A variational approach to online road and path segmentation with monocular vision

In this paper we present an online approach to segmenting roads on large scale trajectories using only a monocular camera mounted on a car. We differ from popular 2D segmentation solutions which use single colour images and machine learning algorithms that require supervised training on huge image databases. Instead, we propose a novel approach that fuses 3D geometric data with appearance-based segmentation of 2D information in an automatic system. Our contribution is twofold: first, we propagate labels from frame to frame using depth priors of the segmented road avoiding user interaction most of the time; second, we transfer the segmented road labels to 3D laser point clouds. This reduces the complexity of state-of-the-art segmentation algorithms running on 3D Lidar data. Segmentation fails is in only 3% of the cases over a sequence of 13,600 monocular images spanning an urban trajectory of more than 10km.