The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Regular inspection for corrosion of the pipes used in Liquified Natural Gas (LNG) processing facilities is critical for safety. We argue that a visual perception system equipped on a pipe crawling robot can improve on existing techniques (Magnetic Flux Leakage, radiography, ultrasound) by producing high resolution registered appearance maps of the internal surface. To achieve this capability, it is necessary to estimate the pose of sensors as the robot traverses the pipes. We have explored two monocular visual odometry algorithms (dense and sparse) that can be used to estimate sensor pose. Both algorithms use a single easily made measurement of the scene structure to resolve the monocular scale ambiguity in their visual odometry estimates. We have obtained pose estimates using these algorithms with image sequences captured from cameras mounted on different robots as they moved through two pipes having diameters of 152mm (6”) and 406mm (16”), and lengths of 6 and 4 meters respectively. Accurate pose estimates were obtained whose errors were consistently less than 1 percent for distance traveled down the pipe.

Keywords

monocular, visual odometry, pipe inspection

Notes

Sponsor: Qatar National Research Fund

Associated Center(s) / Consortia: National Robotics Engineering Center

Associated Project(s): LNG Pipe Vision

Note: This work was jointly performed between NREC and CMU's Qatar campus.

Text Reference

Peter Hansen, Hatem Said Alismail, Peter Rander, and Brett Browning, "Monocular visual odometry for robot localization in LNG pipes," IEEE International Conference on Robotics and Automation, May, 2011.

BibTeX Reference

@inproceedings{Alismail_2011_6988,
   author = "Peter Hansen and Hatem Said Alismail and Peter Rander and Brett Browning",
   title = "Monocular visual odometry for robot localization in LNG pipes",
   booktitle = "IEEE International Conference on Robotics and Automation",
   month = "May",
   year = "2011",
   Notes = "This work was jointly performed between NREC and CMU's Qatar campus."
}

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