物理情報に基づくスライディングウィンドウ粒子フィルタによる触覚のみを用いた手中6自由度物体姿勢推定の精緻化
Physics-Informed Sliding-Window Particle Filtering for Tactile-Only In-Hand 6-DoF Object Pose Refinement
視覚が使えない状況で、手のひらの触覚センサ情報のみを用いて、把持物体の6自由度姿勢を高精度に推定する手法を提案した論文。物理的な接触条件を考慮した粒子フィルタと時間的な情報統合により、従来法より精度が向上することを示した。
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著者: Lingjun Shao, Ying Zhang, Xiangfei Li, Xiangyang Li, Huan Zhao, Zhenyu Wang, Han Ding
分類: cs.RO
原文アブストラクト
This paper studies tactile-only 6-DoF pose refinement and belief maintenance for grasped objects in static and short quasi-static in-hand configurations where vision is unavailable or heavily occluded. The key difficulty is tactile partial observability: whole-hand taxel contacts are sparse, intermittent, and ambiguous under limited excitation and object symmetries. We propose a physics-informed particle filter on $\mathrm{SE}(3)$ that updates pose beliefs from dense whole-hand tactile measurements. The likelihood combines active-contact signed-distance consistency, force-normal alignment, friction-cone feasibility, zero-force negative evidence, and optional feasibility guards. A sliding-window log-likelihood fuses recent tactile frames to reduce single-frame ambiguity, while a potential-field-guided proposal steers particles away from hand--object penetration. Symmetry-aware resampling preserves multiple plausible modes. Experiments on an Allegro Hand V5 with five objects show lower normalized ADD-S than tactile-only geometric, particle-filter, and learning baselines, and ablations confirm the benefits of temporal fusion, potential guidance, and mode preservation.