A variational approach to online road and path segmentation with monocular vision
Lina Maria Paz, Pedro Piniés, Paul Newman
Abstract
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.
BibTeX
@inproceedings{icra2015_avariationalappr,
title = {A variational approach to online road and path segmentation with monocular vision},
author = {Lina Maria Paz and Pedro Piniés and Paul Newman},
booktitle = {ICRA 2015},
year = {2015}
}