Abstract

Abstract. In this paper, a method to model a tunnel using lidar points is presented. The data used was collected using Leica Pegasus Two Ultimate with a Z+F 9012 Profiler mounted on a mobile platform. The tunnel was approximately 151 m long. Visual inspection of a cross-section of the tunnel showed two rail tracks supported on ballast and sidewalks along both sidewalls of the tunnel. The walls and the ceiling of the tunnel were made of five planar surfaces. The tunnel alignment was straight, without any horizontal or vertical curves. The bearing of the central axis of the tunnel was N12.2oW. The following methodology was developed to model just the planar surfaces of the tunnel by excluding the rails, ballast, sidewalks, powerlines, and other accessories. The entire methodology was divided into three broad parts. In the first part, a model cross-section was created. Since the design plans of the tunnel were not available, the model cross-section polyline was created using mean tunnel dimensions from random cross-section points. The model cross-section consisted of the walls and the ceiling of the tunnel. Points were placed at every 1 cm along the model polyline. Six of the model points that represented the shape of the tunnel were selected as salient points. The lower-left salient point was considered as the seed point. In the second part, to define a reference axis of the tunnel, an approximate centerline was manually defined by selecting points at its start and end. Lidar points within 1 m at the start and the end of the tunnel were modeled using the model points to determine the centroids. The reference axis was determined by connecting the centroids at the start and the end of the tunnel. In the third part, the tunnel points were sliced along the reference axis at 5 cm intervals. The model cross-section was matched to points within each tunnel slice using a three-stage approach. In the first stage, the pattern of salient points was matched to the tunnel points by placing the seed point at every tunnel point location. The distances between salient points and their nearest tunnel points were calculated. Ten sets of tunnel points with the least differences to the salient points were shortlisted. In the second stage, a dense point-to-point matching was performed between the model and sliced tunnel data at the shortlisted points. The shortlisted point location with the least difference between the tunnel and the model points was considered as a match. At this point, the model points were hinged to the tunnel points at the seed point location. Hence, in the last stage, a six-parameter affine transformation was performed to match the model points to the tunnel data. The transformed model points at every 5 cm of the length of the tunnel were considered as current shape of the tunnel.

Highlights

  • Transportation networks are arteries of any nation

  • It consists of high-precision lidar (Light Detection and Ranging) sensors for data collection with GPS and Inertial Measurement Units (IMU) mounted on a mobile platform such as a trail or a car

  • Lidar points within 0.5 m width at the inflection points (as described in Figure 5 (b)) were matched with the template points. The centroids of these two slices of data were used to define the actual centreline of the tunnel

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Summary

Introduction

Transportation networks are arteries of any nation. Maintaining transportation infrastructure effectively is essential for the smooth functioning of any nation. Periodic monitoring of transportation infrastructure using traditional surveying techniques and instruments would require a significant amount of time and human efforts. Such methods could be used at sample locations may not provide details to greater resolutions. A Mobile Mapping System (MMS) has developed into a mature technology allowing the acquisition of 3D data on a very large scale. It consists of high-precision lidar (Light Detection and Ranging) sensors for data collection with GPS and Inertial Measurement Units (IMU) mounted on a mobile platform such as a trail or a car. Periodic monitoring can be achieved by adopting such newer and faster technology

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