Automated Extraction of Road Geometry Information Using Mobile LiDAR Point Cloud

doi:10.12141/j.issn.1000-565X.220459

Abstract

Abstract:

To efficiently collect and digitally model road facility information, this paper constructed a method framework for automatic extraction of road geometry information by using vehicle-mounted laser point cloud data. For the disorder and redundancy of laser data, grid drop sampling and radius filtering were used to simplify the size of the point cloud and remove noise points. The point cloud is organized and indexed by grid cell division, and the spatial locality of the point cloud is rationally utilized to reduce the scale of operation. Using the hierarchy of road elements on elevation and the continuity and smoothness of pavement structure, elevation filtering, local normal vector filtering based on the principal component analysis framework, and DBSCAN clustering methods were designed to achieve accurate segmentation from the original point cloud to the pavement point cloud. The road direction was obtained by collecting vehicle trajectory information, and the road cross section was divided by its direction vector and normal vector. The cross-section was cut and projected onto a two-dimensional plane, and the road width and horizontal and horizontal parameters were extracted by sliding window and least square algorithm. By comparing the extraction algorithm with the manual measurement results, in the two experimental data sets of complex blocks and suburban roads, the accuracy of point cloud segmentation is more than 87%, the integrity is more than 97%, and the extraction quality is more than 86%. The average relative error of geometric information is small, indicating that the algorithm has good extraction quality. Under the condition of finite computation, the processing time of two data centralized point clouds is 6.864 and 10.078 s/km, respectively, and the extraction time of geometric information is 1.732 and 0.843 s/km, respectively. The proposed method can give a good balance between extraction efficiency and accuracy, and has good applicability in complex blocks and suburban highway environments. It can provide a reference for the health assessment and three-dimensional reconstruction of road facilities.

Key words: road engineering, mobile LiDAR, road extraction, point cloud segmentation, cluster analysis, road geometry information

CLC Number:

TN958.98

YU Bin, ZHANG Yuqin, WANG Yuchen, et al. Automated Extraction of Road Geometry Information Using Mobile LiDAR Point Cloud[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(2): 88-99.

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实验数据	道路环境	平面范围/m²	点个数/10⁴	路线长度/km
数据集1	郊区公路	1 710×2 693	1 000	4.5
数据集2	复杂街区	535×1 149	781	0.6

实验数据	准确性p/%	完整性r/%	提取质量q/%
数据集1	87.49	98.95	86.67
数据集2	95.15	97.37	92.77

桩号	算法提取的路宽/m	手动提取的路宽/m	相对误差
10	16.492	16.954	0.027
20	17.775	17.888	0.006
30	12.457	13.260	0.061
40	16.883	13.556	0.245
50	15.293	16.048	0.047
60	17.025	16.028	0.062
70	16.599	14.581	0.138

桩号	算法提取的坡度/%	手动提取的坡度/%	相对误差
10	-1.120	-1.375	0.185
30	-0.387	-1.015	0.107
50	-1.135	13.260	0.135
70	0.733	0.767	0.048
90	0.269	0.222	0.215
110	1.011	0.944	0.070
130	-1.226	-1.667	0.264

实验数据	预处理/s	路面提取/s	几何信息计算/s	总时间/s	平均时间/（s·km^-1）
数据集1	3.230	19.863	7.795	30.888	6.864
数据集2	0.303	5.238	0.506	6.047	10.078