Continuous Extraction of Subway Tunnel Cross Sections Based on Terrestrial Point Clouds

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 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).

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

“…The use of the TLS technique has also become popular in tunnel engineering due to the various advantages over conventional geodetic devices, such as laser beam profilers and total stations, which take more time to acquire data [12] and cannot offer high- density 3D datasets. The existing applications of TLS for tunnels contain geological feature detection [13], deformation analysis [14,15,16], and cross-sectional extraction [12,17], and the automatic processing of tunnel point clouds has received increasing research attention [12,18]. …”

Automatic Monitoring of Tunnel Deformation Based on High Density Point Clouds Data

“…And then, the method of Kang (Kang et al, 2014) is used to calculate the projection point set of center axis in XOY plane named Uxoy.…”

“…In order to improve the accuracy of the central axis, a method of Kang (Kang et al, 2014) And then the method of 2.1 is used to calculate the central axis point set Uyoz. In order to avoid the problem that the point clouds are sheltered by device in data acquisition process, we moved the point set Uxoy to the left (or right) for a certain distance τ before searching for the highest and lowest points (the red arrow shown in the figure), and the size of τ must bigger than radius of the orbit and smaller than inner radius of tunnel.…”

“…Carrying out precise, fast and automated deformation monitoring of the subway, especially the subway in operation is so necessary. Although the accuracy of traditional deformation monitoring method (such as total station, convergence meter and so on) for subway tunnel is MMS can obtain absolute coordinates of point cloud directly, it is usually need to rotate the tunnel point clouds and make it parallel to a certain axis before projecting them onto 2D plane (Kang et al, 2014), the process is eliminated in our method and the amount of computation is reduced. Secondly, the construction speed of cross sectional point set is increased by constructing the buffer of each section in the method of k-nearest neighbor.…”

Shiftable Leading Point Method for High Accuracy Registration of Airborne and Terrestrial LiDAR Data

“…A variety of LiDAR techniques have emerged, and LiDAR data are often acquired from various platforms, for example airborne [2,3], mobile [4,5] and terrestrial platforms [6,7]. Acquiring data from a side view, terrestrial LiDAR can obtain detailed facade information of objects at relatively high resolution.…”