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：本论文针对尺寸、重量与功率受限的空中机器人在GPS缺失的多楼层室内环境中的探索及度量-语义地图构建问题展开研究。以往大多数探索研究工作假设机器人定位问题已解决，但忽略智能体状态不确定性最终会导致地图结果与智能体自身状态产生级联误差。此外，降低定位误差的行为可能与探索任务直接冲突。我们提出一个能平衡探索效率与降低智能体状态不确定性行为的框架。具体而言，我们的主动度量-语义SLAM算法方法基于从原始问题数据中提取的稀疏信息构建，使其适用于尺寸、重量与功率受限的机器人。同时，我们将该框架集成到全自主空中机器人系统中，实现在杂乱三维环境中的自主探索。通过大量真实世界实验表明，通过引入语义闭环检测，可将机器人位姿估计误差在平移方向降低超过90%、偏航角降低约75%，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展开论述，但该系统同样适用于其他应用场景，例如基础设施巡检和缺乏可靠GPS数据的精准农业领域。