iMESA: 協調SLAMのための増分分散最適化
iMESA: Incremental Distributed Optimization for Collaborative Simultaneous Localization and Mapping
複数ロボットの協調SLAMにおいて、疎な通信のみでリアルタイムに高精度な状態推定を実現する分散バックエンドアルゴリズムiMESAを提案した。
著者: Daniel McGann, Michael Kaess
分類: cs.RO
原文アブストラクト
This paper introduces a novel incremental distributed back-end algorithm for Collaborative Simultaneous Localization and Mapping (C-SLAM). For real-world deployments, robotic teams require algorithms to compute a consistent state estimate accurately, within online runtime constraints, and with potentially limited communication. Existing centralized, decentralized, and distributed approaches to solving C-SLAM problems struggle to achieve all of these goals. To address this capability gap, we present Incremental Manifold Edge-based Separable ADMM (iMESA) a fully distributed C-SLAM back-end algorithm that can provide a multi-robot team with accurate state estimates in real-time with only sparse pair-wise communication between robots. Extensive evaluation on real and synthetic data demonstrates that iMESA is able to outperform comparable state-of-the-art C-SLAM back-ends.
