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A method of partially overlapping point clouds registration based on differential evolution algorithm.

Xuetao Zhang1,2, Ben Yang1,2, Yunhao Li2

  • 1National Engineering Laboratory for Visual Information Processing and Applications, Xi'an, Shaanxi 710049, China.

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|December 22, 2018
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Summary
This summary is machine-generated.

This study introduces a Differential Evolution algorithm for 3D point cloud registration, overcoming local optima common in the Trimmed Iterative Closest Point method. The new approach offers a more robust and efficient global optimization solution.

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Area of Science:

  • Computer Vision
  • Robotics
  • Geometric Computing

Background:

  • 3D point cloud registration is crucial for applications like 3D reconstruction and object detection.
  • The Trimmed Iterative Closest Point (TICP) algorithm is widely used but susceptible to local optima due to initial value dependency.

Purpose of the Study:

  • To adapt the Differential Evolution (DE) algorithm for global optimal 3D point cloud registration.
  • To enhance population distribution and prevent convergence to local optima in registration processes.

Main Methods:

  • Implementing Differential Evolution (DE) with tailored evolutionary operations for point cloud registration.
  • Comparing the proposed DE-based method against Genetic Algorithms (GA) and particle filters for global optimization.

Main Results:

  • The DE-based algorithm demonstrates superior robustness compared to existing global optimization methods.
  • The proposed method achieves good registration results in fewer generations, indicating higher efficiency.

Conclusions:

  • Differential Evolution provides an effective global optimization strategy for 3D point cloud registration.
  • The adapted DE algorithm overcomes the limitations of TICP, offering improved performance and convergence speed.