TreeLoc++: robust six-DoF LiDAR localization in forests with a compact digital forest inventory

Journal article

Reliable localization is essential for sustainable forest management, as it allows robots or sensor systems to revisit and monitor the status of individual trees over long periods. In modern forestry, this management is structured around Digital Forest Inventories (DFIs), which encode stems using compact geometric attributes rather than raw data. Despite their central role, DFIs have been overlooked in localization research, and most methods still rely on dense gigabyte-sized point clouds that are costly to store and maintain. To improve upon this, we propose TreeLoc++, a global localization framework that operates directly on DFIs as a discriminative representation, eliminating the need to use the raw point clouds. TreeLoc++ substantially strengthens the existing TreeLoc framework by reducing false matches in structurally ambiguous forests and improving the reliability of full 6-DoF pose estimation. Building upon TreeLoc, it augments coarse retrieval with a pairwise distance histogram that encodes local tree-layout context, subsequently refining candidates via tree diameter at breast height (DBH)-based filtering and yaw-consistent inlier selection to reduce mismatches. Furthermore, a constrained optimization leveraging tree geometry jointly estimates roll, pitch, and height, enhancing pose stability and enabling accurate localization without reliance on dense 3D point cloud data. Evaluations on 27 sequences recorded in forests across three datasets and four countries show that TreeLoc++ achieves precise localization with centimeter-level accuracy. We further demonstrate robustness to long-term change by localizing data recorded in 2025 against inventories built from 2023 data, spanning a two-year interval. The system represents 15 sessions spanning 7.98 km of trajectories using only 250 KB of map data and outperforms both hand-crafted and learning-based baselines that rely on dense point cloud maps. This demonstrates the scalability of TreeLoc++ for long-term deployment. TreeLoc++ is open-sourced at https://github.com/minwoo0611/TreeLoc-plusplus.

Authors: Jung, M; Lee, D; Chebrolu, N; Oh, H; Fallon, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Journal: IEEE Transactions on Field Robotics More from this journal
• Volume: 3
• Pages: 724-746
• Publication date: 2026-07-06
• Acceptance date: 2026-06-29
• DOI: 10.1109/tfr.2026.3710448
• EISSN: 2997-1101
• Language: English
• Keywords: pose estimation; robots; vegetation; grounding; trajectory; laser radar; forests; location awareness; trees (botanical); environmental monitoring; clouds; global localization; place recognition; 6-DOF