Real Time Obstacle Detection on Non Flat Road Geometry through `V-Disparity' Representation

Raphael Labayrade, Didier Aubert and Jean-Philippe Tarel

Abstract

Many roads are not totaly planar and often present hills and valleys because of environment topography. Nevertheless, the majority of existing techniques for road obstacle detection assumes that the road is planar. This can cause several issues : imprecision as regards the real position of obstacles as well as false obstacle detection or obstacle detection failures. In order to increase the reliability of the obstacle detection process, this paper proposes an original, fast and robust method for detecting the obstacles without using the flat-earth geometry assumption; this method is able to cope with uphill and downhill gradients as well as dynamic pitching of the vehicle. Our approach is based on the construction and investigation of the "v-disparity" image which provides a good representation of the geometric content of the road scene. The advantage of this image is that it provides semi-global matching and is able to perform robust obstacle detection even in the case of partial occlusion. Furthermore, this detection is performed without any explicit extraction of coherent structures such as road edges or lane-markings in the stereo image pair. This paper begins by reminding the main properties of the "v-disparity" image. On the basis of this image, we then describe a robust method for road obstacle detection in the context of flat and non flat road geometry, including estimation of the relative height and pitching of the stereo sensor with respect to the road surface. The longitudinal profile of the road is estimated and the objects located above the road surface are then extracted as potential obstacles; subsequently, the accurate detection of road obstacles, in particular the position of tyre-road contact points is computed in a precise manner. The whole process is performed at frame rate with a current-day PC. Our experimental findings and comparisons with the results obtained using a flat geometry hypothesis show the benefits of our approach. Future work will be concerned with the construction of a 3D road model and the stereo vision-based estimation of the Euler angles (roll, yaw) of the vehicle.

Reference

@inproceedings{jpt-iv02, author = {Labayrade, R. and Aubert, D. and Tarel, J.-P.}, title = {Real Time Obstacle Detection on Non Flat Road Geometry through `V-Disparity' Representation}, booktitle = {Proceedings of IEEE Intelligent Vehicle Symposium}, date = {June 18-20}, address = {Versailles, France}, volume = {2}, pages = {646--651}, year = {2002}, note = {http://perso.lcpc.fr/tarel.jean-philippe/publis/iv02.html} }