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Research on Student's T-Distribution Point Cloud Registration Algorithm Based on Local Features.

Houpeng Sun1, Yingchun Li2, Huichao Guo2

  • 1Graduate School, Space Engineering University, Beijing 101416, China.

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|August 10, 2024
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Summary
This summary is machine-generated.

This study introduces a new algorithm for processing 3D point cloud data from LiDAR, improving registration accuracy and robustness by using Student's t-distribution. The method effectively handles noise and data loss common in LiDAR applications.

Keywords:
Student’s t-distribution mixture modeladaptive penaltiescomposite weight coefficientlocal featurespoint cloud registration

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Area of Science:

  • Geospatial technology
  • Computer vision
  • Robotics

Background:

  • LiDAR (Light Detection and Ranging) is crucial for autonomous systems and mapping.
  • LiDAR 3D point cloud registration faces challenges like noise, data loss, and disorder.

Purpose of the Study:

  • To develop a novel point cloud registration algorithm for LiDAR data.
  • To address noise, data loss, and disorder in 3D point cloud registration.

Main Methods:

  • Utilized Student's t-distribution mixture model (SMM) for probability distribution.
  • Incorporated local point cloud features for objective function design.
  • Employed adaptive point-to-point and point-to-plane distance penalties.
  • Added a composite weight coefficient based on LiDAR imaging characteristics.

Main Results:

  • The proposed algorithm demonstrated high practicability and dependability.
  • Outperformed five comparison algorithms in accuracy and robustness.
  • Successfully handled missing data and data disorder in point clouds.

Conclusions:

  • The novel Student's t-distribution algorithm enhances LiDAR 3D point cloud registration.
  • The method offers improved accuracy and robustness for applications like autonomous driving and urban planning.