Deep Reinforcement Learning for Localizability-Enhanced Navigation in Dynamic Human Environments

TL;DR

This paper introduces a deep reinforcement learning-based navigation method that enhances localizability in dynamic human environments, enabling robots to navigate more reliably by focusing on features that improve localization accuracy.

Contribution

The paper proposes a novel deep RL approach that automatically extracts and prioritizes geometric features for localization, adapting to dynamic environments without prior knowledge.

Findings

Significant reduction in lost rate in unseen environments

Improved arrival rate compared to baseline methods

Enhanced localization accuracy through learned feature importance

Abstract

Reliable localization is crucial for autonomous robots to navigate efficiently and safely. Some navigation methods can plan paths with high localizability (which describes the capability of acquiring reliable localization). By following these paths, the robot can access the sensor streams that facilitate more accurate location estimation results by the localization algorithms. However, most of these methods require prior knowledge and struggle to adapt to unseen scenarios or dynamic changes. To overcome these limitations, we propose a novel approach for localizability-enhanced navigation via deep reinforcement learning in dynamic human environments. Our proposed planner automatically extracts geometric features from 2D laser data that are helpful for localization. The planner learns to assign different importance to the geometric features and encourages the robot to navigate through areas that are helpful for laser localization. To facilitate the learning of the planner, we suggest two techniques: (1) an augmented state representation that considers the dynamic changes and the confidence of the localization results, which provides more information and allows the robot to make better decisions, (2) a reward metric that is capable to offer both sparse and dense feedback on behaviors that affect localization accuracy. Our method exhibits significant improvements in lost rate and arrival rate when tested in previously unseen environments.