Hypothesis Grids: Improving Long Baseline Navigation for Autonomous Underwater Vehicles

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Abstract
Navigation continues to fundamentally limit our ability to understand the underwater world. Long baseline navigation uses range measurements to localize a remote vehicle using acoustic time-of-flight estimates. For autonomous surveys requiring high precision navigation, current solutions do not satisfy the performance or robustness requirements. Hypothesis grids represent the survey environment capturing the spatial dependence of acoustic range measurement, providing a framework for improving navigation precision and increasing the robustness with respect to non-Gaussian range observations. Prior association probabilities quantify the measurement quality as a belief that subsequent observations will correspond to the direct-path, a multipath, or an outlier as a function of the estimated location. Such a characterization is directly applicable to Bayesian navigation techniques. The algorithm for creating the representation has three main components: Mixed-density sensor model using Gaussian and uniform probability distributions, measurement classification and multipath model identification using expectation-maximization (EM), and grid-based spatial representation. We illustrate the creation of a set of hypothesis grids, the feasibility of the approach, and the utility of the representation using survey data from the autonomous benthic explorer (ABE)

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