Long Range Underwater Navigation using Gravity-Based Measurements
The aim of this thesis is to demonstrate feasibility of a gravity-based system for long range underwater localization. Such a system is demonstrated, in simulations, with the use of particle filter-based localization and Rao-Blackwellized particle filter SLAM (simultaneous localization and mapping). This system allows an autonomous underwater vehicle (AUV) to operate submerged for extended periods without the use of an active sensor, thus widening the variety of missions that an AUV can be tasked with. Additionally, this thesis demonstrates how information theory techniques can be used to plan a path through a region such that SLAM data association within that region is improved thus improving the performance of SLAM. The results from this work also indicate that characteristic value can be used to evaluate the ”SLAMability” of an environment. Combining the characteristic value with information theory techniques improves the performance of SLAM at extended ranges enabling long range underwater localization.