CN105844224A - Point cloud fast ordering method for on-vehicle LiDAR road points - Google Patents
Point cloud fast ordering method for on-vehicle LiDAR road points Download PDFInfo
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Abstract
The invention discloses a point cloud fast ordering method for on-vehicle LiDAR road points, comprising steps of (1) obtaining GPS track data and simplifying the GPS track data; (2) importing the simplified GPS track data obtained in the step (1) into on-vehicle LiDAR point cloud data, taking the simplified GPS track data as auxiliary information, taking the simplified GPS track line direction as a normal vector which is added to a first auxiliary face; and (3) setting a distance d between adjacent second auxiliary faces with the time interval of the simplified GPS track as a threshold and inserting a series of second auxiliary faces according to the set distance d. The point cloud fast ordering method disclosed by the invention enables a computer to fast process the massive on-vehicle point cloud data, reduces the complexity of the method to a great extent, shortens the operation time of the method and improves the processing efficiency of the point cloud data.
Description
Technical field
The present invention relates to earth observation field, fast particularly to a kind of vehicle-mounted LiDAR road waypoint cloud
Speed ordering method.
Background technology
Along with constantly expanding and the continuous lifting of people's application demand of spatial data application,
Obtain the more and more urgent of various terrestrial object information change quickly and accurately.Traditional surveying and mapping technology no matter
Current needs all can not be met in speed or in precision.Vehicle-mounted LiDAR (Light
Detection And Ranging) it is a kind of active ground moving measurement technology, by surveying
The amount laser pulse propagation time, in conjunction with POS system (by differential GPS and inertial navigation navigation system INS
Composition) the position and attitude information that provides, the directly high-precision atural object three-dimensional coordinate of acquisition.At present,
Vehicle-mounted LiDAR technology develops comparative maturity on hardware, but at the data supporting with it
The development of reason method relatively lags behind, and this defines serious restrictive function to vehicle-mounted LiDAR technology.
In the various destination objects that vehicle-mounted LiDAR system obtains, road is one of topmost object,
Road is in uniqueness following feature of main performance in a cloud of spatial shape feature: (1) is in shape
Space banding distribution characteristics is presented in state；(2) some cloud in road surface evenness area falls substantially in level
On face, between point of proximity cloud, the discrepancy in elevation presents slowly varying, often has the prominent of elevation at road edge
Become；(3) in most cases, the width of road is basically identical, and the edge of road both sides is basic
Parallel.In vehicle-mounted laser point cloud chart picture, by naked eyes can clearly tell pavement of road and
Border, but the actual data point expressing road is to present Discrete Distribution in space, various discrete
There is no obvious topological relation between point, and in spatial distribution and uneven, therefore, need yet
Will be on the basis of the feature understanding that road is expressed, could be by roadside, road by series of algorithms
Boundary automatically extracts out.
Summary of the invention
In view of this, the invention reside in offer and a kind of can quickly realize what road separated with limit, slope
Vehicle-mounted LiDAR road waypoint cloud quickly and orderly method, makes computer quickly process mass data
Vehicle-mounted LiDAR is possibly realized, and reduces the complexity of method to a great extent, shortens
Method runs the time, improves the treatment effeciency of cloud data.
For solving the problems referred to above, the present invention adopts the following technical scheme that a kind of vehicle-mounted LiDAR road
Waypoint cloud quickly and orderly method, it is characterised in that comprise the steps:
(1) obtain GPS track data and GPS track data are simplified；
(2) the GPS track data after step (1) being simplified import vehicle-mounted LiDAR point cloud number
According to, and with the GPS track data after simplification for auxiliary information and with the GPS track after simplifying
Line direction is that normal vector adds the first secondary surface；
(3) with the time interval of the GPS track after simplification, adjacent second auxiliary is set for threshold value
Distance d between face, and insert a series of second secondary surfaces according to set distance d, the
Two secondary surfaces have the length and width of the second secondary surface matched with experimental subject；And by
Two secondary surface both sides data points project to, on the first secondary surface in step (2), obtain a series of
Section line data；
(4) data that every section line after being projected in step (3) is comprised as
One independent data processing unit carries out data process, i.e. may separate out road point and highway sideline
Data；
(5) the road waypoint obtained in step (4) is checked and excellent with highway sideline data
Change processes and obtains accurate road boundary.
