Deep Learning for Underwater Visual Odometry Estimation

This paper addresses Visual Odometry (VO) estimation in challenging underwater scenarios. Robot visual-based navigation faces several additional difficulties in the underwater context, which severely hinder both its robustness and the possibility for persistent autonomy in underwater mobile robots u...

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Bibliographic Details
Published in:IEEE access 2020-01, Vol.8, p.1-1
Main Authors: Teixeira, Bernardo, Silva, Hugo, Matos, Anibal, Silva, Eduardo
Format: Article
Language:English
Subjects:
Artificial intelligence
Cameras
Computer vision
Context
Deep learning
Drift estimation
Estimation
Inertial sensing devices
Machine learning
Motion estimation
Robot navigation
Robots
Robustness
Trajectory analysis
Underwater
Visual odometry
Visual perception
Visual SLAM
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Summary:This paper addresses Visual Odometry (VO) estimation in challenging underwater scenarios. Robot visual-based navigation faces several additional difficulties in the underwater context, which severely hinder both its robustness and the possibility for persistent autonomy in underwater mobile robots using visual perception capabilities. In this work, some of the most renown VO and Visual Simultaneous Localization and Mapping (v-SLAM) frameworks are tested on underwater complex environments, assessing the extent to which they are able to perform accurately and reliably on robotic operational mission scenarios. The fundamental issue of precision, reliability and robustness to multiple different operational scenarios, coupled with the rise in predominance of Deep Learning architectures in several Computer Vision application domains, has prompted a great a volume of recent research concerning Deep Learning architectures tailored for visual odometry estimation. In this work, the performance and accuracy of Deep Learning methods on the underwater context is also benchmarked and compared to classical methods. Additionally, an extension of current work is proposed, in the form of a visual-inertial sensor fusion network aimed at correcting visual odometry estimate drift. Anchored on a inertial supervision learning scheme, our network managed to improve upon trajectory estimates, producing both metrically better estimates as well as more visually consistent trajectory shape mimicking.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2978406