基于树干几何特征实现森林中高精度6自由度定位
TreeLoc: 6-DoF LiDAR Global Localization in Forests via Inter-Tree Geometric Matching
- 用树干轴线和胸径构建全局场景表示，通过分布直方图粗匹配
- 结合2D三角形描述子实现细粒度匹配，定位误差低于0.5米
- 适用于长期森林监测，适合机器人导航与生态管理应用
可靠定位对森林导航至关重要，因GPS信号常受干扰，且LiDAR数据重复、遮挡严重、结构复杂，传统依赖独特结构特征的城市定位方法在此失效。为此，我们提出TreeLoc——一种面向森林的激光雷达全局定位框架，支持场景识别与6自由度位姿估计。该方法以树干轴线及胸径（DBH）为基本单元，通过轴线对齐构建统一参考系，并用树分布直方图（TDH）进行粗匹配，再利用2D三角形描述子实现精细匹配，最终通过两步几何验证完成位姿估计。在多个森林数据集上，TreeLoc优于现有基线，定位精度达亚米级。消融实验验证了各模块有效性。此外，我们提出基于紧凑全球树数据库描述子的长期森林管理应用。代码已开源：https://github.com/minwoo0611/TreeLoc。
原文摘要 · Abstract (English)
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.
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