TreeLoc: 6-DoF LiDAR Global Localization in Forests Via Inter-Tree Geometric Matching
Minwoo Jung, Nived Chebrolu, Lucas Carvalho de Lima, Haedam Oh, Maurice Fallon, Ayoung Kim
AI summary
Problem
Reliable global localization in forests is hindered by degraded GPS, repetitive LiDAR scans, and seasonal foliage changes that break traditional urban-centric methods. Existing forest-specific approaches often rely on computationally heavy learning models or dense point clouds, making them impractical for long-term, resource-constrained deployment.
Approach
TreeLoc extracts individual tree stems and their diameter at breast height (DBH) from LiDAR scans, then matches scenes using a two-stage pipeline: a coarse Tree Distribution Histogram (TDH) for fast candidate retrieval followed by a fine 2D triangle descriptor for geometric verification, enabling direct 6-DoF pose estimation.
Key results
- Outperforms state-of-the-art algorithmic and learning-based baselines across multiple forest benchmarks
- Achieves real-time localization within 50 ms per query
- Reduces storage requirements by 3-4 orders of magnitude compared to dense point cloud methods
- Enables precise multi-session alignment across datasets captured years apart
Why it matters
Provides a lightweight, interpretable, and highly accurate localization framework essential for forestry robotics, autonomous navigation, and long-term forest inventory management in GPS-denied environments.
Abstract
Reliable localization is crucial for navigation in forests, where GPS is often degraded and LiDAR measure- ments 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.