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A Point Cloud Data-Driven Pallet Pose Estimation Method Using an Active Binocular Vision Sensor.

Yiping Shao1, Zhengshuai Fan1, Baochang Zhu2

  • 1College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

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|February 11, 2023
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Summary

This study introduces a new method for pallet pose estimation using binocular vision, improving accuracy and speed for automated industrial trucks. The approach enhances real-time performance in complex logistics environments.

Keywords:
adaptive Gaussian weight-based fast point feature histogramdriverless industrial truckspoint cloud registrationpose estimation

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Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Industrial Logistics

Background:

  • Automated fork pickup in driverless industrial trucks relies on accurate pallet pose estimation.
  • Existing methods struggle with real-time, high-accuracy performance in complex logistics environments due to data volume and environmental challenges.

Purpose of the Study:

  • To develop a robust and efficient pallet pose estimation method for driverless industrial trucks.
  • To address the limitations of current methods in terms of accuracy, speed, and robustness.

Main Methods:

  • A point cloud data-driven approach utilizing an active binocular vision sensor.
  • Implementation of point cloud preprocessing, Adaptive Gaussian Weight-based Fast Point Feature Histogram (AGW-FPFH) extraction, and point cloud registration.

Main Results:

  • The proposed AGW-FPFH method demonstrated superior performance compared to traditional Fast Point Feature Histogram and Signature of Histogram of Orientation.
  • Achieved over 35% improvement in accuracy and more than 30% reduction in feature extraction time.
  • Successfully enabled efficient and accurate pallet pose estimation for driverless industrial trucks.

Conclusions:

  • The developed method effectively overcomes the limitations of traditional techniques, offering enhanced robustness, speed, and accuracy.
  • The approach is validated as a superior solution for real-time pallet pose estimation in intelligent logistics.
  • This advancement contributes to the improved operational efficiency of automated guided vehicles in industrial settings.