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Updated: May 28, 2025

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Fast Registration Algorithm for Laser Point Cloud Based on 3D-SIFT Features.

Lihong Yang1, Shunqin Xu1, Zhiqiang Yang1

  • 1School of Photoelectric Engineering, Xi'an Technological University, Xi'an 710021, China.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a fast laser point cloud registration algorithm using 3D scale-invariant feature transform (3D-SIFT) and point-to-plane ICP. The method significantly enhances registration speed and accuracy compared to traditional algorithms.

Keywords:
3D-SIFT feature extractionfast point feature histogramiterative closest pointpoint cloud registrationsampling consensussymmetric target function

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Area of Science:

  • Computer Vision
  • Robotics
  • Geomatics

Background:

  • Traditional iterative closest point (ICP) algorithms suffer from slow convergence and local optima.
  • Efficient point cloud registration is crucial for applications in autonomous driving, surveying, and augmented reality.

Purpose of the Study:

  • To develop a fast and accurate laser point cloud registration algorithm.
  • To overcome the limitations of traditional ICP methods in terms of speed and convergence.

Main Methods:

  • Feature extraction using normal vector thresholding and 3D scale-invariant feature transform (3D-SIFT).
  • Coarse registration with fast point feature histogram (FPFH) and sample consensus initial alignment (SAC-IA).
  • Fine registration using point-to-plane ICP with a symmetric target function.

Main Results:

  • The proposed algorithm significantly improves registration efficiency.
  • Achieved a 29.55% increase in registration accuracy and an 81.01% reduction in time on a public dataset.
  • Demonstrated a 41.72% accuracy increase and a 67.65% time reduction on collected data.

Conclusions:

  • The algorithm achieves high registration accuracy while substantially reducing processing time.
  • This method offers a practical solution for real-time point cloud registration tasks.
  • The combination of 3D-SIFT, FPFH, SAC-IA, and point-to-plane ICP provides a robust registration framework.