時空間アテンションによる動的環境でのLiDARベースロボットナビゲーションの強化
Spatiotemporal Attention Enhances Lidar-Based Robot Navigation in Dynamic Environments
2D LiDARの読み取りから時空間アテンションを用いて動的障害物を強調する新表現を導入し、歩行者がいる動的環境での先読みナビゲーション性能を向上させた。
著者: Jorge de Heuvel, Xiangyu Zeng, Weixian Shi, Tharun Sethuraman, Maren Bennewitz
分類: cs.RO
原文アブストラクト
Foresighted robot navigation in dynamic indoor environments with cost-efficient hardware necessitates the use of a lightweight yet dependable controller. So inferring the scene dynamics from sensor readings without explicit object tracking is a pivotal aspect of foresighted navigation among pedestrians. In this paper, we introduce a spatiotemporal attention pipeline for enhanced navigation based on 2D~lidar sensor readings. This pipeline is complemented by a novel lidar-state representation that emphasizes dynamic obstacles over static ones. Subsequently, the attention mechanism enables selective scene perception across both space and time, resulting in improved overall navigation performance within dynamic scenarios. We thoroughly evaluated the approach in different scenarios and simulators, finding excellent generalization to unseen environments. The results demonstrate outstanding performance compared to state-of-the-art methods, thereby enabling the seamless deployment of the learned controller on a real robot.
