Trailblazer: Learning offroad costmaps for long range planning

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.

@article{viswanath2025_2505.09739,
  title={ Trailblazer: Learning offroad costmaps for long range planning },
  author={ Kasi Viswanath and Felix Sanchez and Timothy Overbye and Jason M. Gregory and Srikanth Saripalli },
  journal={arXiv preprint arXiv:2505.09739},
  year={ 2025 }
}