  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.
