分散圧縮を用いた資源制約下の協調モンテカルロ自己位置推定
Resource-Aware Collaborative Monte Carlo Localization with Distribution Compression
計算・通信制約下で複数ロボットが協調して大域的自己位置推定を行う手法を提案し、分布圧縮により通信量と計算コストを削減、実機・シミュレーションで検証した。
著者: Nicky Zimmerman, Alessandro Giusti, Jérôme Guzzi
分類: cs.RO
原文アブストラクト
Global localization is essential in enabling robot autonomy, and collaborative localization is key for multi-robot systems. In this paper, we address the task of collaborative global localization under computational and communication constraints. We propose a method which reduces the amount of information exchanged and the computational cost. We also analyze, implement and open-source seminal approaches, which we believe to be a valuable contribution to the community. We exploit techniques for distribution compression in near-linear time, with error guarantees. We evaluate our approach and the implemented baselines on multiple challenging scenarios, simulated and real-world. Our approach can run online on an onboard computer. We release an open-source C++/ROS2 implementation of our approach, as well as the baselines
