research-article

Real-time Image Enhancement for Vision-based Autonomous Underwater Vehicle Navigation in Murky Waters

Authors:

Vidyasagar Sadhu,

Dario PompiliAuthors Info & Claims

WUWNet '19: Proceedings of the 14th International Conference on Underwater Networks & Systems

Article No.: 18, Pages 1 - 8

https://doi.org/10.1145/3366486.3366523

Published: 13 February 2020 Publication History

Abstract

Classic vision-based navigation solutions, which are utilized in algorithms such as Simultaneous Localization and Mapping (SLAM), usually fail to work underwater when the water is murky and the quality of the recorded images is low. That is because most SLAM algorithms are feature-based techniques and often it is impossible to extract the matched features from blurry underwater images. To get more useful features, image processing techniques can be used to dehaze the images before they are used in a navigation/localization algorithm. There are many well-developed methods for image restoration, but the degree of enhancement and the resource cost of the methods are different. In this paper, we propose a new visual SLAM, specifically-designed for the underwater environment, using Generative Adversarial Networks (GANs) to enhance the quality of underwater images with underwater image quality evaluation metrics. This procedure increases the efficiency of SLAM and gets a better navigation and localization accuracy. We evaluate the proposed GANs-SLAM combination by using different images with various levels of turbidity in the water. Experiments were conducted and the data was extracted from the Carnegie Lake in Princeton, and the Raritan river both in New Jersey, USA.

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Cited By

Sun LWang YHui XMa XBai XTan M(2024)Underwater Robots and Key Technologies for Operation ControlCyborg and Bionic Systems10.34133/cbsystems.00895Online publication date: 27-Mar-2024
https://doi.org/10.34133/cbsystems.0089
Saad Saoud LElmezain MSultan AHeshmat MSeneviratne LHussain I(2024)Seeing Through the Haze: A Comprehensive Review of Underwater Image Enhancement TechniquesIEEE Access10.1109/ACCESS.2024.346555012(145206-145233)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3465550
Sadhu VAnjum KPompili D(2023)On-Board Deep-Learning-Based Unmanned Aerial Vehicle Fault Cause Detection and Classification via FPGAsIEEE Transactions on Robotics10.1109/TRO.2023.326938039:4(3319-3331)Online publication date: Aug-2023
https://doi.org/10.1109/TRO.2023.3269380
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WUWNet '19: Proceedings of the 14th International Conference on Underwater Networks & Systems

October 2019

210 pages

ISBN:9781450377409

DOI:10.1145/3366486

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 February 2020

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WUWNET'19

WUWNET'19: International Conference on Underwater Networks & Systems

October 23 - 25, 2019

GA, Atlanta, USA

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Cited By

Sun LWang YHui XMa XBai XTan M(2024)Underwater Robots and Key Technologies for Operation ControlCyborg and Bionic Systems10.34133/cbsystems.00895Online publication date: 27-Mar-2024
https://doi.org/10.34133/cbsystems.0089
Saad Saoud LElmezain MSultan AHeshmat MSeneviratne LHussain I(2024)Seeing Through the Haze: A Comprehensive Review of Underwater Image Enhancement TechniquesIEEE Access10.1109/ACCESS.2024.346555012(145206-145233)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3465550
Sadhu VAnjum KPompili D(2023)On-Board Deep-Learning-Based Unmanned Aerial Vehicle Fault Cause Detection and Classification via FPGAsIEEE Transactions on Robotics10.1109/TRO.2023.326938039:4(3319-3331)Online publication date: Aug-2023
https://doi.org/10.1109/TRO.2023.3269380
Mir IGul FMir SKhan MSaeed NAbualigah LAbuhaija BGandomi A(2022)A Survey of Trajectory Planning Techniques for Autonomous SystemsElectronics10.3390/electronics1118280111:18(2801)Online publication date: 6-Sep-2022
https://doi.org/10.3390/electronics11182801
Anjum KLi ZPompili D(2022)Acoustic Channel-aware Autoencoder-based Compression for Underwater Image Transmission2022 Sixth Underwater Communications and Networking Conference (UComms)10.1109/UComms56954.2022.9905691(1-5)Online publication date: 30-Aug-2022
https://doi.org/10.1109/UComms56954.2022.9905691
Gul FMir IAbualigah LSumari PForestiero A(2021)A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future DirectionsElectronics10.3390/electronics1018225010:18(2250)Online publication date: 13-Sep-2021
https://doi.org/10.3390/electronics10182250
Sadhu VZonouz SPompili D(2020)On-board Deep-learning-based Unmanned Aerial Vehicle Fault Cause Detection and Identification2020 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA40945.2020.9197071(5255-5261)Online publication date: May-2020
https://doi.org/10.1109/ICRA40945.2020.9197071

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