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.

翻译：在动态室内环境中，使用低成本硬件实现前瞻性机器人导航需要采用轻量级且可靠的控制器。因此，从传感器读数中推断场景动态（无需显式目标跟踪）是行人导航中前瞻性导航的关键环节。本文提出一种基于二维激光雷达传感器读数的时空注意力管道，用于增强导航性能。该管道辅以新型激光雷达状态表示方法，可优先关注动态障碍物而非静态障碍物。随后，注意力机制通过空间与时间维度的选择性场景感知，显著提升了动态场景中的整体导航性能。我们在不同场景与仿真器中对该方法进行了全面评估，发现其对未见环境具有卓越的泛化能力。实验结果表明，该方法相较于现有最优方法展现出显著优势，从而可实现在真实机器人上无缝部署所学控制器。