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Updated: May 21, 2025

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Pose Estimation of Coil Workpieces by Automated Overhead Cranes Using an Improved Point Pair Features Algorithm.

Yongbo Zhuang1, Jianli Man1, Yuchen Jiang1

  • 1College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
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This summary is machine-generated.

This study introduces a new 3D point cloud algorithm for automated crane operations, improving coil handling efficiency in ports. The enhanced method achieves accurate pose estimation for coil workpieces, meeting practical application needs.

Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Logistics and Supply Chain Management

Background:

  • Automating port operations like warehousing and transshipment is crucial for efficiency.
  • Traditional methods for coil workpiece pose estimation have limitations.

Purpose of the Study:

  • To develop an automated system for crane operations using 3D point cloud algorithms.
  • To improve the pose estimation accuracy and robustness for coil workpieces.

Main Methods:

  • A novel point cloud registration algorithm combining Point Pair Feature (PPF) and Generalized Iterative Closest Point (GICP) algorithms.
  • Utilizing 3D point cloud data for precise pose estimation of coil workpieces.

Main Results:

Keywords:
coil workpiecesgeneralized ICPpoint cloud processingpoint pair featurespose estimation

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  • The proposed algorithm demonstrated superior performance compared to traditional methods.
  • Achieved average pose estimation errors of 1.1%–1.2% for one to three coils.
  • Processing times ranged from 3.4 to 4.7 seconds, meeting practical requirements.

    • Conclusions:

    • The novel algorithm enhances the robustness and applicability of point cloud registration.
    • The developed method effectively supports automated crane operations for coil handling in ports.