TreeLoc: 森林における樹木間幾何マッチングによる6自由度LiDARグローバル位置推定
TreeLoc: 6-DoF LiDAR Global Localization in Forests via Inter-Tree Geometric Matching
森林環境でLiDARを用いて樹木の幹と胸高直径を手がかりに場所認識と6自由度姿勢推定を行うグローバル位置推定フレームワークを提案し、多様な森林ベンチマークで高精度を達成した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Minwoo Jung, Nived Chebrolu, Lucas Carvalho de Lima, Haedam Oh, Maurice Fallon, Ayoung Kim
分類: cs.RO, cs.CV
原文アブストラクト
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.
