Safe and Smart Robotics

Overcoming Nature: Perception for Autonomous Navigation in Dense Vegetation

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Abstract

Autonomous navigation in densely vegetated off-road environments remains challenging because conventional geometric perception often treats traversable vegetation as non-traversable obstacles. In this work, we present a modular semantic–geometric perception pipeline for vegetation-aware navigation. The approach combines camera-based semantic data with LiDAR to generate a local grid map containing geometric and semantic information. A subsequent filtering stage uses this representation to correct vegetation-induced artifacts in standard elevation maps while preserving rigid obstacles for navigation. The system is designed to be portable across multiple robot platforms and sensor configurations. The pipeline was evaluated in challenging alpine off-road environments on three robot platforms, indicating improved distinction between traversable vegetation and solid obstacles and supporting more reliable navigation in dense natural environments.

How to Cite:

Wimmer, L., Koczka, A., Petrovic, U. & Steinbauer-Wagner, G., (2026) “Overcoming Nature: Perception for Autonomous Navigation in Dense Vegetation”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision 3(1), 231-235.