Unified random finite set theoretic approach to autonomous underwater vehicle navigation

216 p.This thesis addresses the problem of navigation, localization and mapbuilding in an unknown and unstructured environment and its application to an autonomous underwater vehicle (AUV). Given the dynamic nature of the underwater environment and the limitations of traditional baseline based systems, the inability to use GPS underwater, and the range limited optical vision systems, our approach for overcoming these navigation limitations has been to embrace a feature based simultaneous localization and mapping (SLAM) framework while explicitly exploiting the available navigational sensor suite and rich blazed array sonar imagery that is commonly used in underwater explorations. The most common formulation of the feature based SLAM problem is founded on a vector based stochastic framework, where the sensor models and the vehicle models are represented in state space form and the joint posterior or its statistics are obtained based on recursive Bayesian estimation.DOCTOR OF PHILOSOPHY (EEE