  In this letter, we present a novel descriptor based on Urquhart tessellations
derived from the position of trees in a forest. We propose a framework that
uses these descriptors to detect previously seen observations and landmark
correspondences, even with partial overlap and noise. We run loop closure
detection experiments in simulation and real-world data map-merging from
different flights of an Unmanned Aerial Vehicle (UAV) in a pine tree forest and
show that our method outperforms state-of-the-art approaches in accuracy and
robustness.
