CN106780524B - Automatic extraction method for three-dimensional point cloud road boundary - Google Patents

Automatic extraction method for three-dimensional point cloud road boundary Download PDF

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CN106780524B
CN106780524B CN201610996779.6A CN201610996779A CN106780524B CN 106780524 B CN106780524 B CN 106780524B CN 201610996779 A CN201610996779 A CN 201610996779A CN 106780524 B CN106780524 B CN 106780524B
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CN106780524A (en
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李军
宰大卫
林阳斌
郭裕兰
王程
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Xiamen University
National University of Defense Technology
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    • G06T2207/10028—Range image; Depth image; 3D point clouds
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Abstract

The invention relates to the field of point cloud processing, and particularly discloses a three-dimensional point cloud road boundary automatic extraction method which comprises the following steps of S1, screening seed points for a whole three-dimensional point cloud data set P to perform voxel division, S2, extracting boundary points between adjacent non-coplanar voxels by using a α -shape algorithm, S3, extracting road boundary points by using an energy minimization algorithm based on graph cut, S4, removing outliers based on an Euclidean distance clustering algorithm, and S5, fitting the extracted road boundary points into a smooth curve.

Description

Automatic extraction method for three-dimensional point cloud road boundary
Technical Field
The invention relates to the field of point cloud processing, in particular to a method for automatically extracting a three-dimensional point cloud road boundary.
Background
The road is used as a traffic infrastructure, and the digital management and construction of the road have important significance for application such as city planning, traffic management and navigation. As a high and new surveying and mapping technology which develops rapidly, compared with the traditional surveying and mapping means, the vehicle-mounted laser scanning technology has the advantages of high data acquisition speed, high data precision, non-contact active measurement, strong real-time performance and the like, can quickly acquire detailed three-dimensional space information of roads and ground objects on two sides in the normal traveling process of a vehicle, and has obvious advantages for acquiring road information in strip distribution.
The traditional method for acquiring road information mainly comprises two modes of manual measurement and digital photogrammetry. Although the manual measurement can obtain more accurate information such as road coordinates and the like, the measurement speed is slow, and the updating period of the road information is long; digital photogrammetry is gradually developed and matured along with the development of scientific technology and the wide application of high and new technologies such as computers, but is limited by reasons such as image resolution and the like, and the requirements on road characteristic information and precision extracted from images still need to be further improved. The vehicle-mounted laser scanning system comprises a global positioning system, an inertial navigation system, a laser scanner, a CCD camera and the like, and becomes a new means for acquiring three-dimensional space data. The vehicle-mounted laser scanning technology can effectively save the measurement time, improve the measurement efficiency, shorten the road information updating period, avoid the exposure risk of measurement operators in the traffic environment and provide powerful technical support for the survey and planning of urban space resources.
However, the city is usually complex in environment, not only are the accessory components complex and numerous, but also the scanned targets are shielded from each other to cause data loss, and the automatic extraction of the road boundary is examined. In addition, the complexity caused by different road environments (such as vehicle parking, vegetation surrounding, fences, etc.) increases the difficulty of automatic extraction of road boundaries. Therefore, the difficulty and the requirement for rapidly and automatically extracting the road boundary from the massive point cloud are high, but the technology has important economic and application requirements and is a research hotspot at home and abroad.
At present, most of research on vehicle-mounted laser scanning data processing focuses on ground object point cloud classification, building facade information extraction and modeling, road accessory facility extraction and the like, while research on road boundary information extraction is relatively few, and main work can be divided into indirect extraction and direct extraction.
The indirect extraction method generally first uses the attributes (height, intensity, wavelength, etc.) of the point cloud to generate a depth image, and then uses image processing methods (cropping, fitting, filtering, etc.) to detect and extract the road boundary. For indirect extraction methods, namely converting point clouds into depth images and then extracting road boundaries by using image processing methods, errors are inevitably generated in the conversion process by the methods, and accurate road boundary results are difficult to obtain.
The direct extraction method generally uses road features (such as planes, road teeth, etc.) to detect and extract road boundaries. A common method is to extract the road surface by a random sample consensus (RANSAC) method and then obtain the road boundary by a linear fitting algorithm. The road tooth is detected by a gaussian filtering method or a sliding window method, thereby obtaining a road boundary. For the direct extraction method, the application range of the scene is greatly limited. The method of extracting the road surface based on random sample consensus (RANSAC) has difficulty in the case of road undulation, and some details of the extracted road surface are lost. The method of using gaussian filtering or sliding window to detect the road tooth is often challenging when facing irregular road boundaries (such as walls, fences) or vegetation surrounding.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides the automatic extraction method of the three-dimensional point cloud road boundary, which can be directly operated on large-scale three-dimensional point cloud, can be used for different scenes, has high calculation speed and good algorithm robustness, and can quickly extract the road boundary.
