Data extraction of tunnel point clouds from images captured by robotic camera

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dc.check.date2024-06-14
dc.check.infoAccess to this item is restricted until after the conference
dc.contributor.authorZhang, Ranen
dc.contributor.authorLi, Zilien
dc.contributor.funderChina Scholarship Council
dc.date.accessioned2024-04-10T14:37:32Z
dc.date.available2024-04-06T18:42:10Zen
dc.date.available2024-04-10T14:37:32Z
dc.date.issued2024
dc.date.updated2024-04-06T17:42:12Zen
dc.description.abstractThe inspection and maintenance of underground tunnels are critical to ensuring their long-term safety and stability, as well as to preventing potential accidents and costly repairs. In recent years, there has been increasing interest in the use of automated tunnel inspection systems to monitor tunnel conditions and detect any signs of deterioration. These systems, developed under projects like ROBO-SPECT [1], represent a significant advancement over traditional inspection methods such as LIDAR detection and handheld camera photography. Unlike these traditional methods, a robot-mounted HD camera offers a more efficient and effective solution for acquiring high-quality image-based data for tunnel inspection[2]. By mounting an HD camera on a robotic platform, it is possible to obtain a detailed and accurate 3D model of the tunnel environment, which can be used for further analysis and inspection. Moreover, the integration of cross-disciplinary datasets, particularly in the areas of geometry analysis and machine learning-based data extraction, is continuing to improve the accuracy and efficiency of automated tunnel inspection systems [3]. By leveraging the latest advances in machine learning and data analysis techniques [4], it is possible to extract valuable insights from the collected data, such as the identification of cracks, spalls, and other forms of tunnel deterioration. In summary, the use of robot-mounted HD cameras and cross-disciplinary data analysis techniques offers exciting opportunities for advancing the field of automated tunnel inspection. By continuing to innovate and improve these technologies, we can help ensure the long-term safety and sustainability of underground tunnel infrastructure.
dc.description.statusPeer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid1717
dc.identifier.citationZhang, R. and Li, Z. (2024) 'Data extraction of tunnel point clouds from images captured by robotic camera', Proceedings of 85th EAGE Annual Conference & Exhibition, Oslo, Norway, 10 -13 June. Extended Abstract 1717 (5pp).en
dc.identifier.endpage5
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/15790
dc.language.isoenen
dc.relation.ispartof85th EAGE Annual Conference & Exhibition, Oslo, Norway, 10 -13 June 2024
dc.rights© 2024, the Authors.
dc.subjectUnderground tunnel infrastructure
dc.subjectRobot-mounted HD camera
dc.subjectCross-disciplinary data analysis techniques
dc.subjectTunnel inspection
dc.titleData extraction of tunnel point clouds from images captured by robotic camera
dc.typeConference item
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