Cross-country obstacle detection: Space-variant resolution and outliers removal

The unstructured nature of cross-country environments poses a great deal of challenges to obstacle detection algorithms. This paper presents an algorithm able to perform obstacle detection with small computational cycles, and extremely robust against outliers. To reduce the computational cost, foveation, i.e. space-variant image resolution adaptation, is employed. To reduce sensitivity to outliers, two voting filters are presented. Experimental results on two different stereo vision heads show that the proposed algorithm is able to perform at 10 Hz. Even in the presence of a low signal-to-noise ratio, caused by the use of a partially damaged lens, the algorithm is able to distinguish obstacles from the background.