Place Recognition in Forests with Urquhart Tessellations
This addresses the challenge of reliable navigation and mapping for UAVs in forest environments, representing an incremental improvement over existing methods.
The paper tackles the problem of place recognition in forests by introducing a novel descriptor based on Urquhart tessellations from tree positions, and it shows that this method outperforms state-of-the-art approaches in accuracy and robustness in loop closure detection experiments with UAV data.
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.