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Underwater robot interventions require a high level of safety andreliability. A major challenge to address is a robust and accurate acquisitionof localization estimates, as it is a prerequisite to enable more complextasks, e.g. floating manipulation and mapping. State-of-the-art navigation incommercial operations, such as oil & gas production (OGP), rely on costlyinstrumentation. These can be partially replaced or assisted by visualnavigation methods, especially in deep-sea scenarios where equipment deploymenthas high costs and risks. Our work presents a multimodal approach that adaptsstate-of-the-art methods from on-land robotics, i.e., dense point cloudgeneration in combination with plane representation and registration, to boostunderwater localization performance. A two-stage navigation scheme is proposedthat initially generates a coarse probabilistic map of the workspace, which isused to filter noise from computed point clouds and planes in the second stage.Furthermore, an adaptive decision-making approach is introduced that determineswhich perception cues to incorporate into the localization filter to optimizeaccuracy and computation performance. Our approach is investigated first insimulation and then validated with data from field trials in OGP monitoring andmaintenance scenarios.

Adaptive Navigation Scheme for Optimal Deep-Sea Localization Using Multimodal Perception Cues