3D road boundary extraction based on mobile laser scanning point clouds and OSM data

doi:10.13474/j.cnki.11-2246.2022.0197

Abstract

Abstract: Accurate road boundary extraction modeling is an important topic in urban road management, intelligent traffic planning and high-precision map generation. In this paper, an accurate 3D road boundary extraction method based on OSM is proposed based on mobile laser scanning point clouds data. Firstly, the original mobile LiDAR point cloud data is processed by CSF filtering to separate the ground points, and the candidate data set of road boundary points is obtained by combining with the relative elevation analysis. Then, the nodes of OSM vector road network data are used to assist the data segmentation of road boundary point candidate point set. Finally, a 3D road boundary point set is obtained based on RANSAC algorithm in each segment point cloud data set. Through the extraction experiment of three different types of urban road boundary sections, the analysis results show that, the accuracy rate and recall rate of the proposed method are 96.12% and 95.17%, respectively, and F1 value is 92.11%. The research method in this paper can be used to extract and vectorize high-precision road boundaries, thus provide support for intelligent transportation and unmanned navigation.

Key words: mobile LiDAR point cloud, OSM data, CSF fltering, RANSAC, road boundary

CLC Number:

P237

WANG Yanjun, LIN Yunhao, WANG Shuhan, LI Shaochun, WANG Mengjie. 3D road boundary extraction based on mobile laser scanning point clouds and OSM data[J]. Bulletin of Surveying and Mapping, 2022, 0(7): 18-25.

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