Above-mentioned vehicle-mounted LiDAR road waypoint cloud quickly and orderly method, in step (1) by with
The POS system of some cloud positioning and orientation obtains GPS track data.
Above-mentioned vehicle-mounted LiDAR road waypoint cloud quickly and orderly method, to GPS in step (1)
Track data simplifies, and specifically comprises the following steps that
(1.1) the GPS track data point that vehicle produces is removed when stopping；
(1.2) the GPS track data point repeated in GPS track data is removed.
Above-mentioned vehicle-mounted LiDAR road waypoint cloud quickly and orderly method, in step (1.1),
For the GPS track data of same GPS track line with time sequencing as parameter threshold, contrast
Corresponding coordinate points, then removes GPS track data point close together, the most removable car
Stop time produce GPS track data point.
Above-mentioned vehicle-mounted LiDAR road waypoint cloud quickly and orderly method, in step (1.2),
After removing the GPS track data point produced when vehicle stops, on same GPS track line
GPS track data point is numbered sequentially in time, then from the original position of numbering, presses
According to number order successively with each GPS track data point as the center of circle, in search radius r and sequence number
More than the GPS track data point of current GPS track data point, remove and current GPS track number
The sequence number difference at strong point and time difference are all higher than given threshold value and are positioned at current GPS track data point
For the center of circle, radius r circle in GPS track data point；Described radius r is more than 0.Search
The size of radius r is according to road modeling permissible accuracy to be modeled and the difference of actual size
Set.
Above-mentioned vehicle-mounted LiDAR road waypoint cloud quickly and orderly method, in step (2), with
GPS track data be auxiliary information add the first secondary surface with simplify after GPS track line hang down
Straight crossing, intersection point is the central point of the first secondary surface.
Above-mentioned vehicle-mounted LiDAR road waypoint cloud quickly and orderly method, in step (2), root
The height and width of the first secondary surface are set according to the width of road, and according to the coordinate and first of central point
The height and width of secondary surface calculate the coordinate on 4 summits of the first secondary surface by following equation
Value:
X=p1-p0,
Y=x z,
vi=p0h·z/2±w·y/2.
In formula: p0, p1For the GPS track line intersection point after inserting the first secondary surface and simplifying；x
Direction vector for the trajectory after simplifying；Z=(0,0,1) is upwardly direction vector；Y is
Direction vector to the right；W, h are width and the height of the first secondary surface；viFor to be inserted first auxiliary
Principal surface apex coordinate.
Above-mentioned vehicle-mounted LiDAR road waypoint cloud quickly and orderly method, step (4) is thrown for utilizing
Shadow puts the section line that cloud obtains on the first secondary surface, arranges threshold value, then utilizes K average to gather
Class method separation pavement of road and road boundary.
Above-mentioned vehicle-mounted LiDAR road waypoint cloud quickly and orderly method, step (4) includes as follows
Step:
(4.1) waypoint cloud in road is little compared to other atural object height value, utilizes the difference of height value
Select two points as cluster centre point；
(4.2) judge after projection o'clock from the distance of two cluster centres, accurate according to minimum range
Then cluster putting cloud after projection, obtain A, B two cluster；
(4.3) due in road waypoint cloud each point between elevation difference value less, it is judged that cluster
Middle height value difference, if distance is more than given threshold value, then repeats step to the A class that elevation is low
And step (4.3) (4.2)；
(4.4) if distance is less than or equal to given threshold value, stop cluster, obtain final
Cluster result, separates road waypoint and side slope point data.
In the present invention, used parameter threshold is according to road practical situation to be modeled and modeling demand
Difference and different.
