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

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Exactly sparse delayed-state filters for view-based SLAM

Summary


Ryan M. Eustice, Hanumant Singh and John J. Leonard, Exactly sparse delayed-state filters for view-based SLAM. IEEE Transactions on Robotics, 22(6):1100-1114, 2006.

Abstract

This paper reports the novel insight that the simultaneous localization and mapping (SLAM) information matrix is exactly sparse in a delayed- state framework. Such a framework is used in view-based representations of the environment that rely upon scan-matching raw sensor data to obtain virtual observations of robot motion with respect to a place it has previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature-based SLAM information algorithms, such as sparse extended information filter or thin junction-tree filter, since these methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparsity of the delayed-state framework is that it allows one to take advantage of the information space parameterization without incurring any sparse approximation error. Therefore, it can produce equivalent results to the full-covariance solution. The approach is validated experimentally using monocular imagery for two datasets: a test-tank experiment with ground truth, and a remotely operated vehicle survey of the RMS Titanic.

Bibtex entry

@ARTICLE { reustice-2006b,
    AUTHOR = { Ryan M. Eustice and Hanumant Singh and John J. Leonard },
    TITLE = { Exactly sparse delayed-state filters for view-based {SLAM} },
    JOURNAL = { IEEE Transactions on Robotics },
    YEAR = { 2006 },
    VOLUME = { 22 },
    NUMBER = { 6 },
    PAGES = { 1100--1114 },
}