Robotics: Science and Systems I

Visually Navigating the RMS Titanic with SLAM Information Filters

Ryan Eustice, Hanumant Singh, John Leonard, Matthew Walter, Robert Ballard

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 presented for a vision-based 6-DOF SLAM implementation using data from a recent ROV survey of the wreck of the RMS Titanic.



    AUTHOR    = {Ryan Eustice and Hanumant Singh and John Leonard and 
                 Matthew Walter and Robert Ballard},
    TITLE     = {Visually Navigating the RMS Titanic with SLAM
                 Information Filters},
    BOOKTITLE = {Proceedings of Robotics: Science and Systems},
    YEAR      = {2005},
    ADDRESS   = {Cambridge, USA},
    MONTH     = {June},
    DOI       = {10.15607/RSS.2005.I.008}