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“iMESA: Incremental Distributed Optimization for Collaborative Simultaneous Localization and Mapping” by D. McGann and M. Kaess. In Proc. Robotics: Science and Systems, RSS, (Delft, Netherlands), July 2024.
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.
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BibTeX entry:
@inproceedings{McGann24rss,
author = {D. McGann and M. Kaess},
title = {{iMESA}: Incremental Distributed Optimization for
Collaborative Simultaneous Localization and Mapping},
booktitle = {Proc. Robotics: Science and Systems, RSS},
address = {Delft, Netherlands},
month = jul,
year = {2024}
}