The invention has the beneficial effects as follows:
1. the present invention can quickly realize the extraction of hills, mountain area road information, it is adaptable to ribbon is big
Scope road structure, has an advantage in that while road boundary extraction accuracy height at its cloud data
Reason efficiency is increased dramatically.
2. the invention enables unordered cloud data ordering, and computer is quickly processed
The vehicle-mounted LiDAR point cloud data of magnanimity become possibility, and reduce algorithm to a great extent
Complexity, shorten algorithm run time, improve efficiency of algorithm.
3. the inventive method step is simple, reasonable in design and realization is convenient, using effect is good, energy
Easy, quick realization unordered some cloud ordering based on in-vehicle LiDAR data, acquired mould
Type precision is high, strong adaptability, and the atural object three-dimensional refined model reconstruction for the later stage has been established good
Data basis.
Accompanying drawing explanation
Fig. 1 is mountain area hilly highway side slope road extraction flow chart；
Fig. 2 cross sectional representation；
Fig. 3 point cloud projected cross-sectional top view；
Fig. 4 point cloud projected cross-sectional side view；
Fig. 5 is for inserting cross section design sketch；
Fig. 6 is Dian Yun drop shadow effect figure；
Fig. 7 highway extraction effect figure；
Fig. 8 highway threedimensional model design sketch.
Detailed description of the invention
For understanding the scheme in the explanation present invention, preferred embodiment is given below and combines accompanying drawing
Describe in detail.
The present invention is a kind of vehicle-mounted LiDAR road waypoint cloud quickly and orderly method, including walking as follows
Rapid:
(1) by obtaining GPS track data and to GPS with the POS system of a cloud positioning and orientation
Track data simplifies；
(2) the GPS track data after step (1) being simplified import vehicle-mounted LiDAR point cloud number
According to, and with the GPS track data after simplification for auxiliary information and with the GPS track after simplifying
Line direction is that normal vector adds the first secondary surface, and the GPS track data after simplifying are believed for auxiliary
GPS track line after each first secondary surface that breath adds and simplification intersects vertically, and intersection point is the
The central point of one secondary surface, arranges the height and width of the first secondary surface, and depends on according to the width of road
The first auxiliary is calculated by following equation according to the coordinate of central point and the height and width of the first secondary surface
The coordinate figure on 4 summits in face:
X=p1-p0,
Y=x z,
vi=p0h·z/2±w·y/2.
In formula: p0, p1For the GPS track line intersection point after inserting the first secondary surface and simplifying；x
Direction vector for the GPS track line after simplifying；Z=(0,0,1) is upwardly direction vector；
Y is direction vector to the right；W, h are width and the height of the first secondary surface；viFor to be inserted
One secondary surface apex coordinate；
(3) with simplify after GPS track time interval for threshold value arrange adjacent cross sectional it
Between distance d, and insert a series of second secondary surface, and second according to set distance
Secondary surface has the length and width of the second secondary surface matched with experimental subject；And by second
Data point in the range of the certain distance of secondary surface both sides projects to the first secondary surface in step (2)
On, obtain a series of section line data；
(4) data that every section line after being projected in step (3) is comprised as
One independent data processing unit carries out data process, and processing method is: utilizes and is projected in cross section
The section line that upper some cloud obtains, arranges threshold value, then utilizes K means clustering method separation road
Road surface and road boundary, comprise the steps:
(4.1) waypoint cloud in road is little compared to other atural object height value, utilizes the difference of height value
Select two points as cluster centre point；
(4.2) judge after projection o'clock from the distance of two cluster centres, accurate according to minimum range
Then cluster putting cloud after projection, obtain A, B two cluster；
(4.3) due in road waypoint cloud each point between elevation difference value less, it is judged that cluster
Middle height value difference, if distance is more than given threshold value, then repeats step to the A class that elevation is low
And step (4.3) (4.2)；
(4.4) if distance is less than or equal to threshold value, stop cluster, finally clustered
As a result, road waypoint and side slope point data are separated；
(5) the road waypoint obtained in step (4) is checked and excellent with highway sideline data
Change processes and obtains accurate road boundary.
