Dr. Ryan M. Eustice Speaker, University of Michigan, Monday, February 26, 2007, 4:00 PM - 5:00 PM, Room 3-133

Abstract: 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 data set to date using imagery 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.

Biography: Ryan M. Eustice received the B.S. degree in mechanical engineering from Michigan State University, East Lansing, in 1998, and the Ph.D. degree in ocean engineering from the Massachusetts Institute of Technology/Woods Hole Oceanographic Institution Joint Program, Woods Hole, MA, in 2005. Currently, he is an Assistant Professor with the Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor. His research interests are in the areas of navigation and mapping, underwater computer vision and image processing, and autonomous underwater vehicles.