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arXiv · 2505.09739

Trailblazer: Learning offroad costmaps for long range planning

Kasi Viswanath·Felix Sanchez·Timothy Overbye·Jason M. Gregory·Srikanth Saripalli
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Abstract

Autonomous navigation in off-road environments remains a significant challenge in field robotics, particularly for Unmanned Ground Vehicles (UGVs) tasked with search and rescue, exploration, and surveillance. Effective long-range planning relies on the integration of onboard perception systems with prior environmental knowledge, such as satellite imagery and LiDAR data. This work introduces Trailblazer, a novel framework that automates the conversion of multi-modal sensor data into costmaps, enabling efficient path planning without manual tuning. Unlike traditional approaches, Trailblazer leverages imitation learning and a differentiable A* planner to learn costmaps directly from expert demonstrations, enhancing adaptability across diverse terrains. The proposed methodology was validated through extensive real-world testing, achieving robust performance in dynamic and complex environments, demonstrating Trailblazer's potential for scalable, efficient autonomous navigation.

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Kasi Viswanath, Felix Sanchez, Timothy Overbye, Jason M. Gregory, Srikanth Saripalli. 2025-05-14. Trailblazer: Learning offroad costmaps for long range planning. https://arxiv.org/abs/2505.09739

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