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Pairwise Registration Algorithm for Large-Scale Planar Point Cloud Used in Flatness Measurement.

Zichao Shu1, Songxiao Cao1, Qing Jiang1

  • 1College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China.

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|July 24, 2021
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Summary
This summary is machine-generated.

This study introduces an optimized 3D point cloud registration algorithm for precise flatness measurement using laser profilometry. The method enhances accuracy and speed in six-degrees-of-freedom (6-DoF) pose estimation for large-scale planar data.

Keywords:
3D point cloud registrationboundary extractionboundary-based registrationflatness measurement

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Area of Science:

  • Computer Vision
  • Metrology
  • Geospatial Data Analysis

Background:

  • Accurate flatness measurement is critical in various industrial applications.
  • Existing 3D point cloud registration methods can struggle with large-scale planar datasets, impacting measurement precision.
  • Laser profilometry generates dense point clouds requiring robust processing for metrology.

Purpose of the Study:

  • To develop and validate an optimized 3D pairwise point cloud registration algorithm for precise flatness measurement.
  • To achieve fast and accurate six-degrees-of-freedom (6-DoF) pose estimation of large-scale planar point clouds.
  • To improve the performance of flatness measurements obtained via laser profilometry.

Main Methods:

  • Proposed an algorithm that extracts point cloud boundaries for enhanced feature descriptors.
  • Implemented neighborhood evaluation to eliminate invalid keypoints and establish initial matching pairs.
  • Utilized clustering with geometric consistency constraints for coarse registration, followed by Iterative Closest Point (ICP) for fine registration.

Main Results:

  • The proposed algorithm demonstrated superior boundary extraction compared to existing methods.
  • Experimental results confirmed enhanced registration performance, leading to more precise flatness measurements.
  • The algorithm achieved fast and accurate 6-DoF pose estimation for large-scale planar point clouds.

Conclusions:

  • The optimized 3D point cloud registration algorithm effectively improves flatness measurement accuracy and efficiency.
  • Boundary extraction and geometric consistency constraints are key to robust registration of planar point clouds.
  • The developed method offers a significant advancement for metrology applications relying on laser profilometry data.