Integrating stereo structure for omnidirectional trail following

We describe a system which follows “trails” for autonomous outdoor robot navigation. Through a combination of appearance and structural cues derived from stereo omnidirectional color cameras, the algorithm is able to detect and track rough paths despite widely varying tread material, border vegetation, and illumination conditions. The approaching trail region is modeled as a circular arc segment of constant width. Using likelihood formulations which measure color, brightness, and/or height contrast between a hypothetical region and flanking areas, the tracker performs a robust randomized search for the most likely trail region and robot pose relative to it with no a priori appearance model. The addition of the structural information, which is derived from a semi-global dense stereo algorithm with ground-plane fitting, is shown to improve trail segmentation accuracy and provide an additional layer of safety beyond solely ladar-based obstacle avoidance. Our system's ability to follow a variety of trails is demonstrated through live runs as well as analysis of offline runs on several long sequences with diverse appearance and structural characteristics using ground-truth segmentations.