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 OPEN ACCESSRemote Sens. 2014, 6 858 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).

ABSTRACT:The deformation monitoring of subway tunnel is of extraordinary necessity. Therefore, a method for deformation monitoring based on terrestrial point clouds is proposed in this paper. First, the traditional adjacent stations registration is replaced by sectioncontrolled registration, so that the common control points can be used by each station and thus the error accumulation avoided within a section. Afterwards, the central axis of the subway tunnel is determined through RANSAC (Random Sample Consensus) algorithm and curve fitting. Although with very high resolution, laser points are still discrete and thus the vertical section is computed via the quadric fitting of the vicinity of interest, instead of the fitting of the whole model of a subway tunnel, which is determined by the intersection line rotated about the central axis of tunnel within a vertical plane. The extraction of the vertical section is then optimized using RANSAC for the purpose of filtering out noises. Based on the extracted vertical sections, the volume of tunnel deformation is estimated by the comparison between vertical sections extracted at the same position from different epochs of point clouds. Furthermore, the continuously extracted vertical sections are deployed to evaluate the convergent tendency of the tunnel. The proposed algorithms are verified using real datasets in terms of accuracy and computation efficiency. The experimental result of fitting accuracy analysis shows the maximum deviation between interpolated point and real point is 1.5mm, and the minimum one is 0.1mm; the convergent tendency of the tunnel was detected by the comparison of adjacent fitting radius. The maximum error is 6mm, while the minimum one is 1mm. The computation cost of vertical section abstraction is within 3 seconds/section, which proves high efficiency..