HSGNav: 物体移動を伴うマルチスケール生涯ナビゲーションのための仮説シーングラフ
HSGNav: Hypothesis Scene Graphs for Multi-Scale Lifelong Navigation under Object Relocation
家具などの永続的な3D物体と、卓上小物の仮説的な表面物体を分離して管理する仮説シーングラフ(HSG)を提案し、物体の移動や多スケール探索に対応する生涯ナビゲーション手法HSGNavを実現した。
詳しい要約
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著者: Kaiyao Liao, Qingfeng Li, Chen Chen, Qing Sun, Ningbo Gu, Xiaoze Wu, Haochen Zhao, Ying Li, Jianwei Niu
分類: cs.RO, cs.CV
原文アブストラクト
In real homes, assistive robots must repeatedly locate targets from large furniture to tiny tabletop items, as people move objects between household surfaces. Uniformly instantiating detections at different scales as 3D objects can be brittle for small on-surface items; without explicit object-state validation, stale entries may persist after scene changes. Our key insight is to separate persistent 3D furniture entities from revisable surface-object hypotheses, retaining their visual evidence without requiring independent object geometry. We formalize this design as the Hypothesis Scene Graph (HSG), which preserves small-object evidence and supports local revision without rebuilding the stable entity topology. Building on HSG, HSGNav uses this separation for multi-scale lifelong navigation: retrieval selects entity or hypothesis evidence according to target scale, exploration acquires additional surface observations, and verification confirms target presence or invalidates contradicted hypothesis records. Because existing lifelong benchmarks such as GOAT-Bench rarely require mid-episode memory invalidation or multi-scale search, we introduce FAROS-Bench (Find And Re-find Objects across Scales) for mid-episode relocation of surface objects and multi-scale find goals. Experiments show that HSGNav performs competitively on GOAT-Bench while substantially outperforming prior methods on FAROS-Bench.