail point cloud,
Tyre tread corresponding position distance d4 in vehicle-mounted Lidar rail point cloud on the rail model of right side.
D1 and d3 is for adjusting model gauge and judging whether matching is completed, and d2 and d4 are for judging whether matching is completed.
Step S33: correcting the gauge adjustment parameter of model trajectory according to the mean value of d1 and d3, so that model gauge is close to very
Real gauge.Left and right rail keeping parallelism relationship when correcting the gauge adjustment parameter change model gauge of model trajectory, in model.
Step S4: step S3 is repeated, iteration carries out, until d1, d2, d3 and d4 are respectively less than 0.1mm.Fig. 5 be comprising
The final matching results of vehicle-mounted Lidar track point cloud and track threedimensional model.
Step S5: the Z coordinate difference at the left and right rail top of steel rail center 4 of the model trajectory after the completion of matching is the level of track
Parameter；Difference in height of the left and right rail top of steel rail central point of Z coordinate difference namely model trajectory in vertical direction.Rail after the completion of matching
The distance of position 5 is gauge parameter at 16mm under the left and right rail rail level of road model.
Claims (2)
1. a kind of railroad track gauge and horizontal parameters measurement method based on vehicle-mounted Lidar point cloud, which is characterized in that including with
Lower step:
S1: the three-dimensional model trajectory (1) including left and right rail is constructed according to standard gauge, and on model trajectory (1) respectively
Demarcate left and right rail working edge (2), upper tyre tread (3) and rail top center (4) and working edge (2) in away from rail top surface 16mm at
Position (5)；
S2: vehicle-mounted Lidar rail point cloud and model trajectory (1) are done into initial matching, the position and posture for adjusting model trajectory make
It is consistent with vehicle-mounted Lidar rail point cloud；
S3: working edge (2) and upper tyre tread (3) pass corresponding with vehicle-mounted Lidar rail point cloud of model trajectory or so rail are established
System, and match parameter is solved according to least square method；The posture of model and position are adjusted according to match parameter, and counted
It calculates:
Left side rail model working edge corresponding position distance d1 in vehicle-mounted Lidar rail point cloud,
Tyre tread corresponding position distance d2 in vehicle-mounted Lidar rail point cloud on the rail model of left side,
Right side rail model working edge corresponding position distance d3 in vehicle-mounted Lidar rail point cloud,
Tyre tread corresponding position distance d4 in vehicle-mounted Lidar rail point cloud on the rail model of right side；
The gauge adjustment parameter of model trajectory is corrected according to the mean value of d1 and d3, so that model gauge is close to true gauge；
S4: repeating step S3, and iteration carries out, until d1, d2, d3 and d4 are respectively less than the threshold value set；
S5: the Z coordinate difference at the left and right rail top of steel rail center (4) of the model trajectory after the completion of matching is the horizontal parameters of track；
Distance away from the position (5) at the 16mm of rail top surface in the left and right gage line of model trajectory after the completion of matching is gauge ginseng
Number；
The S2's method particularly includes:
S21: the barycentric coodinates of vehicle-mounted Lidar rail point cloud and model trajectory are calculated separately；
S22: the principal direction based on rail model on the left of left side rail top center calculation in rail model, and calculated based on PCA algorithm
The principal direction of left side rail point cloud in vehicle-mounted Lidar rail point cloud；
S23: translation track model is overlapped its center of gravity and the center of gravity of vehicle-mounted Lidar rail point cloud, and swing-around trajectory model makes a left side
The principal direction of side rail model is consistent with the left side principal direction of rail point cloud in vehicle-mounted Lidar rail point cloud.
2. the railroad track gauge and horizontal parameters measurement method according to claim 1 based on vehicle-mounted Lidar point cloud,
It is characterized in that, model trajectory (1) length is 1m, and the track primary of left and right rail is away from for 1435mm.
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| CA2892952C (en) | 2015-01-19 | 2019-10-15 | Tetra Tech, Inc. | Protective shroud |
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| CN113446939B (en) * | 2021-07-02 | 2022-07-29 | 北京交通大学 | Wheel-rail contact geometric parameter measuring method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103473734A (en) * | 2013-09-16 | 2013-12-25 | 南京大学 | Power line extracting and fitting method based on in-vehicle LiDAR data |
| CN104236499A (en) * | 2014-10-15 | 2014-12-24 | 厦门大学 | Automatic measurement method for railway on basis of point cloud data |
| CN104463872A (en) * | 2014-12-10 | 2015-03-25 | 武汉大学 | Classification method based on vehicle-mounted LiDAR point cloud data |
| CN105628026A (en) * | 2016-03-04 | 2016-06-01 | 深圳大学 | Positioning and posture determining method and system of mobile object |
| CN106558096A (en) * | 2015-09-30 | 2017-04-05 | 株式会社日立大厦系统 | The modeling method of the model building device, program and cage guide of three dimensional point cloud |
| CN107792115A (en) * | 2017-09-07 | 2018-03-13 | 中铁二院工程集团有限责任公司 | One kind automatically extracts both wired rail crest level methods using three-dimensional laser point cloud |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104344795A (en) * | 2013-07-24 | 2015-02-11 | 鸿富锦精密工业（深圳）有限公司 | Point cloud profile measuring system and method |
-
2018
- 2018-05-03 CN CN201810414013.1A patent/CN108657222B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103473734A (en) * | 2013-09-16 | 2013-12-25 | 南京大学 | Power line extracting and fitting method based on in-vehicle LiDAR data |
| CN104236499A (en) * | 2014-10-15 | 2014-12-24 | 厦门大学 | Automatic measurement method for railway on basis of point cloud data |
| CN104463872A (en) * | 2014-12-10 | 2015-03-25 | 武汉大学 | Classification method based on vehicle-mounted LiDAR point cloud data |
| CN106558096A (en) * | 2015-09-30 | 2017-04-05 | 株式会社日立大厦系统 | The modeling method of the model building device, program and cage guide of three dimensional point cloud |
| CN105628026A (en) * | 2016-03-04 | 2016-06-01 | 深圳大学 | Positioning and posture determining method and system of mobile object |
| CN107792115A (en) * | 2017-09-07 | 2018-03-13 | 中铁二院工程集团有限责任公司 | One kind automatically extracts both wired rail crest level methods using three-dimensional laser point cloud |
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