A feasibility analysis on using bathymetry for navigation of autonomous underwater vehicles

Abstract

Bathymetric terrain maps generated from acoustic data offer an attractive alternative for reducing the submerged pose error estimates for autonomous underwater vehicles (AUVs). The goal of this work is to determine the extent of improvement in the navigational accuracy of an AUV equipped with an echo sounder for near-seafloor, shallow water applications. Given bathymetric variations of a certain terrain, this paper analyzes the best achievable positioning accuracy for AUVs. To counter for the strong non-linearity and the non-Gaussian nature of the problem, an optimal Bayesian estimator is initially derived. The fundamental limitations in the pose uncertainty using this approach is encompassed by the Posterior Cramér-Rao bound (PCRB), that is interpreted in terms of the sonar sensor accuracy and the bathy-metric variations. The PCRB on the position error covari-ance is determined and it is shown that the Bayesian Bootstrap filter closely follows this bound using real inferometric sonar data. Copyright 2013 ACM.