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Real-Time Plane Detection with Consistency from Point Cloud Sequences.

Sensors (Basel, Switzerland)·2020

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Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud.

Cheng Yi¹, Dening Lu¹, Qian Xie¹

¹College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China.

Sensors (Basel, Switzerland)

|December 2, 2020

Summary

Accurate tunnel deformation inspection is achieved by extracting the spatial axis from raw LiDAR point clouds. This method iteratively fits cross-sections to refine the axis, enabling precise structural stability assessments.

Keywords:

LiDAR sensor central axis extraction cross section raw LiDAR data tunnel deformation

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Area of Science:

Geotechnical Engineering
Civil Engineering
Geomatics Engineering

Background:

Global inspection of large-scale tunnels is crucial for structural stability and driving safety.
LiDAR scanners provide 3D point clouds for tunnel deformation inspection (TDI).
Raw LiDAR data often suffers from occlusions, missing data, and noise, hindering accurate analysis.

Purpose of the Study:

To develop an effective algorithm for tunnel deformation inspection using poor-quality 3D point clouds.
To accurately extract the global spatial axis of a tunnel from noisy LiDAR data.
To enable automated computation of tunnel deformation parameters.

Main Methods:

Proposed an iterative fitting optimization algorithm to extract the tunnel's spatial axis.
Generated normal planes in the Frenet frame to create cross-sections from the point cloud.
Fitted circles to cross-sections and approximated centers with a B-Spline curve iteratively until convergence.
Designed a segmentation approach for cross-sections to compute deformation parameters.

Main Results:

Successfully extracted the accurate spatial axis of tunnels even from poor-quality point clouds.
Demonstrated the feasibility and effectiveness of the proposed tunnel deformation inspection method through experiments.
Enabled automated computation of various tunnel deformation inspection parameters.

Conclusions:

The developed algorithm effectively extracts the spatial axis from noisy tunnel point clouds.
The method provides a robust solution for tunnel deformation inspection, enhancing structural safety.
This approach facilitates automated and accurate assessment of tunnel integrity.