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Affine Iterative Closest Point Algorithm Based on Color Information and Correntropy for Precise Point Set

Lexian Liang1, Hailong Pei1

  • 1Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong, South China University of Technology, Guangzhou 510640, China.

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Summary
This summary is machine-generated.

This study introduces a new color and correntropy-based affine iterative closest point algorithm. It significantly improves registration accuracy and robustness for noisy RGB-D datasets with small deformations.

Keywords:
RGB-D datacolor Informationcorrentropyiterative closest pointpoint set registration

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Area of Science:

  • Computer Vision
  • Robotics
  • 3D Data Processing

Background:

  • Accurate 3D data registration is crucial for many applications.
  • Traditional methods struggle with noise, outliers, and weak geometric structures in RGB-D datasets.
  • Small deformations in datasets further complicate registration accuracy.

Purpose of the Study:

  • To develop a novel affine iterative closest point algorithm for RGB-D datasets.
  • To enhance registration accuracy and robustness in the presence of noise and outliers.
  • To address challenges posed by weak geometric structures and small deformations.

Main Methods:

  • Integration of color features into traditional affine algorithms for improved correspondences.
  • Introduction of correntropy measurement to mitigate the impact of noise and outliers.
  • Development of an affine iterative closest point algorithm leveraging both color and correntropy.

Main Results:

  • The proposed algorithm achieves significantly higher registration accuracy compared to existing methods.
  • Experimental results show an error reduction of approximately 10 times.
  • Demonstrated superior and stable robustness against noise and outliers in RGB-D data.

Conclusions:

  • The novel algorithm effectively handles registration problems in RGB-D datasets with noise, outliers, and small deformations.
  • Combining color information and correntropy offers a robust solution for 3D point cloud registration.
  • The method presents a substantial improvement over current advanced registration algorithms.