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

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... 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.