Probabilistic Inference Based Traversability Analysis for Autonomous Robot in Complex Environments
Yang LIU, Kazushige YAMAMOTO, Saburo TAKAHASHI, Toshihisa ABE · Transactions of the Society of Instrument and Control Engineers · 2022
Autonomous navigation on unknown uneven terrain needs a reliable traversability map that indicates potential navigation hazards such as slipping down from a slope, colliding with an obstacle, etc. This paper focuses on generating a real-time traversability map using a 3D LiDAR. The proposed method leverages a probabilistic inference model to update the terrain map, detect static obstacles, and remove moving objects simultaneously. A robust travelability map is created by considering both uneven terrain conditions and obstacles. Our experiments demonstrate its suitability for real-time navigation over a variety type of real-world environments.