Histograms of the Normalized Inverse Depth and Line Scanning for Urban Road Detection

Abstract

In this paper, we propose to fuse the geometric information of a 3-D 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 3-D LiDAR data and the continuity of road in image representation. First, we obtain an efficient representation of LiDAR data and an organized 2-D inverse depth map, by projecting the 3-D LiDAR points onto the camera's image plane. Through the new representation, we can acquire the intermediate representations of road scenes by extracting the vertical and horizontal histograms of the normalized inverse depth. The approximate road regions can be quickly estimated with both histogram-based schemes. To accurately find the road area, we propose a row and column scanning strategy in the approximate road region to refine the detected road area. We have carried out experiments on the public KITTI-Road benchmark, and have achieved one of the best performances among the LiDAR-based road detection methods without learning procedure.