Work

Visually Navigating the RMS Titanic with SLAM Information Filters

Year: 2005

Type: conference-paper

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.

Source: Robotics: Science and Systems

DOI: 10.15607/rss.2005.i.008

Authors: Ryan Eustice, Hanumant Singh, John Leonard, Matthew Walter, Robert Ballard

Institutions: Woods Hole Oceanographic Institution, Massachusetts Institute of Technology, University of Rhode Island

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, Woods Hole Oceanographic Institution

Award: An Engineering Research Center for Subsurface Sensing and Imaging Systems - CenSSIS