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

## TL;DR

This paper introduces an adaptive, multimodal underwater navigation scheme that enhances deep-sea localization accuracy by combining dense point cloud generation, plane registration, and adaptive perception cue selection, validated through simulations and field trials.

## Contribution

It presents a novel two-stage adaptive navigation approach that integrates multimodal perception cues for improved underwater localization performance.

## Key findings

- Enhanced localization accuracy in deep-sea scenarios.
- Effective noise filtering using probabilistic maps.
- Validated approach through simulation and field trials.

## Abstract

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.