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

2026-02-02

AI Summary

This work addresses the challenge of global localization in forest environments, where GPS signals are often unavailable and LiDAR data suffer from occlusions and structural ambiguities that degrade conventional localization methods. To overcome these limitations, we propose TreeLoc, the first forest-wide global localization approach centered on the geometric structure of trees. TreeLoc represents scenes using tree trunks and their diameters at breast height (DBH), performs coarse matching via a Tree Distribution Histogram (TDH), and refines alignment using a 2D triangle-based descriptor, followed by a two-stage geometric verification to estimate full 6-DoF poses. Evaluated on multiple forest benchmark datasets, TreeLoc significantly outperforms existing methods. Ablation studies confirm the contribution of each component, and the framework supports long-term forest management through integration with a compact global tree database. The code is publicly available.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Local Search
• Computer Vision: 3D Computer Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• LiDAR localization
• forest navigation
• 6-DoF pose estimation
• global localization
• tree-based mapping

Innovation

• TreeLoc
• LiDAR localization
• forest navigation
• tree stem representation
• 6-DoF pose estimation