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Kwanghyun Yoo

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Articles

Real-Time Elbow and T-Junction Detection and Estimation Framework for In-Line Inspection in Natural Gas Pipelines Using 3D Point Cloud

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By Hoa-Hung Nguyen, Jae-Hyun Park, Jae-Jun Kim, Kwanghyun Yoo
| MDPI
6. Results In this section, we first describe the experiment setup. Next, the elbow and T-junction detection result is presented, followed by the detailed estimation results for each of the parameters. Finally, we evaluate the processing time of the framework on the real PIG processing unit. 6.1. Experiment Setups To evaluate our proposed approach, we develop a pull-rig robot and a 20-inch prototype pipeline.
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Robust and Unbiased Estimation of Robot Pose and Pipe Diameter for Natural Gas Pipeline Inspection Using 3D Time-of-Flight (ToF) Sensors

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By Hoa-Hung Nguyen, Jae-Hyun Park, Jae-Jun Kim, Kwanghyun Yoo
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Improving Localization Accuracy of Offline Navigation Algorithms for Intelligent Pipeline Inspection Gauges and In‐Line Inspection Robotic Systems

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By Jae-Jun Kim, Kwanghyun Yoo, Dae-Kwang Kim, Seung-Ung Yang
| Wiley Online Library Verified
Conflicts of Interest The authors declare no conflicts of interest. References , and . 2003. The Pipeline Pigging Handbook, 3rd ed., 8–2–8–4. Houston, Texas, USA: Clarion Technical Publishers. , , , and . 2020. “An INS/GNSS Integrated Navigation in GNSS Denied Environment Using Recurrent Neural Network.” Defence Technology 16, no. 2: 334–340. , , , and . 2019.
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