Learning Traversability-Aware Global Planners for Long Horizon Off-Road Navigation

arXiv:2607.23743v1 Announce Type: new Abstract: Autonomous navigation across large off-road environments remains a challenging problem. Onboard sensors perceive only the immediate surroundings, yet safe and efficient routes depend on terrain features that extend well beyond the sensor horizon. Geo-spatial data sources such as satellite imagery, aerial LiDAR, and vector maps can close this gap, but learning traversability from them is difficult: dense labels are unavailable at scale, and existing methods rely on short-range sensing. We propose an efficient formulation that learns a continuous traversability map from overhead data, supervised directly by human-driven GPS trajectories and shaped by self-supervised geometric priors from LiDAR. Alongside the model, we release a public dataset of 299 scenes spanning sim!1{,}244,km^{2} of diverse terrain, paired with 1{,}130,km of human driving. In field trials on a Clearpath Warthog across seven routes at two sites, our method achieves trajectories within 3.66% of human path length and reduces operator interventions by sim!85% compared to local-planner-only autonomy.

Source: arXiv cs.RO | 2026-07-28