Robotics: Science and Systems XX

iMESA: Incremental Distributed Optimization for Collaborative Simultaneous Localization and Mapping

Daniel McGann, Michael Kaess

Abstract:

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 on 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.

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Bibtex:

  
@INPROCEEDINGS{McGann-RSS-24, 
    AUTHOR    = {Daniel McGann AND Michael Kaess}, 
    TITLE     = {{iMESA: Incremental Distributed Optimization for Collaborative Simultaneous Localization and Mapping}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2024}, 
    ADDRESS   = {Delft, Netherlands}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2024.XX.085} 
}