The localization of Autonomous Underwater Vehicles (AUVs) deployed for integrity inspection of liquid storage facilities, to prevent failure of the process, is a critical and challenging task. This is primarily due to the harsh and GPS-denied work environment, as well as to the high degree of accuracy required by such confined-space activities to ensure accurate motion control and navigation, and generate rigorous inspection data associated with their true physical locations. Conventionally, an AUV performing general surveying operations is equipped with an Inertial Navigation System (INS) and/or a Doppler Velocity Log (DVL) for real-time state estimation and positioning, with respect to some inertial reference frame, while navigating its local environment. Due to inherently accumulating measurement errors over time, an INS/DVL device usually relies on the GPS for periodic recalibrations, which requires surfacing of the submersible robot. In deep waters, this strategy is energy resource and time inefficient, hence costly. Furthermore, for covered and underwater environments such as storage tanks, GPS signals are not even accessible at the liquid surface. Moreover, neither the INS/DVL-GPS system nor the traditional baseline acoustic positioning systems, based on trilateration techniques, provide a satisfactory solution accuracy as demanded by precision tasks such as pinpointing defects in steel storage and underwater structures.

To overcome the shortcomings of the conventional underwater localization techniques, and achieve high-fidelity mapping between inspection data and real physical locations, we propose in this thesis a novel, accurate, and robust method to solve the robot localization problem inside confined, harsh, and GPS-denied environments. This method uses affordable sensors and fast algorithms to develop new techniques that provide accurate positions of the mobile agent. Given the geometry of the asset under investigation, a map representation for the robot's workspace is constructed based on range measurements over its boundaries. Then, the robot's position and orientation are accurately estimated relative to some defined reference landmarks (features) extracted from the map. In the event that the robot fails to recognize any landmark, a point-set registration technique is employed. In this case, the robot recursively matches map observations while in motion, which yields a relative position with respect to the most recently determined landmark-based position. The devised localization method will unleash fully autonomous robotic operations in confined, harsh, and GPS-denied environments. It will also facilitate Risk-Based Inspection (RBI) by employing predictive capabilities to optimize maintenance planning. This method can be applied in the oil and gas industry for inspecting liquid storage assets such as Aboveground Storage Tanks (ASTs) and Floating Production Storage and Offloading units (FPSOs).