Version 1
: Received: 16 June 2018 / Approved: 18 June 2018 / Online: 18 June 2018 (16:54:47 CEST)
How to cite:
Xie, X.; Zhao, M.; He, J.; Zhou, B. Automatic Processing Method for Deformation Monitoring of Circle Tunnels Based on 3D LiDAR Data. Preprints2018, 2018060283. https://doi.org/10.20944/preprints201806.0283.v1
Xie, X.; Zhao, M.; He, J.; Zhou, B. Automatic Processing Method for Deformation Monitoring of Circle Tunnels Based on 3D LiDAR Data. Preprints 2018, 2018060283. https://doi.org/10.20944/preprints201806.0283.v1
Xie, X.; Zhao, M.; He, J.; Zhou, B. Automatic Processing Method for Deformation Monitoring of Circle Tunnels Based on 3D LiDAR Data. Preprints2018, 2018060283. https://doi.org/10.20944/preprints201806.0283.v1
APA Style
Xie, X., Zhao, M., He, J., & Zhou, B. (2018). Automatic Processing Method for Deformation Monitoring of Circle Tunnels Based on 3D LiDAR Data. Preprints. https://doi.org/10.20944/preprints201806.0283.v1
Chicago/Turabian Style
Xie, X., Jiamin He and Biao Zhou. 2018 "Automatic Processing Method for Deformation Monitoring of Circle Tunnels Based on 3D LiDAR Data" Preprints. https://doi.org/10.20944/preprints201806.0283.v1
Abstract
The application of 3D LiDAR technology has become increasingly extensive in tunnel monitoring due to the large density and high accuracy of the acquired spatial data. The proposed processing method aims at circle tunnels and provides a clear workflow to automatically process raw point data and easily interpretable results to analyze tunnel health state. The proposed automatic processing method employs a series of algorithms to extract point cloud of a single tunnel segment without obvious noise from entire raw tunnel point cloud mainly by three steps: axis acquisition, segments extraction and denoising. Tunnel axis is extracted by fitting boundaries of the tunnel point cloud rejection in plane with RANSAC algorithm. With guidance of axis, the entire preprocessed tunnel point cloud is segmented by equal division to get a section of tunnel point cloud which corresponds to a single tunnel segment. Then the noise in every single point cloud segment is removed by clustering algorithm twice, based on distance and intensity. Finally, clean point clouds of tunnel segments are processed by effective deformation extraction processor to get ovality and three-dimensional deformation nephogram.
Keywords
Light detection and ranging (LiDAR)； Automation, Circle tunnel； Tunnel deformation monitoring
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
