An Open-Source LiDAR and Monocular Off-Road Autonomous Navigation Stack

• URL: http://arxiv.org/abs/2604.03096v1
• Date: Fri, 03 Apr 2026 15:19:20 GMT
• Title: An Open-Source LiDAR and Monocular Off-Road Autonomous Navigation Stack
• Abstract summary: We present an open-source navigation stack supporting both LiDAR and monocular 3D perception without task-specific training.<n>For the monocular setup, we combine zero-shot depth prediction with metric depth rescaling using sparse SLAM measurements.<n>The resulting point cloud is used to generate a robot-centric 2.5D elevation map for costmap-based planning.
• Score: 35.094251140860756
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Off-road autonomous navigation demands reliable 3D perception for robust obstacle detection in challenging unstructured terrain. While LiDAR is accurate, it is costly and power-intensive. Monocular depth estimation using foundation models offers a lightweight alternative, but its integration into outdoor navigation stacks remains underexplored. We present an open-source off-road navigation stack supporting both LiDAR and monocular 3D perception without task-specific training. For the monocular setup, we combine zero-shot depth prediction (Depth Anything V2) with metric depth rescaling using sparse SLAM measurements (VINS-Mono). Two key enhancements improve robustness: edge-masking to reduce obstacle hallucination and temporal smoothing to mitigate the impact of SLAM instability. The resulting point cloud is used to generate a robot-centric 2.5D elevation map for costmap-based planning. Evaluated in photorealistic simulations (Isaac Sim) and real-world unstructured environments, the monocular configuration matches high-resolution LiDAR performance in most scenarios, demonstrating that foundation-model-based monocular depth estimation is a viable LiDAR alternative for robust off-road navigation. By open-sourcing the navigation stack and the simulation environment, we provide a complete pipeline for off-road navigation as well as a reproducible benchmark. Code available at https://github.com/LARIAD/Offroad-Nav.

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