State Estimation for Shore Monitoring Using an Autonomous Surface Vessel
Gregory Hitz, François Pomerleau, Francis Colas, Roland Siegwart · 2016
Abstract. Although many applications of small Autonomous Surface Vessels rely on two-dimensional state estimation, inspection tasks based on long-range sensors require more accurate attitude estimates. In the context of shoreline monitoring relying on a nodding laser scanner, we evaluate three different extended Kalman filter approaches with respect to an accurate ground truth in the range of millimeters. Our experimen-tal setup allowed us to track the impact of sensors noise, including GPS non-Gaussian error, a phenomenon often underestimated. Extensive field experiments demonstrate that the use of a complementary filter in com-bination with a model-based extended Kalman filter performed best and reduced velocity errors by 73 % compared to GPS. Finally, following our state estimation observations, we present a long-term shore monitoring result highlighting changes in the environment over a period of 6 months.