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

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An Algorithm for Fitting Sphere Target of Terrestrial LiDAR.

Yintao Shi¹, Gang Zhao², Maomei Wang²

¹School of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, China.

Sensors (Basel, Switzerland)

|November 27, 2021

Summary

This summary is machine-generated.

This study introduces a novel finite random search algorithm for accurately fitting sphere targets in terrestrial LiDAR point clouds. The method achieves high precision, even with limited data coverage, enhancing 3D spatial measurements.

Keywords:

constraint space light detection and ranging parameters point cloud robustness sphere target

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Area of Science:

Geomatics Engineering
Computer Vision
Computational Geometry

Background:

Sphere targets are crucial for accurate 3D measurements in terrestrial LiDAR.
Precisely determining the geometric center of sphere targets from point cloud data is a significant challenge.

Purpose of the Study:

To develop a new finite random search algorithm for robust sphere target fitting.
To improve the accuracy and reliability of sphere center and radius estimation from point cloud data.

Main Methods:

A finite random search algorithm based on probability and statistics was proposed.
Parameter estimation was used to determine the initial and optimal sphere center and radius.
Iterative optimization refined the fitting accuracy for point cloud data.

Main Results:

The algorithm demonstrated effective sphere fitting across simulated and realistic point clouds.
High accuracy (within 0.01 mm) was achieved with over 30% point cloud coverage.
Accuracy of ±1 mm was maintained even with less than 20% coverage.

Conclusions:

The proposed finite random search algorithm offers a reliable solution for sphere target fitting in LiDAR.
The method provides accurate geometric center and radius estimation, adaptable to varying data conditions.
This advancement has significant implications for terrestrial LiDAR applications requiring precise spatial metrology.