Curvature-based curb detection method in urban environments using stereo and laser

This paper addresses the problem of curb detection for ADAS or autonomous navigation in urban scenarios. The algorithm is based on clouds of 3D points. It is evaluated using 3D information from a pair of stereo cameras and a LIDAR. Curbs are detected based on road surface curvature. The curvature estimation requires a dense point cloud, therefore the density of the LIDAR cloud has been augmented using Iterative Closest Point (ICP) based on the previous scans. The proposed algorithm can deal with curbs of different curvature and heights, from as low as 3 cm, in a range up to 20 m (whenever that curbs are connected in the curvature image). The curb parameters are modeled using straight lines and compared to the ground-truth using the lateral error as the key parameter indicator. The ground-truth sequences were manually labeled on urban images from the KITTI dataset and made publicly available for the scientific community.