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 good 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，利用经济高效的硬件实现具备前瞻性的机器人导航，需要一种轻量级且可靠的控制器。因此，无需显式目标跟踪即可从传感器读数中推断场景动态，是实现行人环境前瞻性导航的关键。本文提出了一种基于二维激光雷达传感器读数的增强导航时空注意力流程。该流程辅以一种新型激光雷达状态表示方法，该方法优先突出动态障碍物而非静态障碍物。随后，注意力机制可在空间和时间维度上实现选择性场景感知，从而提升动态场景下的整体导航性能。我们在不同场景和模拟器中对该方法进行了全面评估，发现其能良好泛化至未知环境。结果表明，该方法相比现有先进技术表现出卓越性能，从而使得学习到的控制器能够无缝部署于真实机器人上。