Extraction of urban road boundary based on vehicle-mounted LiDAR point cloud data

doi:10.13474/j.cnki.11-2246.2021.0045

Abstract

Abstract: With the rise of high-precision map industry, accurate extraction of road boundary point cloud data has become the focus of research. In this paper, urban road data acquired by vehicle-mounted LiDAR scanning system is firstly segmented according to the collection track, and each section of road point cloud is filtered. By analyzing the elevation and plane information of the point cloud, the point cloud segmentation algorithm is used to separate the road surface from the non-road surface point cloud, and then the processed road surface point cloud is projected. Finally, the boundary feature estimation extraction algorithm is used to obtain the road boundary point cloud. Through the experimental analysis of two typical road sections, the experiment shows that this method is effective in extracting the urban road boundary point cloud, with high accuracy and robustness, which can be used for reference for the line extraction of road boundary in the future.

Key words: vehicle LiDAR point cloud, filtering, RANSAC, road boundary, projection

CLC Number:

P237

XIE Hongquan, WANG Qiuling, CAI Dongjian, XING Wanli, SUN Quan, LIU Fucheng. Extraction of urban road boundary based on vehicle-mounted LiDAR point cloud data[J]. Bulletin of Surveying and Mapping, 2021, 0(2): 64-67.

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Extraction of urban road boundary based on vehicle-mounted LiDAR point cloud data

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