Remote Sensing · 2019 · 44 citations · 22 references
EngineeringCivil EngineeringGeoinformaticsMachine VisionSurveyingTunnel EngineeringLidar Remote SensingPhotogrammetric SurveyingPhotogrammetryPoint Cloud ProcessingTunnel MonitoringUnderground Surveying3D Laser ScanningLaser ScannersLaser ScanningLidar Data AcquisitionLidar Data CollectionLight Detection And Ranging Point Cloud AcquisitionRailway TunnelsTunnels
Transport networks need periodic inspections to increase their safety and improve their management. In the last few years, LiDAR (light detection and ranging) technology has become a tool for helping to create a precise database of almost any type of infrastructure. Mobile laser scanning (MLS) systems use a laser beam to collect dense three dimensional (3D) point clouds, which include geometric and radiometric data of the environment in which they are placed. In the context of this paper, a methodology for automatically inspecting the clearance gauge and the deflection of the aerial contact line in railway tunnels is presented. The main objective is to compare results and verify their compliance with the Spanish norm. The 3D data are provided by a LYNX Mobile Mapper System (MMS). First, the area is surveyed and then the obtained (3D) point cloud is classified into contact wire, suspension wire, and remaining points. Finally, the inspection of the railway’s power line is performed. The validation of the proposed methodology has been carried out in three different tunnel point clouds, obtaining both qualitative and quantitative results for points’ classification, together with the results of the measures performed.
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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles R. Qi, Hao Su, Kaichun Mo et al. · arXiv (Cornell University) · 2016 · 2.9K citations · Full text
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Charles R. Qi, Li Yi, Hao Su et al. · arXiv (Cornell University) · 2017 · 2.1K citations · Full text
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