Traversable path identification in unstructured terrains: A Markov random walk approach

Many terrains in outdoor robot navigation problems have paths that are distinct and continuous compared to the non-traversable regions. In image space these paths correspond to continuous segments that can be thought of as clusters embedded in image feature space. These segments very often translate directly to traversable ground plane. In this paper we build the intuition for semi-supervised methods in path identification and present a Markov random walk based approach that requires very few labeled points. The method creates a nearest neighbor graph representation of the current image frame using features deemed suitable for the task and propagates labels based on the concept of absorbing Markov chains. We extend this formalism to the task of dynamically identifying traversable and non-traversable regions in the incoming image frames. We present results on actual terrains corresponding to test courses used by the LAGR test team. The results demonstrate that with minimal initial supervision the robot can navigate to the goal. We also conduct comparisons of our path labeling technique against other machine learning techniques including nonlinear support vector machines on hand labeled data. The results demonstrate that our semi-supervised approach is proficient in the domain of path traversal in unstructured domains.