Toward Simultaneous Localization and Mapping in Aquatic Dynamic Environments

Simultaneous Localization and Mapping (SLAM) is a desired capability for an autonomous vehicle. SLAM is the process used to construct a representation map of an unknown surrounding world, localize in it, and use this map to navigate through it. For aquatic environments, this is an even harder challenge since it is a non-structured and highly dynamical environment in which classical landmark-based approaches to the SLAM problem are not suitable. In this paper, we use previously developed representations of aquatic environments based on Dynamical Systems and Augmented Bathymetry to construct a preliminary solution to the SLAM problem. A simple motion model is combined with physicochemical readings of the water to find candidate locations. Our ideas were preliminarily tested in a dataset acquired in the Lake Nighthorse, CO, obtaining promising results in the new strategy.