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：本文针对使用受尺寸、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及度量-语义建图的问题。以往绝大多数探索研究均假设机器人定位已解决，然而忽略智能体的状态不确定性最终会导致建图结果与智能体自身状态出现级联误差。此外，降低定位误差的行动可能与探索任务存在直接冲突。本文提出一种框架，在探索效率与降低智能体状态不确定性的行动之间取得平衡。具体而言，我们基于原始问题数据中提取的稀疏信息构建主动度量-语义SLAM算法，使其适用于SWaP受限机器人。进一步，该框架被集成至全自主空中机器人系统中，实现杂乱三维环境下的自主探索。通过大量实际实验证明，引入语义闭环检测（SLC）可将机器人位姿估计的平移误差降低90%以上、偏航角误差降低约75%，同时使位姿估计与语义地图的不确定性分别降低70%和65%以上。尽管本文以室内多楼层探索为背景进行讨论，该系统还可用于基础设施检测、精密农业等其他GPS信号不可靠的应用场景。