Bathymetric Change Detection During the 2013 Excavation of Monterrey A

Ian Vaughn
Ph.D. Candidate in Ocean Engineering

University of Rhode Island

Friday, Nov. 20, 2015, 3:00pm
Chase 130

Deep ocean microbathymetry presents a number of unique challenges as navigation accuracy rarely approaches sensor resolution. Navigation artifacts are especially apparent when looking for temporal changes at a site. A Simultaneous Localization and Mapping (SLAM) algorithm to refine the navigation solution in post processing using bathymetric sonar data is demonstrated as a solution to this problem. Extensive surveys of the Monterrey A shipwreck were conducted before and after the 2013 excavation with a 1350 kHz BlueView multibeam from ROV Hercules at 1,330m (4360 ft) depth. The proposed SLAM algorithm is shown to significantly outperform both Doppler Velocity Log (DVL)-based dead reckoning and Long BaseLine (LBL) acoustic navigation when producing maps on a 2.5cm grid. The applicability of each method to change detection is evaluated on two independent subsets of the pre-excavation survey. Finally, the effect of excavation on the site is shown by differencing the pre- and post- disturbance surveys. Excavation was found to have less impact than expected on site bathymetry.


Ian Vaughn is finishing a Ph.D. in Ocean Engineering at the University of Rhode Island in December. His dissertation focuses on algorithms to improve the navigation of robotic platforms for deep-ocean microbathymetric surveys, usually in collaboration with the Ocean Exploration Trust. Ian has completed an M.S. in Ocean Engineering (University of Rhode Island, 2012) and a B.S. in Computer Science (Cornell University, 2008). His research interests include software for automated data processing, navigation algorithms, and optical systems for sub-centimeter imaging in the deep ocean.