期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 6, 期 2, 页码 279-286出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2020.3039217
关键词
Localization; mapping; robotics and automation in agriculture and forestry
类别
资金
- Treeswift under the NSF SBIR [193856]
- ARL [ARL DCIST CRA 2911NF-17-2-0181]
- ONR [N00014-071-0829]
- ARO [W911NF-13-1-0350, CNPq 465755/2014-3]
- FAPESP [2014/50851-0, 2018/24526-5]
- Semiconductor Research Corporation
- DARPA
A novel descriptor based on Urquhart tessellations is presented, which is derived from the position of trees in a forest. The proposed framework demonstrates superior accuracy and robustness in loop closure detection experiments for map-merging from different flights of a UAV in a pine tree forest.
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.
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