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cs.RO2026
riMESA: Consensus ADMM for Real-World Collaborative SLAM
Daniel McGann, Michael Kaess
Collaborative Simultaneous Localization and Mapping (C-SLAM) is a fundamental capability for multi-robot teams as it enables downstream tasks like planning and navigation. However,…
cs.RO2024
iMESA: Incremental Distributed Optimization for Collaborative Simultaneous Localization and Mapping
Daniel McGann, Michael Kaess
This paper introduces a novel incremental distributed back-end algorithm for Collaborative Simultaneous Localization and Mapping (C-SLAM). For real-world deployments, robotic teams…
cs.RO2023★ 1 cited
Asynchronous Distributed Smoothing and Mapping via On-Manifold Consensus ADMM
Daniel McGann, Kyle Lassak, Michael Kaess
In this paper we present a fully distributed, asynchronous, and general purpose optimization algorithm for Consensus Simultaneous Localization and Mapping (CSLAM). Multi-robot team…