The specific technical scheme is as follows:
a three-dimensional point cloud road boundary automatic extraction method comprises the following steps:
s1, screening seed points of the obtained whole three-dimensional point cloud data set P for superpixel division;
s2, extracting boundary points between adjacent non-coplanar hyper-voxels by using a α -shape algorithm;
s3, extracting road boundary points by using an energy minimization algorithm based on graph cut;
s4, removing outliers based on an Euclidean distance clustering algorithm;
and S5, fitting the extracted road boundary points into a smooth curve.
Preferably, the partitioning procedure of the super-voxel in step S1 is as follows:
s11 solving fitting plane Tp(pi)：
For each of the entire three-dimensional point cloud data sets PInput point piIts tangent plane Tp (p)i) Can be represented as a point o from its centeriSum normal vector nlFormed doublets, i.e.
Figure BDA0001150616040000031
Any point p to Tp (p) in three-dimensional spacei) Can be expressed as
Figure BDA0001150616040000032
Note piK neighbors of (a) are set of Nbk(pi) The best fit plane in the least squares sense can be obtained by solving the following equation
Figure BDA0001150616040000033
The fitted plane is then optimized using an iterative reweighted least squares method
Figure BDA0001150616040000034
Solving the least square equation of the band weight to obtain the optimized fitting plane Tp (p)i)
Figure BDA0001150616040000035
To plane Tp (p)i) The above process is repeated until the algorithm converges.
S12, removing non-ground points:
note that the final component tangent plane Tp (p)i) Three eigenvalues of the covariance matrix of the point set of (a) are λ1,λ2And λ3And satisfy lambda1≥λ2≥λ3. Then point piSmoothness of(s) (p)i) Can be expressed as
Figure BDA0001150616040000041
The following two constraints are used to remove non-ground points:
A. removing points significantly above the road surface (z)i≥5m)(ziIs a point piHeight value of (d);
B. and removing the point of which the included angle between the normal vector and the Z axis is more than 22.5 degrees.
S13, calculating the hyper-voxel fi：
Removing the point set P after the non-ground pointgAccording to the smoothness ranking of each point, the point with high smoothness is selected as a seed point. The hyper-voxels are computed in such a way that region growing starts from the seed point. Let voxel fiFormalized definition as a set of points P to which there belongsiCenter point oiAnd the normal vector nlFormed triplet
Figure BDA0001150616040000042
Seed for each seed pointiLet its initial hyper-voxel fiIs { p }iCenter point and normal vector are Tp (p) respectivelyi).oiAnd Tp (p)i).nl. Then, the principle of width priority is adopted to fiA region growing is performed. For each candidate point pjIf (1) p is satisfiedjTo piIs less than a threshold value Rseed(ii) a (2) Vector Tp (p)j).nlAnd Tp (p)i).nlThe included angle of the angle is less than 22.5 degrees; (3) p is a radical ofjTo Tp (p)i) Is less than a threshold e; then p will bejIs added to fiIs concentrated. When f isiWhen no longer expandable, according to fi.piFitting the plane using least squares, and fitting fi.nlg is updated as the normal vector of the fitted plane. And (3) assigning points to the hyper-voxels by adopting local K-means clustering on the basis of the initial facets, and ensuring that the distance from each point to the hyper-voxel to which the point belongs is smaller than the distance from each point to other hyper-voxels. The distance function here is defined as:
Figure BDA0001150616040000043
wherein Ds,DnAnd DiRespectively euclidean distance, normal vector distance and intensity distance. Omegas,ωnAnd ωiRespectively, the corresponding weights.
Preferably, the specific steps of extracting boundary points between adjacent non-coplanar hyper-voxels using the α -shape algorithm in step S2 are as follows:
after the point cloud is segmented into the hyper-voxels, for each hyper-voxel, an α -shape algorithm can be used to extract boundary points, and meanwhile, the boundary points between two hyper-voxels coplanar with each other are removed, namely, if the included angle of normal vectors of the two hyper-voxels is less than 22.5 degrees, the boundary points between the two hyper-voxels are deleted, and the boundary point P is the boundary point at the momentbIncluding road boundary points and non-road boundary points.
