Polarization-based underwater geolocalization with deep learning

Xiaoyang Bai; Zuodong Liang; Zhongmin Zhu; Alexander Schwing; David Forsyth; Viktor Gruev

doi:10.1186/s43593-023-00050-6

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Polarization-based underwater geolocalization with deep learning

eLight Vol. 3, Issue 1, (2023)

RESEARCH ARTICLE | Updated：2024-09-25

- Polarization-based underwater geolocalization with deep learning
- Affiliations：
  
  Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
  Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
  Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
  Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
  Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL 61820, USA
- Author bio：
  
  *Viktor Gruevvgruev@illinois.edu
- Funds：
- DOI：10.1186/s43593-023-00050-6
  CLC：
- Published：2023，
  
  Published Online：10 July 2023，
  
  Received：07 April 2023，
  
  Revised：19 May 2023，
  
  Accepted：01 June 2023
- Accepted：
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Xiaoyang Bai, Zuodong Liang, Zhongmin Zhu, et al. Polarization-based underwater geolocalization with deep learning. [J]. eLight, 2023,3(1).
DOI：

Xiaoyang Bai, Zuodong Liang, Zhongmin Zhu, et al. Polarization-based underwater geolocalization with deep learning. [J]. eLight, 2023,3(1). DOI： 10.1186/s43593-023-00050-6.

摘要

Abstract

Water is an essential component of the Earth's climate

but monitoring its properties using autonomous underwater sampling robots remains a significant challenge due to lack of underwater geolocalization capabilities. Current methods for underwater geolocalization rely on tethered systems with limited coverage or daytime imagery data in clear waters

leaving much of the underwater environment unexplored. Geolocalization in turbid waters or at night has been considered unfeasible due to absence of identifiable landmarks. In this paper

we present a novel method for underwater geolocalization using deep neural networks trained on ~10 million polarization-sensitive images acquired globally

along with camera position sensor data. Our approach achieves longitudinal accuracy of ~55 km (~1000 km) during daytime (nighttime) at depths up to ~8 m

regardless of water turbidity. In clear waters

the transfer learning longitudinal accuracy is ~255 km at 50 m depth. By leveraging optical data in conjunction with camera position information

our novel method facilitates underwater geolocalization and offers a valuable tool for untethered underwater navigation.

关键词

Keywords

Underwater geolocalizationOcean explorationCelestial-based navigationPolarizationDeep learning

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