To effectively examine ocean processes we must often sample over the duration of long (weeks to months) oscillation patterns. Such sampling requires persistent autonomous underwater vehicles, that have a similarly long deployment duration. Actively actuated (propeller-driven) underwater vehicles have proven effective in multiple sampling scenarios, however they have limited deployment endurance. The emergence of less actuated vehicles, i.e., underwater gliders, has enabled greater energy savings and thus increased endurance. Due to reduced actuation, these vehicles are more susceptible to external forces, e.g., ocean currents, causing them to have poor navigational and localization accuracy underwater. This is exacerbated in coastal regions, where current velocities are the same order of magnitude as the vehicle velocity.In this paper, we examine a method of reducing navigation and localization error, not only for navigation, but more so for more accurately reconstructing the path that the glider traversed to contextualize the gathered data, with respect to the science question at hand. We present a set of algorithms for offline processing that accurately localizes the traversed path of an underwater glider over long-term, ocean deployments. The proposed method utilizes terrain-based navigation with only depth, altimeter and compass data compared to local bathymetry maps to provide accurate reconstructions of traversed paths in the ocean.978-1-4799-8736-8/15/$31.00 ©2015 IEEE

Effective study of ocean processes requires sampling over the duration of long (weeks to months) oscillation patterns. Such sampling requires persistent, autonomous underwater vehicles that have a similarly, long deployment duration. The spatiotemporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. In this paper, we consider the combination of two methods for reducing navigation and localization error: a predictive approach based on ocean model predictions and a prior information approach derived from terrain-based navigation. The motivation for this work is not only for real-time state estimation but also for accurately reconstructing the actual path that the vehicle traversed to contextualize the gathered data, with respect to the science question at hand. We present an application for the practical use of priors and predictions for large-scale ocean sampling. This combined approach builds upon previous works by the authors and accurately localizes the traversed path of an underwater glider over long-duration, ocean deployments. The proposed method takes advantage of the reliable, short-term predictions of an ocean model, and the utility of priors used in terrain-based navigation over areas of significant bathymetric relief to bound uncertainty error in dead-reckoning navigation. This method improves upon our previously published works by (1) demonstrating the utility of our terrain-based navigation method with multiple field trials and (2) presenting a hybrid algorithm that combines both approaches to bound navigational error and uncertainty for long-term deployments of underwater vehicles. We demonstrate the approach by examining data from actual field trials with autonomous underwater gliders and demonstrate an ability to estimate geographical location of an underwater glider to <100 m over paths of length >2 km. Utilizing the combined algorithm, we are able to prescribe an uncertainty bound for navigation and instruct the glider to surface if that bound is exceeded during a given mission.

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