Visually navigating the rms titanic with slam information filters
Date of Original Version
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.
Robotics: Science and Systems
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.