Visually mapping the RMS Titanic: Conservative covariance estimates for SLAM information filters

Summary

Ryan M. Eustice, Hanumant Singh, John J. Leonard and Matthew R. Walter, Visually mapping the RMS Titanic: Conservative covariance estimates for SLAM information filters. International Journal of Robotics Research, 25(12):1223-1242, 2006.

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 reported for a vision-based, six degree of freedom SLAM implementation using data from a recent survey of the wreck of the RMS Titanic.

Bibtex entry