A Self-Supervised Near-to-Far Approach for Terrain-Adaptive Off-Road Autonomous Driving

We introduce a self-supervised method for systematically choosing traversable terrain while autonomously navigating a vehicle to a goal position in an unknown off-road environment. Leveraging the color discriminant bias of off-road terrain types, and using images from a vehicle-mounted camera, we employ a viewpoint transformation that maintains the spatial layout of the terrain to cluster terrain types by color and register corresponding traversability features to guide future navigation decisions. As it navigates, our algorithm also generates training images for use in contemporary end-to-end navigation schemes. Our test results demonstrate the advantages of our approach over classical near-to-far approaches in off-road environments with unknown traversability characteristics, and highlight its fit to supervised semantic segmentation schemes that require foreknowledge of traversability characteristics for labeling, which are limited by insufficient data and suffer pixel-level class imbalance. We detail the techniques for clustering, feature registration, path planning and navigation; and demonstrate the method. Finally, we study the effectiveness of non-discretionary self-supervised data labeling.