Vision-Based Localization System Suited to Resident Underwater Vehicles
Open Access
Abstract
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.Keywords
Funding Information
- Science Foundation Ireland (12/RC/2302_P2 & 14/SP/2740)
- Horizon 2020 Framework Programme (731103)
This publication has 20 references indexed in Scilit:
- Autonomous underwater panel operation by GIRONA500 UVMS: A practical approach to autonomous underwater manipulation2015
- Review of underwater SLAM techniques2015
- Underwater SLAM: Challenges, state of the art, algorithms and a new biologically-inspired approach2014
- AUV Navigation and Localization: A ReviewIEEE Journal of Oceanic Engineering, 2013
- Field resident AUV systems — Chevron's long-term goal for AUV development2012
- Vision-based localization of an underwater robot in a structured environment2004
- A review of sensor fusion techniques for underwater vehicle navigation2004
- A vision system for an underwater cable trackerMachine Vision and Applications, 2002
- Multisensor data fusion for underwater navigationRobotics and Autonomous Systems, 2001
- Vision based autonomous underwater vehicle navigation: underwater cable tracking1997