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导航至关重要，因为GPS信号在此类环境中常会衰减，而激光雷达测量则具有重复性、遮挡性和结构复杂性。这些条件削弱了传统以城市为中心的定位方法所依赖的假设——即一致特征源于独特的结构模式，因此需要针对森林环境设计专门的解决方案以实现鲁棒性。为应对这些挑战，我们提出了TreeLoc，一种基于激光雷达的森林全局定位框架，能够处理地点识别和六自由度姿态估计。我们使用树干及其胸径（DBH）来表示场景，通过树干轴线将其对齐到公共参考系，并利用树木分布直方图（TDH）进行粗匹配，随后使用二维三角形描述符进行精细匹配。最后，通过两步几何验证实现姿态估计。在多样化的森林基准测试中，TreeLoc优于基线方法，实现了精确的定位。消融研究验证了各组件的贡献。我们还提出了利用紧凑的全球树木数据库中的描述符进行长期森林管理的应用方案。TreeLoc已面向机器人学界开源，代码发布于 https://github.com/minwoo0611/TreeLoc。