Online acoustic localization methods for autonomous underwater vehicles

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. ​
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