| Summary: | This thesis considers intelligent solutions that facilitates for autonomous technology in underwater intervention and navigation. A special focus have been on implementing methods and solutions in inspection, maintenance, and repair (IMR) operations using low cost equipment. The presented work involves development and implementation of solutions to increase the efficiency and safety of operations, and includes both theoretical contributions and experimental testing. The work includes learning algorithms to improve visual authenticity of simulators and digital twin scenarios, computer vision in guidance and navigation of underwater vehicles and intervention systems, development and testing of novel equipment, and experimental verification of presented methods and equipment. The introduction of advanced learning algorithms enables systems to perform tasks that was previous too complex and complicated for any modelled solutions. This thesis explores the use of generative adversarial networks to improve the realism of simulated environments, which again will improve the transferred learning between simulated environments and real world operations. Such a mapping between domains is complex to model, especially in the underwater scene given the intricate scenery with scattering of light and marine particles. Machine learning algorithms provides new solutions for this mapping, and can aid in improving result from simulation tools to have greater impact on real world operations. In the same way humans uses their senses to experience life, autonomous systems requires sensory feedback to act and react upon. A sensor is only as effective as the information that can be extracted from the sensory output, and increasing and strengthening this information will improve the support from the sensor. Camera footage contain information with higher spatial and temporal resolution than acoustic information, and utilizing this information to its full will improve today's sensory systems. This thesis explores the use of visual aid and ...
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