Volume 25 Issue 12 - Publication Date: 1 December 2006
Visually Mapping the RMS Titanic: Conservative Covariance Estimates for SLAM Information Filters
R.M. Eustice Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI 48109 USA, H. Singh Department of Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA 02543 USA, J.J. Leonard and M.R. Walter Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139 USA
This paper describes a vision-based, large-area, 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 DOF SLAM implementation using data from a recent survey of the wreck of the RMS Titanic
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