Search and Rescue under the Forest Canopy using Multiple UAVs
This addresses search and rescue operations in challenging forest environments for emergency responders, representing an incremental advance in multi-robot systems.
The paper tackles the problem of GPS-denied search and rescue in forests using multiple UAVs, focusing on overcoming perceptual aliasing for reliable loop closure detection and map fusion, with results showing improved precision and recall in data associations validated in simulation and real-world missions.
We present a multi-robot system for GPS-denied search and rescue under the forest canopy. Forests are particularly challenging environments for collaborative exploration and mapping, in large part due to the existence of severe perceptual aliasing which hinders reliable loop closure detection for mutual localization and map fusion. Our proposed system features unmanned aerial vehicles (UAVs) that perform onboard sensing, estimation, and planning. When communication is available, each UAV transmits compressed tree-based submaps to a central ground station for collaborative simultaneous localization and mapping (CSLAM). To overcome high measurement noise and perceptual aliasing, we use the local configuration of a group of trees as a distinctive feature for robust loop closure detection. Furthermore, we propose a novel procedure based on cycle consistent multiway matching to recover from incorrect pairwise data associations. The returned global data association is guaranteed to be cycle consistent, and is shown to improve both precision and recall compared to the input pairwise associations. The proposed multi-UAV system is validated both in simulation and during real-world collaborative exploration missions at NASA Langley Research Center.