Robotics: Science and Systems I
Visually Navigating the RMS Titanic with SLAM Information Filters
Ryan Eustice, Hanumant Singh, John Leonard, Matthew Walter, Robert BallardAbstract: 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.
Bibtex:
@INPROCEEDINGS{ Eustice-RSS-05, 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} }