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arXiv · 2604.03096

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

Rémi Marsal·Quentin Picard·Adrien Poiré·Sébastien Kerbourc'h·Thibault Toralba·Clément Yver·Alexandre Chapoutot·David Filliat
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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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Rémi Marsal, Quentin Picard, Adrien Poiré, Sébastien Kerbourc'h, Thibault Toralba, Clément Yver, Alexandre Chapoutot, David Filliat. 2026-04-03. An Open-Source LiDAR and Monocular Off-Road Autonomous Navigation Stack. https://arxiv.org/abs/2604.03096

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