EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy

arXiv:2311.06234v2 Announce Type: replace-cross
Abstract: Traversing terrain with good traction is crucial for achieving fast off-road navigation. Instead of manually designing costs based on terrain features, existing methods learn terrain properties directly from data via self-supervision to automatically penalize trajectories moving through undesirable terrain, but challenges remain to properly quantify and mitigate the risk due to uncertainty in learned models. To this end, this work proposes a unified framework to learn uncertainty-aware traction model and plan risk-aware trajectories. For uncertainty …

abstract arxiv autonomy challenges costs cs.lg cs.ro cs.sy data designing eess.sy features good learn moving navigation risk supervision through type via