帯域適応型時空間対応付けによる協調知覚
Bandwidth-Adaptive Spatiotemporal Correspondence Identification for Collaborative Perception
通信帯域が動的に変化する環境で、複数ロボットが同じ物体を一貫して認識できるよう、時空間観測を段階的に選択・共有する手法を提案した。
著者: Peng Gao, Williard Joshua Jose, Hao Zhang
分類: cs.RO
原文アブストラクト
Correspondence identification (CoID) is an essential capability in multi-robot collaborative perception, which enables a group of robots to consistently refer to the same objects within their respective fields of view. In real-world applications, such as connected autonomous driving, vehicles face challenges in directly sharing raw observations due to limited communication bandwidth. In order to address this challenge, we propose a novel approach for bandwidth-adaptive spatiotemporal CoID in collaborative perception. This approach allows robots to progressively select partial spatiotemporal observations and share with others, while adapting to communication constraints that dynamically change over time. We evaluate our approach across various scenarios in connected autonomous driving simulations. Experimental results validate that our approach enables CoID and adapts to dynamic communication bandwidth changes. In addition, our approach achieves 8%-56% overall improvements in terms of covisible object retrieval for CoID and data sharing efficiency, which outperforms previous techniques and achieves the state-of-the-art performance. More information is available at: https://gaopeng5.github.io/acoid.
