TreeLoc++: Robust 6-DoF LiDAR Localization in Forests with a Compact Digital Forest Inventory

2026-03-03

AI Summary

This work addresses the challenge of achieving long-term, reliable, and point-cloud-sparse 6-DoF LiDAR localization in forest environments by proposing TreeLoc++. It introduces compact digital forest inventory (DFI) as a discriminative map representation, replacing raw point clouds with geometric attributes of tree trunks. The method integrates coarse retrieval based on tree layout, diameter-at-breast-height (DBH) filtering, yaw-consistent inlier selection, and tree-geometry-constrained optimization to effectively suppress mismatches in structurally ambiguous regions and jointly refine roll, pitch, and height estimates. Evaluated across 27 sequences spanning four sites in three countries over a total trajectory length of 7.98 km, TreeLoc++ achieves centimeter-level accuracy using only 250 KB of map data and demonstrates robustness over a two-year period, significantly outperforming point-cloud-dependent baseline approaches.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• 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

Abstract

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++ reduces false matches in structurally ambiguous forests and improves the reliability of full 6-DoF pose estimation. It augments coarse retrieval with a pairwise distance histogram that encodes local tree-layout context, subsequently refining candidates via DBH-based filtering and yaw-consistent inlier selection to further 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 250KB of map data and outperforms both hand-crafted and learning-based baselines that rely on point cloud maps. This demonstrates the scalability of TreeLoc++ for long-term deployment.

Problem

• LiDAR localization
• Digital Forest Inventory
• 6-DoF pose estimation
• forest robotics
• long-term autonomy

Innovation

• Digital Forest Inventory
• 6-DoF LiDAR Localization
• Tree-based Mapping
• Long-term Robustness
• Compact Representation