  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.