GPS track data after wherein simplifying import in vehicle-mounted LiDAR point cloud data, it is achieved
GPS track data are mated with cloud data, and arrange the first auxiliary according to the width of road
The height and width that face is suitable, and the time interval of GPS track after simplifying arranges adjacent for threshold value
Distance d between second secondary surface, and insert a series of second auxiliary according to set distance d
Principal surface, and the second secondary surface has length and the width of the second secondary surface matched with experimental subject
Degree, then projects to first by the data point in the range of each second secondary surface both sides certain distance
On secondary surface, form a series of section line data, and every section line after projecting is comprised
Data can be considered as a scan line, and process as an independent data processing unit,
Then according to subpoint three-dimensional coordinate information on each first secondary surface with on each second secondary surface
Subpoint be that data processing unit carries out cluster analysis to subpoint, can by pavement of road with
Road boundary separates.
In the present embodiment, use the present invention that mountain area hilly highway three-dimensional refined model carries out weight
Building, its handling process is as shown in Figure 1.
First, by obtaining GPS track data and to GPS with the POS system of a cloud positioning and orientation
Track data simplifies；When concrete method for simplifying is to simplify GPS track, time mainly with GPS
Between be spaced apart principle and realize GPS track unification and process, its specific algorithm is broadly divided into two parts:
1. for the GPS track data of same GPS track line with time sequencing as parameter threshold, right
Than corresponding coordinate points, then GPS track data point close together is removed, the most removable
The GPS track data point that vehicle produces when stopping, it may be assumed that needing GPS before data acquisition
Initializing Deng sensor, in initialization procedure, GPS produces substantial amounts of non-tracing point data,
Be vehicle in the process of moving, owing to the information of GPS receiver includes time and coordinate information,
The corresponding GPS position information of each time point, and consecutive number on same GPS track line
Strong point is the most of slight difference, with time sequencing as parameter threshold, and the coordinate points that contrast is corresponding,
Point close together is removed, so can remove the data point that vehicle produces when stopping；2. go
After the GPS track data point produced when vehicle stops, to the GPS on same GPS track line
Track data point is numbered sequentially in time, then from the original position of numbering, according to volume
Number order successively with each GPS track data point as the center of circle, in search radius r (r ＞ 0) and
Sequence number, more than the GPS track data point of current GPS track data point, is removed and current GPS rail
The sequence number difference of mark data point and time difference are all higher than given threshold value and are positioned at current GPS track number
Strong point is the GPS track data point in the circle of the center of circle, radius r；I.e. when removing vehicle and stopping
On the basis of the data point produced, each data point is numbered sequentially in time；From volume
Number original position, according to number order successively with each data point as the center of circle, search for a spacing
In radius, and sequence number is more than the point of current data point, it is judged that the sequence between current point and each point
Number difference and time difference, then remove this point more than given threshold value, so can remove in track and repeat
Tracing point, it is achieved the unification on track.Then, using simplify GPS track data as
Auxiliary information is loaded into the original vehicle-mounted LiDAR three dimensional point cloud of mountain area hilly highway road
In, and add the first secondary surface with the GPS track line direction after simplification for normal vector, to simplify
After GPS track data be auxiliary information add each first secondary surface with simplify after GPS
Trajectory intersects vertically, and intersection point is the central point of the first secondary surface, as in figure 2 it is shown, and basis
The width of road arranges a height of 5m of the first secondary surface, a width of 16m, then according to central point
The height and width of coordinate and the first secondary surface calculate 4 tops of the first secondary surface by following equation
The coordinate figure of point:
X=p1-p0,
Y=x z,
vi=p0h·z/2±w·y/2.
In formula: p0, p1For the GPS track line intersection point after inserting the first secondary surface and simplifying；x
Direction vector for the GPS track line after simplifying；Z=(0,0,1) is upwardly direction vector；
Y is direction vector to the right；W, h are width and the height of the first secondary surface；viFor to be inserted
One secondary surface apex coordinate.
