  Underwater robot interventions require a high level of safety and
reliability. A major challenge to address is a robust and accurate acquisition
of localization estimates, as it is a prerequisite to enable more complex
tasks, e.g. floating manipulation and mapping. State-of-the-art navigation in
commercial operations, such as oil & gas production (OGP), rely on costly
instrumentation. These can be partially replaced or assisted by visual
navigation methods, especially in deep-sea scenarios where equipment deployment
has high costs and risks. Our work presents a multimodal approach that adapts
state-of-the-art methods from on-land robotics, i.e., dense point cloud
generation in combination with plane representation and registration, to boost
underwater localization performance. A two-stage navigation scheme is proposed
that initially generates a coarse probabilistic map of the workspace, which is
used to filter noise from computed point clouds and planes in the second stage.
Furthermore, an adaptive decision-making approach is introduced that determines
which perception cues to incorporate into the localization filter to optimize
accuracy and computation performance. Our approach is investigated first in
simulation and then validated with data from field trials in OGP monitoring and
maintenance scenarios.
