Conference item
TreeLoc: 6-DoF LiDAR global localization in forests via inter-tree geometric matching
- Abstract:
- Reliable localization is crucial for navigation in forests, where GPS is often degraded and LiDAR measurements are repetitive, occluded, and structurally complex. These conditions weaken the assumptions of traditional urban-centric localization methods, which assume that consistent features arise from unique structural patterns, necessitating forest-centric solutions to achieve robustness in these environments. To address these challenges, we propose TreeLoc, a LiDAR-based global localization framework for forests that handles place recognition and 6-DoF pose estimation. We represent scenes using tree stems and their diameter at breast height (DBH), which are aligned to a common reference frame via their axes and summarized using the tree distribution histogram (TDH) for coarse matching, followed by fine matching with a 2D triangle descriptor. Finally, pose estimation is achieved through a two-step geometric verification. On diverse forest benchmarks, TreeLoc outperforms baselines, achieving precise localization. Ablation studies validate the contribution of each component. We also propose applications for long-term forest management using descriptors from a compact global tree database. TreeLoc is open-sourced for the robotics community at https://github.com/minwoo0611/TreeLoc.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 2.2MB, Terms of use)
-
- Publisher copy:
- 10.1109/icra57385.2026.11696235
Authors
+ UK Research and Innovation
More from this funder
- Funder identifier:
- https://ror.org/001aqnf71
- Grant:
- 10037847
- Publisher:
- IEEE
- Host title:
- 2026 IEEE International Conference on Robotics and Automation: IEEE ICRA 2026 Proceedings
- Pages:
- 12667-12674
- Publication date:
- 2026-06-01
- Acceptance date:
- 2026-01-31
- Event title:
- 2026 IEEE International Conference on Robotics and Automation (ICRA)
- Event location:
- Vienna, Austria
- Event website:
- https://2026.ieee-icra.org/
- Event start date:
- 2026-06-01
- Event end date:
- 2026-06-05
- DOI:
- EISBN:
- 9798331581602
- ISBN:
- 979331581619
- Language:
-
English
- Keywords:
- Pubs id:
-
2494404
- Local pid:
-
pubs:2494404
- Deposit date:
-
2026-11-07
- ARK identifier:
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2026
- Rights statement:
- ©2026 by IEEE
- Notes:
- The author accepted manuscript (AAM) of this paper has been made available under the University of Oxford's Open Access Publications Policy, and a CC BY public copyright licence has been applied.
- Licence:
- CC Attribution (CC BY)
If you are the owner of this record, you can report an update to it here: Report update to this record