Then set two with the time interval of the GPS track after simplification for threshold value and be inserted into second
Distance between secondary surface, and insert a series of second secondary surfaces according to set distance, and
Second secondary surface has length 16m and the width of the second secondary surface matched with road to be modeled
5m.In the present embodiment, it is second auxiliary with the time interval Δ t=0.4S of GPS track after simplifying
Principal surface interval threshold adds the second secondary surface, as it is shown in figure 5, and for each second secondary surface
For, the data point in the distance range of its both sides 0.2S is projected on the first secondary surface, as
Shown in Fig. 3 and 4, constitute a series of section line data, as shown in Figure 6；Then projection is utilized
First secondary surface is put the section line that cloud obtains, threshold value is set, recycle K mean cluster side
Method separation pavement of road and road boundary, and road boundary is fitted.Draw road boundary
Information, as it is shown in fig. 7, finally road is carried out reconstructing three-dimensional model according to network forming principle,
To the threedimensional model of road, as shown in Figure 8.
In this example, the developed width of road to be modeled is 15.523m, is extracted by prior art
The width of road be 15.605m, and the width of the road extracted by the present invention is
15.510m.As can be seen here, the model accuracy acquired in the present invention is higher, can be the ground in later stage
Thing three-dimensional refined model is rebuild and is established good data basis.
To in the hilly highway three-dimensional refined model process of reconstruction of mountain area, step of the present invention is simple,
Reasonable in design and realize convenient, using effect is good, can easy, quickly realize based on vehicle-mounted LiDAR
Unordered some cloud ordering of data, acquired model accuracy is high, and strong adaptability, for the later stage
Atural object three-dimensional refined model is rebuild and has been established good data basis.
Above-described embodiment is only for clearly demonstrating the invention example, and not
Restriction to the invention detailed description of the invention.Those of ordinary skill in the field are come
Say, can also make other changes in different forms on the basis of the above description.This
In without also cannot all of embodiment be given exhaustive.All in the spirit and principles in the present invention
Within any obvious change extended out or variation want still in the invention right
Among the protection domain asked.
Claims (9)
1. a vehicle-mounted LiDAR road waypoint cloud quickly and orderly method, it is characterised in that bag
Include following steps:
(1) obtain GPS track data and GPS track data are simplified；
(2) the GPS track data after step (1) being simplified import vehicle-mounted LiDAR point cloud number
According to, and with the GPS track data after simplification for auxiliary information and with the GPS track after simplifying
Line direction is that normal vector adds the first secondary surface；
(3) with the time interval of the GPS track after simplification, adjacent second auxiliary is set for threshold value
Distance d between face, and insert a series of second secondary surfaces according to set distance d, the
Two secondary surfaces have the length and width of the second secondary surface matched with road to be modeled；And will
Second secondary surface both sides data point projects on the first secondary surface in step (2), and obtaining one is
The section line data of row；
(4) data that every section line after being projected in step (3) is comprised as
One independent data processing unit carries out data process, i.e. may separate out road point and highway sideline
Data；
(5) the road waypoint obtained in step (4) is checked and excellent with highway sideline data
Change processes and obtains accurate road boundary.
Vehicle-mounted LiDAR road the most according to claim 1 waypoint cloud quickly and orderly method,
It is characterized in that, by obtaining GPS with a POS system for cloud positioning and orientation in step (1)
Track data.
Vehicle-mounted LiDAR road the most according to claim 2 waypoint cloud quickly and orderly method,
It is characterized in that, GPS track data are simplified in (1) by step, specifically comprise the following steps that
(1.1) the GPS track data point that vehicle produces is removed when stopping；
(1.2) the GPS track data point repeated in GPS track data is removed.
Vehicle-mounted LiDAR road the most according to claim 3 waypoint cloud quickly and orderly method,
It is characterized in that, in step (1.1), for the GPS track number of same GPS track line
Time sequencing is parameter threshold according to this, and the coordinate points that contrast is corresponding, then by GPS close together
Track data point is removed, the GPS track data point that the most removable vehicle produces when stopping.
