IGV-RRT: 変化する環境における能動的物体探索のための事前・リアルタイム観測融合
IGV-RRT: Prior-Real-Time Observation Fusion for Active Object Search in Changing Environments
時間的に変化する屋内環境で物体を探すため、3Dシーングラフの事前知識とVLMによるリアルタイム観測を融合し、情報利得と意味的手がかりを考慮したRRTプランナで探索する手法を提案。
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: Wei Zhang, Ping Gong, Yujie Wang, Leilei Yao, Minghui Bai, Rongfeng Ye, Yinchuan Wang, Yachao Wang, Chen Sun, Chaoqun Wang
分類: cs.RO
原文アブストラクト
Object Goal Navigation (ObjectNav) in temporally changing indoor environments is challenging because object relocation can invalidate historical scene knowledge. To address this issue, we propose a probabilistic planning framework that combines uncertainty-aware scene priors with online target relevance estimates derived from a Vision Language Model (VLM). The framework contains a dual-layer semantic mapping module and a real-time planner. The mapping module includes an Information Gain Map (IGM) built from a 3D scene graph (3DSG) during prior exploration to model object co-occurrence relations and provide global guidance on likely target regions. It also maintains a VLM score map (VLM-SM) that fuses confidence-weighted semantic observations into the map for local validation of the current scene. Based on these two cues, we develop a planner that jointly exploits information gain and semantic evidence for online decision making. The planner biases tree expansion toward semantically salient regions with high prior likelihood and strong online relevance (IGV-RRT), while preserving kinematic feasibility through gradient-based analysis. Simulation and real-world experiments demonstrate that the proposed method effectively mitigates the impact of object rearrangement, achieving higher search efficiency and success rates than representative baselines in complex indoor environments.
関連論文
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- 大規模物体関係事前知識を用いたゼロショット物体探索物体探索ナビゲーション
- 屋内物体探索のための汎化可能な接近方策学習物体探索ナビゲーション
- 能動的物体知覚者：物体探索のための認識誘導型方策学習物体探索ナビゲーション
