An Evaluation of Bayesian Methods for Bathymetry-based Localization of Autonomous Underwater Robots
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This paper presents novel probabilistic algorithms for localization of autonomous underwater vehicles (AUVs) using bathymetry data. The algorithms, based on the principles of the Bayes filter, work by fusing bathymetry information with depth and altitude data from an AUV. Four different Bayes filter-based algorithms are used to design the localization algorithms: the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF), and Marginalized Particle Filter (MPF). The goal is to have these four filters available for localization under different bathymetric conditions and available computational resources. The localization algorithms overcome unique challenges of the underwater domain, such as visual distortion and RF signal attenuation, which make landmark-based localization infeasible. We evaluate the accuracy and computational cost of the algorithms in a range of simulations that use real-world bathymetry data under different operating conditions.
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