一時的協調知覚のための視覚基盤モデルを用いた通信効率的な相対姿勢推定
Communication-Efficient Relative Pose Estimation with Vision Foundation Models for Ephemeral Collaborative Perception
ロボット間の短時間の遭遇時に、限られた通信帯域と視野重複の欠如に対応するため、視覚基盤モデルを活用して自己運動とロボット間の相対姿勢を効率的に推定するフレームワークを提案した。
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著者: Qihang Li, Jo-Hao Huang, Jiewen Liu, Suyoung Kang, Hao Zhang, Peng Gao
分類: cs.RO
原文アブストラクト
Relative pose estimation is a fundamental capability for collaborative perception and coordination in multi-robot systems. However, robots encountering each other in real-world environments often operate in short interaction windows and must operate under limited communication bandwidth with intermittent or missing visual overlap caused by occlusions or limited fields of view. Existing approaches typically rely on global reference frames, assume sustained view overlap, or incur prohibitive communication costs, thereby limiting their applicability to ephemeral collaborative perception. To address these challenges, we introduce communication-efficient relative pose estimation (CERPE), a system-level framework that coordinates vision foundation models to jointly estimate ego-motion and inter-robot relative pose. CERPE reduces unnecessary raw-observation exchange by using continuously shared fixed-size descriptors to gate event-triggered raw-image requests independently of pose estimation. Non-overlapping encounters are handled by propagating inter-robot relative poses through metrically scaled ego-motion, thus maintaining relative pose estimates even in the absence of visual overlap. Experiments in simulation and real-world robots show that CERPE improves 6-DoF relative pose estimation over selected baselines in ephemeral collaborative perception.
