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Sensors 2016, 16(10), 1648; doi:10.3390/s16101648

Automatic Extraction of Tunnel Lining Cross-Sections from Terrestrial Laser Scanning Point Clouds

MOE Key Laboratory of Transportation Tunnel Engineering, Southwest Jiaotong University, Chengdu 610031, China
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Received: 25 July 2016 / Accepted: 30 September 2016 / Published: 6 October 2016
(This article belongs to the Section Remote Sensors)
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Abstract

Tunnel lining (bare-lining) cross-sections play an important role in analyzing deformations of tunnel linings. The goal of this paper is to develop an automatic method for extracting bare-lining cross-sections from terrestrial laser scanning (TLS) point clouds. First, the combination of a 2D projection strategy and angle criterion is used for tunnel boundary point detection, from which we estimate the two boundary lines in the X-Y plane. The initial direction of the cross-sectional plane is defined to be orthogonal to one of the two boundary lines. In order to compute the final cross-sectional plane, the direction is adjusted twice with the total least squares method and Rodrigues' rotation formula, respectively. The projection of nearby points is made onto the adjusted plane to generate tunnel cross-sections. Finally, we present a filtering algorithm (similar to the idea of the morphological erosion) to remove the non-lining points in the cross-section. The proposed method was implemented on railway tunnel data collected in Sichuan, China. Compared with an existing method of cross-sectional extraction, the proposed method can offer high accuracy and more reliable cross-sectional modeling. We also evaluated Type I and Type II errors of the proposed filter, at the same time, which gave suggestions on the parameter selection of the filter. View Full-Text
Keywords: cross-section; filtering; terrestrial laser scanning; tunnel; point cloud cross-section; filtering; terrestrial laser scanning; tunnel; point cloud
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MDPI and ACS Style

Cheng, Y.-J.; Qiu, W.; Lei, J. Automatic Extraction of Tunnel Lining Cross-Sections from Terrestrial Laser Scanning Point Clouds. Sensors 2016, 16, 1648.

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