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Article

Vision-Based Localization System Suited to Resident Underwater Vehicles

1
Centre for Robotics & Intelligent Systems, University of Limerick, V94 T9PX Limerick, Ireland
2
School of Computing, Engineering, and Physical Sciences, University of the West of Scotland, Glasgow G72 0AG, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 529; https://doi.org/10.3390/s20020529
Received: 9 December 2019 / Revised: 10 January 2020 / Accepted: 16 January 2020 / Published: 18 January 2020
(This article belongs to the Special Issue Autonomous Underwater Vehicle Navigation)
In recent years, we have seen significant interest in the use of permanently deployed resident robotic vehicles for commercial inspection, maintenance and repair (IMR) activities. This paper presents a concept and demonstration, through offshore trials, of a low-cost, low-maintenance, navigational marker that can eliminate drift in vehicle INS solution when the vehicle is close to the IMR target. The subsea localisation marker system is fixed on location on the resident field asset and is used in on-vehicle machine vision algorithms for pose estimation and facilitation of a high-resolution world coordinate frame registration with a high refresh rate. This paper presents evaluation of the system during trials in the North Atlantic Ocean during January 2019. System performances and propagation of position error is inspected and estimated, and the effect of intermittent visual based position update to Kalman filter and onboard INS solution is discussed. The paper presents experimental results of the commercial state-of-the-art inertial navigation system operating in the pure inertial mode for comparison. View Full-Text
Keywords: resident UUVs; localization; subsea navigation resident UUVs; localization; subsea navigation
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MDPI and ACS Style

Trslić, P.; Weir, A.; Riordan, J.; Omerdic, E.; Toal, D.; Dooly, G. Vision-Based Localization System Suited to Resident Underwater Vehicles. Sensors 2020, 20, 529. https://doi.org/10.3390/s20020529

AMA Style

Trslić P, Weir A, Riordan J, Omerdic E, Toal D, Dooly G. Vision-Based Localization System Suited to Resident Underwater Vehicles. Sensors. 2020; 20(2):529. https://doi.org/10.3390/s20020529

Chicago/Turabian Style

Trslić, Petar, Anthony Weir, James Riordan, Edin Omerdic, Daniel Toal, and Gerard Dooly. 2020. "Vision-Based Localization System Suited to Resident Underwater Vehicles" Sensors 20, no. 2: 529. https://doi.org/10.3390/s20020529

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