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Conservative edge sparsification for graph SLAM node removal

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Nicholas Carlevaris-Bianco and Ryan M. Eustice, Conservative edge sparsification for graph SLAM node removal. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 854-860, Hong Kong, China, June 2014.

Abstract

This paper reports on optimization-based methods for producing a sparse, conservative approximation of the dense potentials induced by node marginalization in simultaneous localization and mapping (SLAM) factor graphs. The proposed methods start with a sparse, but overconfident, Chow-Liu tree approximation of the marginalization potential and then use optimization-based methods to adjust the approximation so that it is conservative subject to minimizing the Kullback-Leibler divergence (KLD) from the true marginalization potential. Results are presented over multiple real-world SLAM graphs and show that the proposed methods enforce a conservative approximation, while achieving low KLD from the true marginalization potential.

Bibtex entry

@INPROCEEDINGS { ncarlevaris-2014a,
    AUTHOR = { Nicholas Carlevaris-Bianco and Ryan M. Eustice },
    TITLE = { Conservative edge sparsification for graph {SLAM} node removal },
    BOOKTITLE = { Proceedings of the IEEE International Conference on Robotics and Automation },
    YEAR = { 2014 },
    MONTH = { June },
    ADDRESS = { Hong Kong, China },
    PAGES = { 854--860 },
}