AUTOMATIC EXTRACTION OF ROAD SURFACE AND CURBSTONE EDGES FROM MOBILE LASER SCANNING DATA

Abstract

We present a procedure for automatic extraction of the road surface from geo-referenced mobile laser scanning data. The basic assumption of the procedure is that the road surface is smooth and limited by curbstones. Two variants of jump detection are investigated for detecting curbstone edges, one based on height differences the other one based on histograms of the height data. Region growing algorithms are proposed which use the irregular laser point cloud. Two- and four-neighbourhood growing strategies utilize the two height criteria for examining the neighborhood. Both height criteria rely on an assumption about the minimum height of a low curbstone. Road boundaries with lower or no jumps will not stop the region growing process. In contrast to this objects on the road can terminate the process. Therefore further processing such as bridging gaps between detected road boundary points and the removal of wrongly detected curbstone edges is necessary. Road boundaries are finally approximated by splines. Experiments are carried out with a ca. 2 km network of smalls streets located in the neighbourhood of University of Applied Sciences in Stuttgart. For accuracy assessment of the extracted road surfaces, ground truth measurements are digitized manually from the laser scanner data. For completeness and correctness of the region growing result values between 92% and 95% are achieved.