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.

翻译：可靠的定位对于森林环境中的导航至关重要，因为GPS信号在此类环境中常出现衰减，而LiDAR测量数据则具有重复性、易受遮挡且结构复杂的特点。这些条件削弱了传统以城市为中心定位方法的基本假设——即一致特征源于独特的结构模式，因此需要开发以森林环境为中心的解决方案以实现鲁棒性。为应对这些挑战，本文提出TreeLoc，一种基于LiDAR的森林全局定位框架，能够处理地点识别与6自由度位姿估计。我们使用树干及其胸高直径（DBH）表征场景，通过树干轴线将数据对齐至公共参考坐标系，并利用树木分布直方图（TDH）进行粗匹配，随后采用二维三角形描述符进行精细匹配。最终，通过两步几何验证实现位姿估计。在多样化的森林基准测试中，TreeLoc性能优于基线方法，实现了精确的定位。消融实验验证了各模块的有效性。我们还提出了利用紧凑型全球树木数据库描述符进行长期森林管理的应用方案。TreeLoc已通过https://github.com/minwoo0611/TreeLoc开源，供机器人学研究社区使用。