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

Visually navigating the RMS Titanic with SLAM information filters

Summary


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 },
}