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.

中文翻译：

---

使用 Urquhart Tessellations 在森林中进行地点识别

在这封信中，我们提出了一个基于 Urquhart 细分的新颖描述符，该细分源自森林中树木的位置。我们提出了一个框架，该框架使用这些描述符来检测先前看到的观察结果和地标对应，即使存在部分重叠和噪声。我们在来自松树林中无人驾驶飞行器 (UAV) 的不同飞行的模拟和现实世界数据地图合并中运行闭环检测实验，并表明我们的方法在准确性和鲁棒性方面优于最先进的方法.