Nardari, V, Guilherme
Chen, Steven W.
Romero, Roseli A. F.
Número total de Autores: 7
Afiliação do(s) autor(es):
 Nardari, Guilherme, V, Univ Penn, GRASP Lab, Philadelphia, PA 19104 - USA
 Treeswift, Philadelphia, PA 19104 - USA
 Nardari, Guilherme, V, Univ Sao Paulo, Robot Learning Lab, BR-13566590 Sao Carlos, SP - Brazil
Número total de Afiliações: 3
Tipo de documento:
IEEE ROBOTICS AND AUTOMATION LETTERS;
Citações Web of Science:
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. (AU)