🤖 AI Summary
This work addresses the challenge of autonomous exploration and mapping for size-, weight-, and power-constrained (SWaP-limited) UAVs in multi-floor, GPS-denied indoor environments. We propose a metric-semantic joint active SLAM framework. Our key contributions are: (1) the first integration of semantic loop closure (SLC) into an active SLAM policy, enabling synergistic optimization between exploration behavior and pose uncertainty reduction; and (2) a lightweight algorithm based on sparse information abstraction, specifically designed to comply with onboard computational constraints. The system jointly performs metric mapping, semantic recognition, loop closure correction, and online exploration decision-making. Experimental results demonstrate median translational and yaw errors reduced by 90% and 75%, respectively, while pose uncertainty and semantic map uncertainty decrease by 70% and 65%. These improvements significantly enhance both mapping accuracy and exploration efficiency in complex indoor settings.
📝 Abstract
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 develop 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.