Preferably, step S3 uses an energy minimization algorithm based on graph cut to extract road boundary points, as follows:
and providing vehicle running track line data by a vehicle-mounted laser scanning system, and taking the track line data as an initial observation model of a graph cutting algorithm. The energy formula is defined as:
E(f)＝Edata(f)+λ·Esmooth(f)
Figure BDA0001150616040000051
Figure BDA0001150616040000052
Figure BDA0001150616040000053
where P isbRefers to the set of boundary points extracted in step 2. n is piThe potential of the point set of the belonging superpixel. Δ djIs pointing at pjTo a straight line LpiThe distance of (c). Δ diIs pointing at piAll points in the neighborhood to the straight line LpiAverage redundancy of (2). Sigma1Mean redundancy of all points. Straight line LpiIs defined as passing through point piAnd the direction and the distance piThe direction of the nearest trajectory line.
Figure BDA0001150616040000054
Figure BDA0001150616040000055
Figure BDA0001150616040000056
Figure BDA0001150616040000057
(xi,yi,zi),(xj,yj,zj) Are respectively a point piAnd pjIs determined by the three-dimensional coordinates of (a),
Figure BDA0001150616040000058
is pointing at piAnd pjThe euclidean distance of (c). Here, the
Figure BDA0001150616040000059
Indicates if the point piAnd pjIf the allocated labels are consistent, the cost is zero, otherwise, the cost is
Figure BDA0001150616040000061
Where σ is2Refers to a set of points PbThe spatial resolution of (a). The result of using the graph cut algorithm to find the minimum value of the above energy formula is to divide the boundary points into two types, one is the road boundary points, and the other is the non-road boundary points.
Compared with the prior art, the scheme of the invention has the following advantages:
(1) the method can be directly operated on large-scale three-dimensional point cloud, and provides a set of rapid, effective and automatic solution for extracting and positioning the road boundary. The parameters which need to be set manually are very few, and the subjective intervention of human is reduced. Compared with the prior art, the method adopts the voxel segmentation and the minimum energy algorithm based on the graph segmentation, can still effectively extract the road boundary under the complex urban environment condition, overcomes the defects of point cloud data shielding, uneven density distribution and the like due to the combined use of vehicle-mounted system trajectory data for calculation, enables the result to be stable and robust, has universality for different scenes, and is easy to practically apply.
(2) The method fully excavates the basic attributes (spatial distance, geometric properties and intensity information) of the point cloud, carries out hyper-voxel segmentation on the point cloud, removes non-ground points and improves the subsequent calculation efficiency. Due to the method of sequencing and preferentially selecting the seed points, the boundary information is well preserved in the super-voxel segmentation result, and the robustness of the subsequent road boundary extraction algorithm is improved.
(3) The method is characterized in that an energy minimization algorithm based on graph cut is provided for the first time, a graph cut model is established by utilizing trajectory line data provided by a vehicle-mounted system and combining the internal characteristics of the road boundary, and the road boundary is effectively and quickly extracted.
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FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 illustrates raw point cloud data according to an embodiment of the present invention;
figure 3 shows the effect after treatment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Examples
The specific implementation of the method for extracting the road boundary based on the vehicle-mounted laser scanning point cloud data provided by the invention is as follows (the flow of the general technical scheme can be shown in figure 1):
s1, screening seed points for the obtained whole three-dimensional point cloud data set P to perform voxel division (the original point cloud data in this embodiment can be shown in fig. 2);
the hyper-voxel division refers to gathering adjacent points with consistent properties into a hyper-point so as to reduce the complexity of data processing;
s11 solving fitting plane Tp(pi)：
For each input point P of the entire three-dimensional point cloud dataset PiIts tangent plane Tp (p)i) Can be represented as a point o from its centeriSum normal vector nlFormed doublets, i.e.
Figure BDA0001150616040000071
Any point p to Tp (p) in three-dimensional spacei) Can be expressed as
Figure BDA0001150616040000072
Note piK neighbors of (a) are set of Nbk(pi) The best fit plane in the least squares sense can be obtained by solving the following equation
Figure BDA0001150616040000073
The fitted plane is then optimized using an iterative reweighted least squares method
Figure BDA0001150616040000074
Solving the least square equation of the band weight to obtain the optimized fitting plane Tp (p)i)
Figure BDA0001150616040000075
To plane Tp (p)i) The above process is repeated until the algorithm converges.
