Learning Traversability for Long Horizon Off-Road Navigation
The work proposes a method to learn a continuous map of terrain traversability from overhead data like satellite imagery and LiDAR. This map is trained using human-driven GPS routes and refined by geometric information from LiDAR. The resulting system allows autonomous vehicles to plan safe, long-distance off-road paths by understanding terrain features beyond immediate sensor range.