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

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Visually navigating the RMS Titanic with SLAM information filters

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Ryan Eustice, Hanumant Singh, John Leonard, Matthew Walter and Robert Ballard, Visually navigating the RMS Titanic with SLAM information filters. In Proceedings of the Robotics: Science and Systems Conference, pages 57-64, Cambridge, MA, USA, June 2005.

Abstract

This paper describes a vision-based large-area simultaneous localization and mapping (SLAM) algorithm that respects the constraints of low-overlap imagery typical of underwater vehicles while exploiting the information associated with the inertial sensors that are routinely available on such platforms. We present a novel strategy for efficiently accessing and maintaining consistent covariance bounds within a SLAM information filter, 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 presented for a vision-based 6 DOF SLAM implementation using data from a recent ROVsurvey of the wreck of the RMS Titanic.

Bibtex entry

@INPROCEEDINGS { reustice-2005b,
    AUTHOR = { Ryan Eustice and Hanumant Singh and John Leonard and Matthew Walter and Robert Ballard },
    TITLE = { Visually navigating the {RMS} {T}itanic with {SLAM} information filters },
    BOOKTITLE = { Proceedings of the Robotics: Science and Systems Conference },
    PUBLISHER = { MIT Press },
    YEAR = { 2005 },
    MONTH = { June },
    ADDRESS = { Cambridge, MA, USA },
    PAGES = { 57--64 },
}