Visual Campus Road Detection for an UGV using Fast Scene Segmentation and Rapid Vanishing Point Estimation

Vision-based road detection plays a key role for Unmanned Ground Vehicles (UGVs) working in an unknown outdoor environment. The estimation of the vanishing point is a practical solution for general road detection using monocular vision, which however is not good enough for robust road detection in a campus environment due to the strong noises of texture orientations generated from roadside trees and buildings. In this paper, a novel system framework is proposed by combining the fast scene segmentation (FSS) and the rapid vanishing point detection. The proposed FSS algorithm can segment a single image into road and non-road regions based on the similarity analysis of color histogram, which can eliminate the inherent noises in the trees and buildings and improve the robustness of road detection effectively. Before voting for the vanishing point, we use Canny algorithm to extract the edges in the road region roughly segmented in FSS step. Since most of the strong texture orientations exist in the extracted edges, the computational complexity in the voting stage can be reduced significantly. Experimental results implemented on a real UGV platform show the validity and robustness of the proposed approach.