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Remote Sens. 2019, 11(3), 297; https://doi.org/10.3390/rs11030297

A Flexible Architecture for Extracting Metro Tunnel Cross Sections from Terrestrial Laser Scanning Point Clouds

1
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
2
Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
3
School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
4
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
5
School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
6
The State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Received: 5 January 2019 / Revised: 27 January 2019 / Accepted: 29 January 2019 / Published: 1 February 2019
(This article belongs to the Special Issue Mobile Mapping Technologies)
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Abstract

This paper presents a novel framework to extract metro tunnel cross sections (profiles) from Terrestrial Laser Scanning point clouds. The entire framework consists of two steps: tunnel central axis extraction and cross section determination. In tunnel central extraction, we propose a slice-based method to obtain an initial central axis, which is further divided into linear and nonlinear circular segments by an enhanced Random Sample Consensus (RANSAC) tunnel axis segmentation algorithm. This algorithm transforms the problem of hybrid linear and nonlinear segment extraction into a sole segmentation of linear elements defined at the tangent space rather than raw data space, significantly simplifying the tunnel axis segmentation. The extracted axis segments are then provided as input to the step of the cross section determination which generates the coarse cross-sectional points by intersecting a series of straight lines that rotate orthogonally around the tunnel axis with their local fitted quadric surface, i.e., cylindrical surface. These generated profile points are further refined and densified via solving a constrained nonlinear least squares problem. Our experiments on Nanjing metro tunnel show that the cross sectional fitting error is only 1.69 mm. Compared with the designed radius of the metro tunnel, the RMSE (Root Mean Square Error) of extracted cross sections’ radii only keeps 1.60 mm. We also test our algorithm on another metro tunnel in Shanghai, and the results show that the RMSE of radii only keeps 4.60 mm which is superior to a state-of-the-art method of 6.00 mm. Apart from the accurate geometry, our approach can maintain the correct topology among cross sections, thereby guaranteeing the production of geometric tunnel model without crack defects. Moreover, we prove that our algorithm is insensitive to the missing data and point density. View Full-Text
Keywords: terrestrial laser scanning; tunnel central axis; tunnel cross section; enhanced RANSAC; quadric fitting; constrained nonlinear least-squares problem terrestrial laser scanning; tunnel central axis; tunnel cross section; enhanced RANSAC; quadric fitting; constrained nonlinear least-squares problem
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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 (CC BY 4.0).
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Cao, Z.; Chen, D.; Shi, Y.; Zhang, Z.; Jin, F.; Yun, T.; Xu, S.; Kang, Z.; Zhang, L. A Flexible Architecture for Extracting Metro Tunnel Cross Sections from Terrestrial Laser Scanning Point Clouds. Remote Sens. 2019, 11, 297.

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