A line-based approach for precise extraction of road and curb region from mobile mapping data
Keywords: MMS, point cloud, planar structure, boundary, uneven distribution density, region growing, curbstone
Abstract. Planar structure detection from point clouds is important process in many applications such as maintenance of infrastructure facility including roads and curbs because most artificial structures consists of planar surfaces. The Mobile Mapping System can obtain a large amount of points with traveling at a standard speed. However, in the case that the high-end laser scanning system is equipped, the distribution density of points is uneven. In the point-based method, this situation causes the problem to the method of calculating geometric information using neighborhood points. In this paper, we propose a line-based region growing method in order to detect planar structures with precise boundary from point clouds with uneven distribution density of points. The precise boundary of a planar structure is maintained by appropriately creating line segments from the input clouds. We adapt the definition of neighborhood and the estimation of the normal vector to the line-based region growing. The evaluation by comparing our result with manually extracted points shows that more than 98% of curb points are detected. And, about 90% of the boundary points between a road and a curb are detected with less than 0.005 meters of the distance error.