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6-DOF Multi-session Visual SLAM using Anchor Nodes
McDonald, John; Kaess, Michael; Cadena, Cesar; Neira, Jose; Leonard, John J.
This paper describes a system for performing multisession visual mapping in large-scale environments. Multi-session mapping considers the problem of combining the results of multiple Simultaneous Localisation and Mapping (SLAM) missions performed repeatedly over time in the same environment. The goal is to robustly combine multiple maps in a common metrical coordinate system, with consistent estimates of uncertainty. Our work employs incremental Smoothing and Mapping (iSAM) as the underlying SLAM state estimator and uses an improved appearance-based method for detecting loop closures within single mapping sessions and across multiple sessions. To stitch together pose graph maps from multiple visual mapping sessions, we employ spatial separator variables, called anchor nodes, to link together multiple relative pose graphs. We provide experimental results for multi-session visual mapping in the MIT Stata Center, demonstrating key capabilities that will serve as a foundation for future work in large-scale persistent visual mapping.
Keyword(s): multi-session visual SLAM; lifelong learning; persistent autonomy
Publication Date:
Type: Conference item
Peer-Reviewed: Yes
Institution: Maynooth University
Citation(s): McDonald, John and Kaess, Michael and Cadena, Cesar and Neira, Jose and Leonard, John J. (2011) 6-DOF Multi-session Visual SLAM using Anchor Nodes. In: European Conference on Mobile Robots (ECMR), 7-9 November 2011, Orebro, Sweden.
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First Indexed: 2020-04-02 06:44:18 Last Updated: 2020-04-02 06:44:18