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.

翻译：可靠的定位对于自主机器人高效安全地导航至关重要。部分导航方法能够规划具有高可定位性（即获取可靠定位能力）的路径。通过遵循这些路径，机器人可获取有助于定位算法实现更精确位置估计结果的传感器数据流。然而，大多数现有方法需要先验知识，且难以适应未见过场景或动态变化。为克服这些局限，我们提出一种新颖方法——在动态人类环境中通过深度强化学习实现可定位性增强的导航。所提出的规划器能够自动从二维激光数据中提取有利于定位的几何特征。该规划器学习为不同几何特征分配重要性权重，并引导机器人穿越有利于激光定位的区域。为促进规划器的学习，我们提出两项技术：(1) 增强状态表征——考虑动态变化与定位结果置信度，为机器人提供更多信息以做出更优决策；(2) 奖励度量——能够针对影响定位精度的行为提供稀疏与密集反馈。实验表明，在未见环境中测试时，我们的方法在丢失率和到达率方面均有显著提升。