Laser Scanning Point Cloud Improvement by Implementation of RANSAC for Pipeline Inspection Application
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Laser Scanning Point Cloud Improvement by Implementation of RANSAC for Pipeline Inspection Application. / Sepulveda-Valdez, Cesar; Sergiyenko, Oleg; Alaniz-Plata, Ruben et al.
IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. Piscataway : IEEE - Institute of Electrical and Electronics Engineers Inc., 2023. (IECON Proceedings (Industrial Electronics Conference)).Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - Laser Scanning Point Cloud Improvement by Implementation of RANSAC for Pipeline Inspection Application
AU - Sepulveda-Valdez, Cesar
AU - Sergiyenko, Oleg
AU - Alaniz-Plata, Ruben
AU - Nunez-Lopez, Jose A.
AU - Tyrsa, Vera
AU - Flores-Fuentes, Wendy
AU - Rodriguez-Quinonez, Julio C.
AU - Mercorelli, Paolo
AU - Kolendovska, Marina
AU - Kartashov, Vladimir
AU - Miranda-Vega, Jesus Elias
AU - Murrieta-Rico, Fabian N.
N1 - Publisher Copyright: © 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Laser Scanners used for Structural Health Monitoring applications such as Pipelines Structural Inspections normally needs Point Clouds from a large quantity of individual measurements that should be adjusted or post-processed to decrease overall point-cloud errors depending on scanner's characteristics. The posterior adjustment is commonly addressed by different mathematical methods or computational algorithms. According to application requirements methods such as machine learning, signal filtering, or RANSAC algorithms are used. This paper shows the application of an adapted/modify RANSAC algorithm especially suited for the pipeline inspection task. Aiming to increase the percentage of useful data per capture.
AB - Laser Scanners used for Structural Health Monitoring applications such as Pipelines Structural Inspections normally needs Point Clouds from a large quantity of individual measurements that should be adjusted or post-processed to decrease overall point-cloud errors depending on scanner's characteristics. The posterior adjustment is commonly addressed by different mathematical methods or computational algorithms. According to application requirements methods such as machine learning, signal filtering, or RANSAC algorithms are used. This paper shows the application of an adapted/modify RANSAC algorithm especially suited for the pipeline inspection task. Aiming to increase the percentage of useful data per capture.
KW - 3D Point-Cloud
KW - Dynamic Triangulation
KW - Laser Scanner
KW - Pipeline Inspection
KW - RANSAC
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85179513043&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/b17f0b06-c3f4-323a-912b-e47d68fadafe/
U2 - 10.1109/IECON51785.2023.10312684
DO - 10.1109/IECON51785.2023.10312684
M3 - Article in conference proceedings
AN - SCOPUS:85179513043
SN - 979-8-3503-3183-7
T3 - IECON Proceedings (Industrial Electronics Conference)
BT - IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
PB - IEEE - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway
T2 - 49th Annual Conference of the IEEE Industrial Electronics Society, 2023
Y2 - 16 October 2023 through 19 October 2023
ER -