1 citations · 1 across the 4 of their papers we have counts for
4 papers
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,…
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…
A Slices Perspective for Incremental Nonparametric Inference in High Dimensional State Spaces
Moshe Shienman, Ohad Levy-Or, Michael Kaess +1
We introduce an innovative method for incremental nonparametric probabilistic inference in high-dimensional state spaces. Our approach leverages \slices from high-dimensional surfa…
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…