Lidar-based urban road detection by histograms of normalized inverse depths and line scanning

In this paper, we propose to fuse the geometric information of a 3D Lidar and a monocular camera to detect the urban road region ahead of an autonomous vehicle. Our method takes advantage of both the high definition of 3D Lidar data and the continuity of road in image representation. First, we obtain an efficient representation of Lidar data, an organized 2D inverse depth map, by projecting the spatially unorganized 3D Lidar points onto the camera's image plane. The approximate road regions can be quickly estimated by extracting vertical and horizontal histograms of the normalized inverse depths. To accurately find the road area, a row and column scanning strategy is applied in the approximate road region. We have carried out experiments on the public KITTI-Road benchmark, and achieve one of the best performance among the Lidar-based road detection methods without learning procedure.