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Improved Registration Algorithm Based on Double Threshold Feature Extraction and Distance Disparity Matrix.

Biao Wang1, Jie Zhou1, Yan Huang1

  • 1School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230009, China.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary

This study introduces an improved 3D point cloud registration algorithm using double threshold feature extraction and a distance disparity matrix (DDM). The method enhances efficiency and precision for large datasets in 3D measurement systems.

Keywords:
ICP algorithmdistance disparity matrixdouble thresholdfeature extractionmachine visionpoint cloud registration

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Area of Science:

  • Computer Vision
  • Computational Geometry
  • Metrology

Background:

  • Noncontact measurement methods like machine vision capture complex objects incompletely from a single direction.
  • Acquiring and registering point clouds from multiple directions is essential for complete 3D reconstruction.
  • High efficiency and precision are critical challenges in registering large-scale point cloud data.

Purpose of the Study:

  • To develop an improved registration algorithm for large-scale 3D point clouds.
  • To enhance the efficiency and precision of 3D object registration.
  • To address limitations in current point cloud registration techniques for complex objects.

Main Methods:

  • Feature points extracted using double thresholds (normal vectors and curvature) for dimensionality reduction.
  • Fast Point Feature Histogram (FPFH) used for describing feature points and initial pair generation.
  • Distance Disparity Matrix (DDM) and Euclidean invariant features employed to eliminate incorrect point pairs.
  • Sample Consensus Initial Alignment (SAC-IA) and Iterative Closest Point (ICP) algorithms for final registration.

Main Results:

  • The proposed algorithm demonstrates rapid processing of large point cloud datasets.
  • Achieved efficient and precise matching of target objects.
  • Successfully reduced the number of points through effective feature extraction.

Conclusions:

  • The developed algorithm offers a significant improvement in the efficiency and precision of 3D point cloud registration.
  • It is well-suited for applications in distributed or mobile 3D measurement systems.
  • The method effectively handles large datasets and complex object geometries.