A Comparative Study of Vision Guided AUV Navigation Techniques for Pipeline/Cable Inspection

International Journal of VLSI & Signal Processing

© 2015 by SSRG - IJVSP Journal

Volume 2 Issue 2

Year of Publication : 2015

Authors : Alex Raj S.M , Aruna S. Rajan and Supriya M. H

10.14445/23942584/IJVSP-V2I2P107

How to Cite?

Alex Raj S.M , Aruna S. Rajan and Supriya M. H, "A Comparative Study of Vision Guided AUV Navigation Techniques for Pipeline/Cable Inspection," SSRG International Journal of VLSI & Signal Processing, vol. 2,  no. 2, pp. 42-47, 2015. Crossref, https://doi.org/10.14445/23942584/IJVSP-V2I2P107

Abstract:

In the last few years exploitation of underwater gas and oil fields have increased. The produced oil and gas is transported mainly using pipelines which need to be inspected regularly. Submarine communication cables are laid on the sea floor for data communication across stretches of ocean. Due to earthquakes and other environmental changes these underwater cables and pipelines can get wear or get damaged. These damages need to be found and repaired quickly. Also the state of the pipelines and cables need to be constantly monitored. Currently inspection of underwater pipelines are done using Remotely Operated Vehicles (ROVs) which require human intervention. But this method is very risky. A more practical solution is to develop an intelligent vision based navigation and guidance system which involves efficient method for vision based target detection and tracking methods in underwater environment. AUVs used for survey missions in an underwater environment requires advanced precision navigation systems. Navigation usually requires high speed and high accuracy computation. Navigation is done through different techniques of which vision based navigation is the cheapest as it requires only a single camera. This paper presents a review on various AUV navigation techniques which uses vision sensor or any combination of vision sensor with any other sensor for navigation over the last decade

Keywords:

Automatic Underwater Vehicle, Navigation, CCD camera, Vision, Optical Flow, Mean Shift Tracking, Segmentation

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