Abstract:
Traversability mapping for autonomous navigation in unstructured environments has been widely investigated for decades. However, it remains challenging due to the uncerta...Show MoreMetadata
Abstract:
Traversability mapping for autonomous navigation in unstructured environments has been widely investigated for decades. However, it remains challenging due to the uncertainty in geometry perception and the simplified representation of traversability maps that fail to capture detailed structures of environments. We propose PTS-Map, a 2.5D probabilistic terrain state map to address these issues. PTS-Map sequentially updates the ground surface state and above-ground elevation state, explicitly distinguishing the geometric features of ground and obstacles. During state updates, we introduce a novel ground uncertainty estimation to mitigate the effects of unreliable feature measurements. By effectively designing the terrain states and addressing the geometric feature uncertainty, PTS-Map constructs a temporally consistent traversability map that provides precise ground conditions and vertical features relevant to navigation. Experiments are conducted in various large-scale unstructured environments with distinct characteristics. PTS-Map outperforms other state-of-the-art methods in success rate and efficiency by constructing a precise traversability map.
Published in: IEEE Robotics and Automation Letters ( Volume: 10, Issue: 2, February 2025)