Adaptive Navigation Scheme for Optimal Deep-Sea Localization Using Multimodal Perception Cues
This work addresses the problem of costly and risky deep-sea navigation for underwater robots in commercial operations like oil and gas, offering an incremental improvement by adapting existing methods.
The paper tackles the challenge of achieving robust and accurate localization for underwater robots in deep-sea scenarios, presenting a multimodal approach that adapts on-land robotics methods to boost performance, with validation in simulation and field trials for oil and gas operations.
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.