Matrix analysis
Autonomous Robotic Vehicle Road Following
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Journal of Field Robotics - Special Issue on LAGR Program, Part II
Semi-Supervised Learning
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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 non-linear 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.