Spatiotemporal Attention Enhances Lidar-Based Robot Navigation in Dynamic Environments

TL;DR

This paper introduces a spatiotemporal attention framework for lidar-based robot navigation that improves dynamic obstacle handling and generalizes well across unseen environments, facilitating real-world deployment.

Contribution

It presents a novel spatiotemporal attention pipeline and lidar-state representation that enhance navigation in dynamic environments without explicit object tracking.

Findings

Outperforms state-of-the-art methods in dynamic scenarios

Generalizes effectively to unseen environments

Enables real robot deployment with improved navigation performance

Abstract

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.