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Clique-like Point Cloud Registration: A Flexible Sampling Registration Method Based on Clique-like for

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  • 1School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China.

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Summary

This study introduces CL-PCR, a novel 3D point cloud registration method. It enhances accuracy and robustness, especially with low overlap and outliers, outperforming existing techniques.

Keywords:
3D sensor perceptionclique-likepoint cloud registration

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Area of Science:

  • Computer Vision
  • Robotics
  • 3D Perception

Background:

  • 3D point cloud registration is crucial for sensor-based 3D perception.
  • Traditional methods like RANSAC struggle with low overlap, outliers, and computational cost.

Purpose of the Study:

  • To develop a novel 3D registration method, CL-PCR, to overcome limitations of existing techniques.
  • To improve robustness against low overlap and outliers in point cloud registration.

Main Methods:

  • Introduced CL-PCR, a novel method based on maximal cliques and the SC^2-PCR framework.
  • Constructed a graph matrix for correspondence compatibility and used clique-like subsets for consensus.
  • Employed SVD for transformation hypothesis computation and selected the best based on evaluation metrics.

Main Results:

  • CL-PCR demonstrated superior registration performance, particularly Fast-CL-PCRv1, on 3DMatch/3DLoMatch datasets.
  • The method showed enhanced robustness against low overlap and reduced outlier influence.
  • Validated effectiveness and practicality with real-world data.

Conclusions:

  • CL-PCR offers a robust and accurate solution for 3D point cloud registration.
  • The method's ability to leverage smaller sampling subsets improves performance in challenging environments.
  • CL-PCR represents a significant advancement in 3D perception for sensors.