Visual curb localization for autonomous navigation

Percept-referenced commanding is an attractive paradigm for autonomous navigation over long distances. Rather than relying on precise prior environment maps and self-localization, this approach uses high-level primitives that refer to environmental features. The assumption is that the sensing and processing system onboard the robot should be able to extract such features reliably. In this context, we present an algorithm for the visual-based localization of curbs in an urban scenario. This represents a basic sensing capability that would enable behaviors such as "follow this road keeping at a certain distance from its edge". Our approach to curb localization relies on both photometry and range information (as obtained by stereopsis). Candidate image points are first selected using a combination of cues, and then input to a weighted Hough transform, which determines the line (or lines) that are most likely to belong to the curb\´s edge. Our experimental results show that, as long as the range data has acceptable quality, our system performs very robustly in real-world situations.