TreeLoc: 6-DoF LiDAR Global Localization in Forests via Inter-Tree Geometric Matching

TL;DR

TreeLoc is a novel LiDAR-based localization method for forests that uses inter-tree geometric matching to achieve accurate 6-DoF pose estimation, overcoming challenges like occlusion and repetitive structures.

Contribution

It introduces a forest-specific localization framework using tree-based scene representation and geometric matching, outperforming existing methods in complex forest environments.

Findings

TreeLoc achieves higher localization accuracy than baselines.

The method is robust to occlusion and structural complexity.

Ablation studies confirm the effectiveness of each component.

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.