S12, removing non-ground points:
note that the final component tangent plane Tp (p)i) Three eigenvalues of the covariance matrix of the point set of (a) are λ1,λ2And λ3And satisfy lambda1≥λ2≥λ3. Then point piSmoothness of(s) (p)i) Can be expressed as
Figure BDA0001150616040000081
The following two constraints are used to remove non-ground points:
A. removing points significantly above the road surface (z)i≥5m)(ziIs a point piHeight value of (d);
B. and removing the point of which the included angle between the normal vector and the Z axis is more than 22.5 degrees.
S13, calculating the hyper-voxel fi：
Thus, the point set P after the non-ground point is removedgAccording to the smoothness ranking of each point, the point with high smoothness is selected as a seed point. The hyper-voxels are computed in such a way that region growing starts from the seed point. Let voxel fiFormalized definition as a set of points P to which there belongsiCenter point oiAnd the normal vector nlFormed triplet
Figure BDA0001150616040000082
Seed for each seed pointiLet its initial hyper-voxel fiIs { p }iCenter point and normal vector are Tp (p) respectivelyi).oiAnd Tp (p)i).nl. Then, the principle of width priority is adopted to fiA region growing is performed. For each candidate point pjIf (1) p is satisfiedjTo piIs less than a threshold value Rseed(ii) a (2) Vector Tp (p)j).nlAnd Tp (p)i).nlThe included angle of the angle is less than 22.5 degrees; (3) p is a radical ofjTo Tp (p)i) Is less than a threshold e; then p will bejIs added to fiIs concentrated. When f isiWhen no longer expandable, according to fi.piFitting the plane using least squares, and fitting fi.nlg is updated as the normal vector of the fitted plane. And (3) assigning points to the hyper-voxels by adopting local K-means clustering on the basis of the initial facets, and ensuring that the distance from each point to the hyper-voxel to which the point belongs is smaller than the distance from each point to other hyper-voxels. The distance function here is defined as:
Figure BDA0001150616040000083
wherein Ds,DnAnd DiRespectively euclidean distance, normal vector distance and intensity distance. Omegas,ωnAnd ωiRespectively, the corresponding weights.
S2, extracting boundary points between adjacent non-coplanar hyper-voxels by using a α -shape algorithm;
after the point cloud is segmented into superpixels, for each superpixel, an α -shape algorithm can be used to extract boundary points, and meanwhile, the boundary points between two superpixels which are coplanar with each other are removed, namely, if the included angle of normal vectors of the two superpixels is less than 22.5 degrees, the boundary points between the two superpixels are deletedbIncluding road boundary points and non-road boundary points.
The α -shape algorithm can be viewed as an extension of the closure Convex Hull, which can compute a finer closure by adjusting the α parameters to describe roughly the shape of a group of points in a plane or space, specifically, a pair of points is nested with a circle of a certain fixed radius, and when a pair of points just falls on the circle and no other point is contained in the circle, the two points are the boundary points of the shape.
S3, extracting road boundary points by using an energy minimization algorithm based on graph cut;
road boundary points are next extracted using a graph cut based energy minimization algorithm. The vehicle-mounted laser scanning system provides vehicle running track line data, and the track line data is observed to be basically consistent with the position direction of a measured road. The trajectory data is thus used as an initial observation model for the graph cut algorithm. The energy formula is defined as:
E(f)＝Edata(f)+λ·Esmooth(f)
Figure BDA0001150616040000091
Figure BDA0001150616040000092
Figure BDA0001150616040000093
where P isbRefers to the set of boundary points extracted in step 2. n is piThe potential of the point set of the belonging superpixel. Δ djIs pointing at pjTo a straight line
Figure BDA0001150616040000094
The distance of (c). Δ diIs pointing at piAll points to straight lines in the neighborhood
Figure BDA0001150616040000095
Average redundancy of (2). Sigma1Mean redundancy of all points. Straight line
Figure BDA0001150616040000096
Is defined as passing through point piAnd the direction and the distance piThe direction of the nearest trajectory line.
