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Continuous Extraction of Subway Tunnel Cross Sections Based on Terrestrial Point Clouds
School of Land Science and Technology, China University of Geosciences, No. 29 Xueyuan Road, Haidian District, Beijing 100083, China
The Research Center for Remote Sensing and GIS, School of Geography, Beijing Normal University, No. 19, XinJieKouWai St., Haidian District, Beijing 100875, China
TIANDITU Co. Ltd., TIANDITU Building, National Geographic Information Technology Industrial Park, Air CBD, No. 2 Jichangdong Road, Shunyi District, Beijing 101300, China
Beijing Siwei Spatial Data Technology Co. Ltd., No. 16 North Taiping Road, Haidian District, Beijing 100039, China
* Author to whom correspondence should be addressed.
Received: 25 November 2013; in revised form: 7 January 2014 / Accepted: 7 January 2014 / Published: 15 January 2014
Abstract: An efficient method for the continuous extraction of subway tunnel cross sections using terrestrial point clouds is proposed. First, the continuous central axis of the tunnel is extracted using a 2D projection of the point cloud and curve fitting using the RANSAC (RANdom SAmple Consensus) algorithm, and the axis is optimized using a global extraction strategy based on segment-wise fitting. The cross-sectional planes, which are orthogonal to the central axis, are then determined for every interval. The cross-sectional points are extracted by intersecting straight lines that rotate orthogonally around the central axis within the cross-sectional plane with the tunnel point cloud. An interpolation algorithm based on quadric parametric surface fitting, using the BaySAC (Bayesian SAmpling Consensus) algorithm, is proposed to compute the cross-sectional point when it cannot be acquired directly from the tunnel points along the extraction direction of interest. Because the standard shape of the tunnel cross section is a circle, circle fitting is implemented using RANSAC to reduce the noise. The proposed approach is tested on terrestrial point clouds that cover a 150-m-long segment of a Shanghai subway tunnel, which were acquired using a LMS VZ-400 laser scanner. The results indicate that the proposed quadric parametric surface fitting using the optimized BaySAC achieves a higher overall fitting accuracy (0.9 mm) than the accuracy (1.6 mm) obtained by the plain RANSAC. The results also show that the proposed cross section extraction algorithm can achieve high accuracy (millimeter level, which was assessed by comparing the fitted radii with the designed radius of the cross section and comparing corresponding chord lengths in different cross sections) and high efficiency (less than 3 s/section on average).
Keywords: cross section; statistical testing; quadric parametric surface; random sample consensus; Bayesian sampling consensus; terrestrial laser scanning
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Cite This Article
MDPI and ACS Style
Kang, Z.; Zhang, L.; Tuo, L.; Wang, B.; Chen, J. Continuous Extraction of Subway Tunnel Cross Sections Based on Terrestrial Point Clouds. Remote Sens. 2014, 6, 857-879.
Kang Z, Zhang L, Tuo L, Wang B, Chen J. Continuous Extraction of Subway Tunnel Cross Sections Based on Terrestrial Point Clouds. Remote Sensing. 2014; 6(1):857-879.
Kang, Zhizhong; Zhang, Liqiang; Tuo, Lei; Wang, Baoqian; Chen, Jinlei. 2014. "Continuous Extraction of Subway Tunnel Cross Sections Based on Terrestrial Point Clouds." Remote Sens. 6, no. 1: 857-879.