Terrain Traversability Mapping Based on LiDAR and Camera Fusion
Lupeng Zhou, Jikai Wang, Shiqi Lin, Zonghai Chen
Abstract
Terrain traversability mapping plays an important role in autonomous exploration of unmanned ground vehicles. In many cases, information from a single sensor such as LiDAR or camera may not be sufficient for estimating traversability reliably. For example, LiDAR-based methods are better at identifying areas with strong structural characteristics, rather than cluttered areas, such as lawns. Vision-based methods can distinguish different regions with semantic meanings. However, sometimes there may be a misclassification due to a domain gap or other reasons, which will make it risky during the robot’s navigation process. In this work, we propose a novel LiDAR-vision-based method for terrain traversability mapping. Our method is mainly composed of three modules: vision-based traversable area segmentation, LiDAR-based traversable area extraction, and Bayesian fusion. Experimental results demonstrate that the proposed method is able to fulfill real-time and reliable traversability mapping and shows superior to the state-of-the-art method.