Figure BDA0001150616040000097
Figure BDA0001150616040000098
Figure BDA0001150616040000101
Figure BDA0001150616040000102
(xi,yi,zi),(xj,yj,zj) Are respectively a point piAnd pjIs determined by the three-dimensional coordinates of (a),
Figure BDA0001150616040000103
is pointing at piAnd pjThe euclidean distance of (c). Here, the
Figure BDA0001150616040000104
Indicates if the point piAnd pjIf the allocated labels are consistent, the cost is zero, otherwise, the cost is
Figure BDA0001150616040000105
Where σ is2Refers to a set of points PbThe spatial resolution of (a). The result of using the graph cut algorithm to find the minimum value of the above energy formula is to divide the boundary points into two types, one is the road boundary points, and the other is the non-road boundary points.
S4, removing outliers based on an Euclidean distance clustering algorithm;
and clustering the obtained road boundary points by using an Euclidean distance clustering algorithm, and deleting the category with small point number, namely deleting the category if the number of the points contained in one category is less than 5 after clustering.
And S5, fitting the extracted road boundary points into a smooth curve.
The remaining classes are fitted to smooth curves, respectively, thereby obtaining the road boundaries. Here, Cubic Spline Interpolation (Cubic Spline Interpolation) is used to fit the line.
Fig. 3 is an effect diagram after the processing, showing the extracted road.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (5)

1. A three-dimensional point cloud road boundary automatic extraction method is characterized by comprising the following steps: the method comprises the following steps:
s1, screening seed points of the obtained whole three-dimensional point cloud data set P for superpixel division;
s2, extracting boundary points between adjacent non-coplanar hyper-voxels by using a α -shape algorithm;
s3, extracting road boundary points by using an energy minimization algorithm based on graph cut;
s4, removing outliers based on an Euclidean distance clustering algorithm;
s5, fitting the road boundary points with the outliers removed into a smooth curve;
the process of dividing the hyper-voxels in step S1 includes the following steps:
s11 solving fitting plane Tp(Pi) Wherein P isiFor each input point of the entire three-dimensional point cloud dataset P;
s12, removing non-ground points;
s13, calculating the hyper-voxel fi；
And, solving the fitting plane Tp(Pi) The method comprises the following specific steps:
for each input point P of the entire three-dimensional point cloud dataset PiTangent plane T thereofp(Pi) Can be represented as a point o from its centeriSum normal vector nlThe binary set of components, namely:
Figure FDA0002349358200000011
wherein,
Figure FDA0002349358200000012
is a normal vector nlIs determined by the estimated value of (c),
any point p to T in three-dimensional spacep(Pi) The distance of (d) can be expressed as:
Figure FDA0002349358200000013
note PiK neighbors of (a) are set of NbK(Pi) The best fit plane in the least squares sense can be obtained by solving the following equation:
Figure FDA0002349358200000014
the fitted plane is then optimized using an iterative reweighted least squares method:
Figure FDA0002349358200000021
obtaining the optimized fitting plane T by solving the weighted least square equationp(Pi)：
Figure FDA0002349358200000022
For the optimized fitting plane Tp(Pi) The above solving process is repeated until the algorithm for solving the weighted least squares equation converges.
2. The method for automatically extracting the road boundary by using the three-dimensional point cloud as claimed in claim 1, wherein the step S2 of extracting the boundary points between the adjacent non-coplanar hyper-voxels by using α -shape algorithm comprises the following specific steps:
after the point cloud is segmented into superpixels, for each superpixel, the α -shape algorithm may be used to extract boundary points while removing edges between two superpixels that are coplanar with each otherBoundary points, i.e. if the normal vector angle of two hyper-voxels is less than 22.5 °, the boundary point between the two hyper-voxels is deleted, the boundary point P being the point at this timebIncluding road boundary points and non-road boundary points.
3. The method for automatically extracting the road boundary by the three-dimensional point cloud according to claim 1, wherein the method comprises the following steps:
s12, removing the non-ground points, the concrete steps are as follows:
finally form fitting plane Tp(Pi) Three eigenvalues of the covariance matrix of the point set of (a) are λ1，λ2And λ3And satisfy lambda1≥λ2≥λ3Then point PiSmoothness of (S) (P)i) Can be expressed as:
Figure FDA0002349358200000023
the following two constraints are used to remove non-ground points:
A. removing points significantly above the road surface, i.e. ziA point of not less than 5m, ziIs a point PiA height value of (d);
B. and removing the point of which the included angle between the normal vector and the Z axis is more than 22.5 degrees.
