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).
Highlights
Because underground structures such as tunnels require routine inspections and maintenance for their optimal use, efficient and accurate tunnel inspections are necessary
An interpolation algorithm based on quadric parametric surface fitting using an improved BaySAC
(Bayesian SAmpling Consensus) algorithm was proposed to compute a cross-sectional point when no cross-sectional point could be directly acquired from the tunnel points along the direction of interest
Summary
Because underground structures such as tunnels require routine inspections and maintenance for their optimal use, efficient and accurate tunnel inspections are necessary. The shape, width and area of the cross sections of constructed or natural tunnels can be used to determine their structural stability These estimations are generally conducted based on sparsely sampled points that are surveyed using a total station. The second method determines the cross section based only on a subset of the point cloud, which forms a thin, sliced, solid body In this case, the vertical plane that defines the cross section is determined in terms of the central axis of the tunnel, and the cross section is developed using a poly-line string that results from projecting the point-cloud data in the sliced body onto the plane of the desired cross section [15].
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