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Visually Mapping the RMS Titanic: Conservative Covariance Estimates for SLAM Information Filters

Department of Naval Architecture and Marine Engineering University of Michigan Ann Arbor, MI 48109 USA, eustice{at}umich.edu

Hanumant Singh

Department of Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA 02543 USA, hanu{at}whoi.edu

John J. Leonard

Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139 USA, jleonard{at}mit.edu

Matthew R. Walter

Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139 USA, mwalter{at}mit.edu

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.

Key Words: SLAM • data association • information filters • mobile robotics • computer vision • underwater vehicles

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