Abstract

Automation is an inevitable trend in the development of tunnel shotcrete machinery. Tunnel environmental perception based on 3D LiDAR point cloud has become a research hotspot. Current researches about the detection of tunnel point clouds focus on the completed tunnel with a smooth surface. However, few people have researched the automatic detection method for steel arches installed on a complex rock surface. This paper presents a novel algorithm to extract tunnel steel arches. Firstly, we propose a refined function for calibrating the tunnel axis by minimizing the density variance of the projected point cloud. Secondly, we segment the rock surface from the tunnel point cloud by using the region-growing method with the parameters obtained by analyzing the tunnel section sequence. Finally, a Directed Edge Growing (DEG) method is proposed to detect steel arches on the rock surface in the tunnel. Our experiment in the highway tunnels under construction in Changsha (China) shows that the proposed algorithm can effectively extract the points of the edge of steel arches from 3D LiDAR point cloud of the tunnel without manual assistance. The results demonstrated that the proposed algorithm achieved 92.1% of precision, 89.1% of recall, and 90.6% of the F-score.

Highlights

  • In recent years, the demand for intelligent construction machinery used in a harsh environment has been constantly increasing [1]

  • We studied the problem of extracting steel arches from a 3D LiDAR point cloud of variable cross-section tunnels, such as round tunnels used for transportation and square tunnels used in coal mines

  • We calibrate the tunnel axis of the point cloud in the world coordinate system by minimizing the Rotational Projection Density Variance (RPDV), which is profile-insusceptible to the tunnel; we propose an adaptive threshold extraction algorithm to extract the rock surface by using the Differential Analysis for the Section Sequences of the Tunnel point cloud (DASST); and we propose a Directed Edge Growing (DEG) method to extract the steel arches edge from the rock surface, using the local normal saliency as the evaluation index of candidate points

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Summary

Introduction

The demand for intelligent construction machinery used in a harsh environment has been constantly increasing [1]. The rock surface is one of the most complex sections in the tunnel surface point cloud (Figure 1c) to realize 3D reconstruction and detection. Since the steel arch area has continuous geometric characteristics, it can be considered as a 3D edge feature according to the definition of the 3D edge of Ni et al [4]. The point cloud of the steel arch areas (the red points in Figure 3) has unique characteristics, which are different from other kinds of feature lines, such as the road edge [5,6], building outline [7,8], and components edge [9]. Since the point cloud of each arch area is a continuous line of the point cloud (as shown in Figures 1d and 3), the bottom edge of the arch can be viewed as an exterior boundary [10] of the point cloud

Related Works
Contributions
Data Acquisition Scenario
Acquisition Equipment
Limitations
Pre-Processing of the Data
Data Set and Basic Parameters
Overview of the Proposed Algorithm
Orientation Calibration
Rotation matrix
Voxelized point cloud
The density variance of the point cloud
Curvature of the Point Cloud
DASST Used for Region-Growing
Feasible Methods
Method Principle
Steel Arch Extraction Based on DEG
Experiment
Qualitative Analysis
Quantitative Analysis
Anti-Noise Performance
Findings
Conclusions and Future Work

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