  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.
