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Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

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Automatic Tunnel Steel Arches Extraction Algorithm Based on 3D LiDAR Point Cloud.

Wenting Zhang¹, Wenjie Qiu², Di Song³

¹School of Automation, Central South University, Changsha 410083, China. zkxdoit@csu.edu.cn.

Sensors (Basel, Switzerland)

|September 22, 2019

Summary

This summary is machine-generated.

This study introduces an automated method for detecting steel arches in tunnels using 3D LiDAR point clouds, even on complex rock surfaces. The novel algorithm effectively extracts steel arch edges, improving tunnel construction monitoring.

Keywords:

3D LiDAR point cloud boundary detection region-growing tunnel

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Area of Science:

Civil Engineering
Robotics
Geospatial Data Analysis

Background:

Automation is crucial for tunnel construction machinery.
3D LiDAR point cloud analysis is a key area for tunnel environmental perception.
Existing methods struggle with detecting steel arches on complex rock surfaces.

Purpose of the Study:

To develop a novel algorithm for automatic detection of tunnel steel arches.
To address the challenge of detecting steel arches on complex rock surfaces in tunnels under construction.

Main Methods:

Refined tunnel axis calibration using projected point cloud density variance.
Rock surface segmentation via region-growing with analyzed section parameters.
Directed Edge Growing (DEG) method for steel arch edge detection.

Main Results:

The algorithm effectively extracts steel arch edges from 3D LiDAR point clouds.
Experiments in highway tunnels under construction demonstrated high performance.
Achieved 92.1% precision, 89.1% recall, and 90.6% F-score.

Conclusions:

The proposed algorithm enables effective, automated steel arch detection in tunnels.
This method advances tunnel construction monitoring and automation.
The Directed Edge Growing method shows promise for complex point cloud analysis.