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

• URL: http://arxiv.org/abs/2607.23743v1
• Date: Sun, 26 Jul 2026 16:36:35 GMT
• Title: Learning Traversability-Aware Global Planners for Long Horizon Off-Road Navigation
• Abstract summary: We propose an efficient formulation that learns a continuous traversability map from overhead data.<n>We release a public dataset of 299 scenes spanning $sim!1,244,mathrmkm2$ of diverse terrain, paired with $1,130,mathrmkm$ of human driving.<n>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-
• Score: 4.184845027588593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 $\sim\!1{,}244\,\mathrm{km}^{2}$ of diverse terrain, paired with $1{,}130\,\mathrm{km}$ 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.

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