In this letter, we address the problem of exploration and metric-semantic mapping of multi-floor GPS-denied indoor environments using Size Weight and Power (SWaP) constrained aerial robots. Most previous work in exploration assumes that robot localization is solved. However, neglecting the state uncertainty of the agent can ultimately lead to cascading errors both in the resulting map and in the state of the agent itself. Furthermore, actions that reduce localization errors may be at direct odds with the exploration task. We propose a framework that balances the efficiency of exploration with actions that reduce the state uncertainty of the agent. In particular, our algorithmic approach for active metric-semantic SLAM is built upon sparse information abstracted from raw problem data, to make it suitable for SWaP-constrained robots. Furthermore, we integrate this framework within a fully autonomous aerial robotic system that achieves autonomous exploration in cluttered, 3D environments. From extensive real-world experiments, we showed that by including Semantic Loop Closure (SLC), we can reduce the robot pose estimation errors by over 90% in translation and approximately 75% in yaw, and the uncertainties in pose estimates and semantic maps by over 70% and 65%, respectively. Although discussed in the context of indoor multi-floor exploration, our system can be used for various other applications, such as infrastructure inspection and precision agriculture where reliable GPS data may not be available.

翻译：在本信中，我们研究了利用受尺寸、重量和功率（SWaP）约束的空中机器人在多楼层GPS拒止室内环境中进行探索与度量-语义建图的问题。以往的探索研究大多假设机器人定位问题已解决。然而，忽略智能体的状态不确定性最终会导致地图及智能体自身状态出现级联误差。此外，减少定位误差的动作可能与探索任务直接相悖。我们提出了一种框架，能够在探索效率与降低智能体状态不确定性的动作之间取得平衡。特别地，我们的主动度量-语义SLAM算法方法基于从原始问题数据中抽象出的稀疏信息构建，以适配受SWaP约束的机器人。此外，我们将该框架集成到全自主空中机器人系统中，实现在杂乱的三维环境中进行自主探索。通过大量真实世界实验，我们发现引入语义闭环（SLC）可将机器人位姿估计误差降低超过90%（平移）和约75%（偏航角），并将位姿估计与语义地图的不确定性分别降低超过70%和65%。尽管本文以室内多楼层探索为背景进行讨论，但我们的系统还可应用于其他场景，例如基础设施检测以及无法获得可靠GPS数据的精准农业领域。