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

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Visually mapping the RMS Titanic: Conservative covariance estimates for SLAM information filters

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Ryan M. Eustice, Hanumant Singh, John J. Leonard and Matthew R. Walter, Visually mapping the RMS Titanic: Conservative covariance estimates for SLAM information filters. International Journal of Robotics Research, 25(12):1223-1242, 2006.

Abstract

This paper describes a vision-based, large-area, simultaneous localization and mapping (SLAM) algorithm that respects the low-overlap imagery constraints typical of underwater vehicles while exploiting the inertial sensor information that is routinely available on such platforms. We present a novel strategy for efficiently accessing and maintaining consistent covariance bounds within a SLAM information filter, thereby greatly increasing the reliability of data association. The technique is based upon solving a sparse system of linear equations coupled with the application of constant-time Kalman updates. The method is shown to produce consistent covariance estimates suitable for robot planning and data association. Real-world results are reported for a vision-based, six degree of freedom SLAM implementation using data from a recent survey of the wreck of the RMS Titanic.

Bibtex entry

@ARTICLE { reustice-2006c,
    AUTHOR = { Ryan M. Eustice and Hanumant Singh and John J. Leonard and Matthew R. Walter },
    TITLE = { Visually mapping the {RMS} {Titanic}: Conservative covariance estimates for {SLAM} information filters },
    JOURNAL = { International Journal of Robotics Research },
    YEAR = { 2006 },
    VOLUME = { 25 },
    NUMBER = { 12 },
    PAGES = { 1223--1242 },
}