Visually navigating the rms titanic with slam information filters

Authors

Ryan Eustice, Woods Hole Oceanographic Institution
Hanumant Singh, Woods Hole Oceanographic Institution
John Leonard, Massachusetts Institute of Technology
Matthew Walter, Massachusetts Institute of Technology
Robert Ballard, University of Rhode Island

Document Type

Conference Proceeding

Date of Original Version

1-1-2005

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.

Publication Title, e.g., Journal

Robotics: Science and Systems

Volume

1

Citation/Publisher Attribution

Eustice, Ryan, Hanumant Singh, John Leonard, Matthew Walter, and Robert Ballard. "Visually navigating the rms titanic with slam information filters." Robotics: Science and Systems 1, (2005): 57-64. doi: 10.15607/rss.2005.i.008.