Vehicle-mounted LiDAR road the most according to claim 4 waypoint cloud quickly and orderly method,
It is characterized in that, in step (1.2), remove the GPS track data produced when vehicle stops
After Dian, the GPS track data point on same GPS track line is compiled sequentially in time
Number, then from the original position of numbering, according to number order successively with each GPS track data
Put in being the center of circle, search radius r and sequence number is more than the GPS track of current GPS track data point
Data point, removes the difference of the sequence number with current GPS track data point and time difference is all higher than given threshold
Value and be positioned at current GPS track data point as the center of circle, the GPS track number of the circle of radius r
Strong point；Described radius r is more than 0.
Vehicle-mounted LiDAR road the most according to claim 5 waypoint cloud quickly and orderly method,
It is characterized in that, in step (2), the GPS track data after simplifying add for auxiliary information
The first secondary surface entered with simplify after GPS track line intersect vertically, intersection point is the first secondary surface
Central point.
Vehicle-mounted LiDAR road the most according to claim 6 waypoint cloud quickly and orderly method,
It is characterized in that, in step (2), according to the width of road arrange the first secondary surface height and
Width, and calculated by following equation according to the coordinate of central point and the height and width of the first secondary surface
The coordinate figure on 4 summits of the first secondary surface:
X=p1-p0,
Y=x z,
vi=p0h·z/2±w·y/2.
In formula: p0, p1For the GPS track line intersection point after inserting the first secondary surface and simplifying；x
Direction vector for the GPS track line after simplifying；Z=(0,0,1) is upwardly direction vector；
Y is direction vector to the right；W, h are width and the height of the first secondary surface；viFor to be inserted
One secondary surface apex coordinate.
8. according to the arbitrary described vehicle-mounted LiDAR road waypoint cloud quickly and orderly of claim 1～7
Change method, it is characterised in that step (4) is that utilization is projected on the first secondary surface some clouds and obtains
Section line, threshold value is set, then utilizes K means clustering method separation pavement of road and road
Border.
Vehicle-mounted LiDAR road the most according to claim 8 waypoint cloud quickly and orderly method,
It is characterized in that, step (4) comprises the steps:
(4.1) waypoint cloud in road is little compared to other atural object height value, utilizes the difference of height value
Select two points as cluster centre point；
(4.2) judge after projection o'clock from the distance of two cluster centres, accurate according to minimum range
Then cluster putting cloud after projection, obtain A, B two cluster；
(4.3) due in road waypoint cloud each point between elevation difference value less, it is judged that cluster
Middle height value difference, if distance is more than given threshold value, then repeats step to the A class that elevation is low
And step (4.3) (4.2)；
(4.4) if distance is less than or equal to given threshold value, stop cluster, obtain final
Cluster result, separates road waypoint and side slope point data.
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| CN110008921B (en) * | 2019-04-12 | 2021-12-28 | 北京百度网讯科技有限公司 | Road boundary generation method and device, electronic equipment and storage medium |
| CN110763147A (en) * | 2019-10-31 | 2020-02-07 | 中交三航局第三工程有限公司 | Cofferdam deformation monitoring method based on three-dimensional laser scanning technology |
| CN111768417A (en) * | 2020-06-23 | 2020-10-13 | 中南大学 | Railway wagon overrun detection method based on monocular vision 3D reconstruction technology |
| CN111768417B (en) * | 2020-06-23 | 2023-12-05 | 中南大学 | Railway wagon overrun detection method based on monocular vision 3D reconstruction technology |
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| CN112164081B (en) * | 2020-09-30 | 2023-04-21 | 西南交通大学 | Vehicle-mounted LiDAR point cloud railway cross section contour extraction method |
| CN112164080B (en) * | 2020-09-30 | 2023-05-09 | 西南交通大学 | Vehicle-mounted LiDAR point cloud railway track top point extraction method |
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