Road Boundary, Curb and Surface Extraction from 3D Mobile LiDAR Point Clouds in Urban Environment

Abstract

According to the spatial structure characteristics of road curbs and road surfaces, a robust method for automatic extraction of road boundaries, road curbs and road surfaces was proposed using mobile laser scanning (MLS) point cloud data. Firstly, ground filtering was performed to separate ground points and non-ground points according to the angle between the normal vector of the point cloud and the direction vector of the z-axis. Secondly, based on the vertical and linear features of the road curb, the MLS trajectory points were used to extract road curb and road boundary points. Then, Euclidean clustering and fitting were performed on the road boundary point segments. Adjacent clusters were merged, and sparse points were densified. In addition, based on the principle that road surfaces are within road boundaries, road surface points were obtained in scanning line order. Two MLS point clouds with different resolutions and road roughness were tested. Compared with the manually calibrated reference road curb, the extraction completenesses of the road curb from the two datasets were 95.66% and 96.45%, respectively, and the extraction correctnesses of the road curb were 96.34% and 99.10%, respectively, with both qualities over 92%. The algorithm can effectively extract straight or curved road boundaries and road curbs from the point cloud data containing vehicles, pedestrians and obstacle occlusions in an urban environment, and is applicable to MLS point cloud data with different resolutions and roughness.