DUGiDocsOnline acoustic localization methods for autonomous underwater vehicles
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Autonomous Underwater Vehicles (AUVs) true autonomy capabilities in complex
unknown environments, have not yet been fully achieved because of the lack of
online algorithms that can solve fundamental problems such as localization,
mapping and path-planning on-board the AUV.
This thesis presents the development of two online localization algorithms
for AUVs. The first algorithm is based on a Sum of Gaussian filter for online
range-only localization of a Docking Station for battery recharging and data
uploading. This algorithm is tested in a wider project where it is combined
with other algorithms to produce a complete homing and docking strategy.
The second algorithm proposes an online SLAM framework for continuous
occupancy mapping named H-SLAM. It uses a Rao-Blackwellized Particle Filter
where each particle carries a Hilbert Map representation of the environment.
This algorithm is tested on two real-world datasets offering a significantly
better reconstruction of the environment than using DR navigation.
L'accés als continguts d'aquesta tesi queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: http://creativecommons.org/licenses/by/4.0/