PeRL STUDIES AUTONOMOUS NAVIGATION & MAPPING FOR MOBILE ROBOTS IN A PRIORI UNKNOWN ENVIRONMENTS.

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Exactly sparse delayed-state filters

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Ryan M. Eustice, Hanumant Singh and John J. Leonard, Exactly sparse delayed-state filters. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 2417-2424, Barcelona, Spain, April 2005.

Abstract

This paper presents the novel insight that the SLAM information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment which rely upon scan-matching raw sensor data. Scan-matching raw data results in virtual observations of robot motion with respect to a place its previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature-based SLAM information algorithms like Sparse Extended Information Filters or Thin Junction Tree Filters. These methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparseness of the delayed-state framework is that it allows one to take advantage of the information space parameterization without having to make any approximations. Therefore, it can produce equivalent results to the "full-covariance" solution.

Bibtex entry

@INPROCEEDINGS { reustice-2005a,
    AUTHOR = { Ryan M. Eustice and Hanumant Singh and John J. Leonard },
    TITLE = { Exactly sparse delayed-state filters },
    BOOKTITLE = { Proceedings of the IEEE International Conference on Robotics and Automation },
    YEAR = { 2005 },
    MONTH = { April },
    ADDRESS = { Barcelona, Spain },
    PAGES = { 2417--2424 },
}