Visually Mapping the RMS Titanic with SLAM Information Filters

Professor Ryan M. Eustice

University of Michigan

Department of Naval Architecture & Marine Engineering

This talk describes a vision-based, large-area, simultaneous localization and mapping (SLAM) algorithm that respects the low-overlap imagery constraints typical of autonomous underwater vehicles (AUVs) while exploiting the inertial sensor information that is routinely available on such platforms. We adopt a systems-level approach exploiting the complementary aspects of inertial sensing and visual perception from a calibrated pose-instrumented platform. This systems-level strategy yields a robust solution to underwater imaging that overcomes many of the unique challenges of a marine environment (e.g., unstructured terrain, low-overlap imagery, moving light source). Our large-area SLAM algorithm recursively incorporates relative-pose constraints using a view-based representation that exploits exact sparsity in the Gaussian canonical form. We show that our algorithmic formulation is inherently sparse unlike other feature-based canonical SLAM algorithms, which impose sparseness via pruning approximations. In summary, our work advances the current state-of-the-art in underwater visual navigation by demonstrating end-to-end automatic processing of the largest visually navigated dataset to date using data collected from a survey of the RMS Titanic (path length over 3 kilometers, and 3100 square meters of mapped area). This accomplishment embodies the summed contributions to several current SLAM research issues including scalability, 6 degree of freedom motion, unstructured environments, and visual perception.

Friday, September 22, 2006

3:30 – 4:30 p.m.

1500 EECS