Robotics: Science and Systems XX
iMESA: Incremental Distributed Optimization for Collaborative Simultaneous Localization and Mapping
Daniel McGann, Michael KaessAbstract:
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.
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} }