4. The method for automatically extracting the road boundary by the three-dimensional point cloud according to claim 2, wherein the method comprises the following steps:
s13, calculating the hyper-voxel fiThe method comprises the following specific steps:
removing the point set P after the non-ground pointgSorting according to the smoothness of each point, firstly selecting the point with the smoothness larger than a preset value as a seed point, and calculating the hyper-voxels in a mode of starting region growing from the seed point; let voxel fiFormalized definition as a point PiCenter point oiAnd the normal vector nlFormed triplet
Figure FDA0002349358200000031
Seed for each seed pointiLet its initial hyper-voxel fiIs { P }iThe center point and normal vector are T respectivelyp(Pi).oiAnd Tp(Pi).nl(ii) a Then, the principle of width priority is adopted to fiPerforming region growing on each candidate point PjIf (1) P is satisfied at the same timejTo PiIs less than a threshold value Rseed(ii) a (2) Vector Tp(Pj).nlAnd Tp(Pi).nlThe included angle of the angle is less than 22.5 degrees; (3) pjTo Tp(Pi) Is less than a threshold e; then P will bejIs added to fiWhen f is concentratediWhen the expansion can not be performed any more, according to the triple fiP in (1)iThis term fi.PiFitting the plane using least squares and fitting the triplet fiIn (1)
Figure FDA0002349358200000032
This item
Figure FDA0002349358200000033
Updating the normal vector of the fitting plane; at all f calculatediOn the basis of the triple, points are assigned to the hyper-voxels by adopting local K-means clustering, and the distance from each point to the hyper-voxel to which the point belongs is ensured to be smaller than the distances to other hyper-voxels, wherein a distance function is defined as:
Figure FDA0002349358200000034
wherein Ds，DnAnd DiRespectively Euclidean distance, normal vector distance and intensity distance, omegas，ωnAnd ωiThe weights are corresponding to the three distance values, respectively.
5. The method for automatically extracting the three-dimensional point cloud road boundary according to claim 4, wherein the method comprises the following steps: step S3 extracts road boundary points using an energy minimization algorithm based on graph cut, as follows:
the method comprises the steps of taking vehicle running track line data provided by a vehicle-mounted laser scanning system as an initial observation model, dividing boundary points into the following two categories { 'road boundary points', 'non-road boundary points' } by using a graph cut algorithm, namely, solving a classification function f to assign a label f to each point by using the graph cut algorithmpE.g., L is a category set { "road boundary points", "non-road boundary points" }, so that the cost of payment is minimal, i.e., the energy formula is minimized,
the energy formula is defined here as:
E(f)＝Edata(f)+λ·Esmooth(f)
here Edata(f) I.e. the data item in the energy formula, refers to the error of the comparison of the classification result with the initial observation model, which is the cost of assigning a label to each point in the classification process, Esmooth(f) That is, the smooth term in the energy formula refers to the degree of non-smoothness of the classification function f, specifically, the cost of inconsistency of classification results between each point and neighboring points in the classification process, and λ is the smooth term Esmooth(f) Is set to 32, here empirically, wherein,
Figure FDA0002349358200000041
Figure FDA0002349358200000042
Figure FDA0002349358200000043
where P isbRefers to the set of boundary points extracted in step S2, n is PiPotential of point set of the supervoxel to which it belongs, Δ djIs pointing at PjTo a straight line
Figure FDA0002349358200000044
A distance of Δ diIs pointing at PiAll points to straight lines in the neighborhood
Figure FDA0002349358200000045
Average redundancy of, σ1Is a set of points PgMean redundant, straight line of all points in
Figure FDA00023493582000000414
Is defined as passing through point PiAnd the direction vector and the distance PiThe nearest trajectory line is in the same direction,
Figure FDA0002349358200000046
Figure FDA0002349358200000047
Figure FDA0002349358200000048
Figure FDA0002349358200000049
Figure FDA00023493582000000410
is PiThe label of (a) is used,
Figure FDA00023493582000000411
is Pj(ii) a label of (x)i，yi，zi)，(xj，yj，zj) Are respectively a point PiAnd PjIs determined by the three-dimensional coordinates of (a),
Figure FDA00023493582000000412
is pointing at PiAnd PjEuclidean distance ofFrom here, here
Figure FDA00023493582000000413
Indicates if the point P isiAnd PjIf the allocated labels are consistent, the cost is zero, otherwise, the cost is B { P }i，Pj}，
Where σ is2Refers to a set of points PbThe spatial resolution of (2) is obtained by using a graph cut algorithm to obtain the minimum value of the energy formula, namely, the boundary points are divided into two types, one type is road boundary points, and the other type is non-road boundary points.
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