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Abstract

Abstract

Qatar is a worldwide leader in liquid natural gas (LNG) production and is poised to lead the world in gas-to-liquids (GTL) production with the commissioning of the Pearl GTL facility. Unfortunately, Qatar's gas fields contain non-negligible quantities of corrosive and toxic hydrogen sulfide (H2S), resulting in the ongoing need for expensive and labor intensive pipe inspection to detect and monitor areas of corrosion. Such inspection is critical to plant integrity, worker safety, and to ensure the economic productivity of the facility. Current industry practice relies on manual sensors operated by a worker located externally to the pipe. The complex pipe geometries and sheer number of pipes, result in a sparse inspection process that forces inspectors to extrapolate measurements to large areas of the pipe network that are unseen.

To overcome these limitations, we are pursuing a radically different approach that uses an articulated robot to navigate inside the pipe, combined with a vision-based perception system that can build a detailed, registered, high resolution 3D appearance map of the inside pipe surface. By using an articulated robot, we can significantly increase the direct measurement coverage of the pipe network. By using a vision-based perception system, we can build models for visualization of the inside pipe surface that can be directly evaluated for corrosion damage. Moreover, our approach lays the foundation for automating corrosion detection by enabling changes in co-registered multi-sensor fusion (e.g. using magnetic flux leakage) to be evaluated over time.

Our work to date has focused on developing monocular and stereo visual odometry systems, which are the core component to building high resolution 3D appearance maps of the pipe surface from a robot crawler located inside the pipe. We have developed algorithms that take imagery collected from a robot moving inside the pipe, and are able to estimate the motion of the vehicle and the resulting structure and appearance of the pipe surface. We have evaluated our algorithms on pipe segments and have generated accurate, high resolution stitched images of the internal pipe surface. We will describe the details of our algorithms, current results, and next steps in our work.

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/content/papers/10.5339/qfarf.2010.EEO4
2010-12-13
2019-10-19
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References

  1. Hansen 2010. P.I. Hansen., , B. Browning., , P. Rander., , H. Alismail. , Automating visual inspection of pipes used for natural gas production. , QFARF Proceedings , 2010 , EEO4 .
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarf.2010.EEO4
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