SEAM: 軌道変形下での生涯LiDARマッピングのためのサブマップアンカー証拠
SEAM: Submap-Anchored Evidence for Lifelong LiDAR Mapping under Trajectory Deformation
サブマップ単位のアンカーで軌道を最適化し、動的物体除去と変化検出を行う生涯LiDARマッピング手法を提案。軌道が後から修正されても証拠を再利用でき、信頼度に基づくループエッジ抑制と方向別ボクセル証拠モデルで高精度・高速化を実現した。
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著者: Kyuwon Kim
分類: cs.RO
原文アブストラクト
We propose SEAM, a LiDAR-based lifelong mapping framework. Instead of relying on a single anchor spanning the entire session, SEAM generates evidence based on a trajectory optimized with submap-level anchors, and performs dynamic object removal and change detection. Through submap-level reprojection, the generated evidence remains usable even if the trajectory is subsequently modified by a new session, eliminating the need to recompute the entire process from scratch. SEAM suppresses geometrically unreliable inter-session loop edges using a DOP-based confidence measure. Suppressing unreliable loop edges prevents alignment errors. SEAM also uses a directional voxel-wise evidence model. The model accounts for occupancy patterns that vary with ray direction. Direction-aware evidence separates dynamic objects from environmental changes more precisely. Experiments on a real construction-site dataset and a long-term multi-session dataset show that SEAM achieves higher accuracy and faster processing than existing methods.