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Related Experiment Video

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Point Pair Feature-Based Pose Estimation with Multiple Edge Appearance Models (PPF-MEAM) for Robotic Bin Picking.

Diyi Liu1, Shogo Arai2, Jiaqi Miao3

  • 1Graduate School of Engineering, Tohoku University, Aramaki Aza Aoba 6-6-01, Aoba-Ku, Sendai 980-8579, Japan. liu@irs.mech.tohoku.ac.jp.

Sensors (Basel, Switzerland)
|August 22, 2018
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Summary
This summary is machine-generated.

This study introduces a new pose estimation method, Boundary-to-Boundary-using-Tangent-Line (B2B-TL), for robotic bin picking. It accurately locates industrial parts even with incomplete 3D data, enabling reliable robot manipulation.

Keywords:
Boundary-to-Boundary-using-Tangent-Line (B2B-TL)Multiple Edge Appearance Models (MEAM)pose estimationrobotic bin picking

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Area of Science:

  • Robotics
  • Computer Vision
  • Industrial Automation

Background:

  • Robotic bin picking requires accurate object pose estimation for successful manipulation.
  • Existing methods struggle with industrial parts lacking detailed 3D point cloud data.

Purpose of the Study:

  • To develop a novel pose estimation descriptor, Boundary-to-Boundary-using-Tangent-Line (B2B-TL), for robust robotic bin picking.
  • To improve recognition rates and reduce computation time using Multiple Edge Appearance Models (MEAM).

Main Methods:

  • Utilizing a novel point pair feature descriptor (B2B-TL) that combines 3D point cloud and 2D image data.
  • Implementing Multiple Edge Appearance Models (MEAM) for enhanced object recognition.
  • Developing an online computation pipeline for real-time application.

Main Results:

  • The B2B-TL descriptor effectively compensates for missing details in 3D point clouds using 2D image data.
  • The integrated B2B-TL and MEAM approach demonstrated high accuracy in pose estimation for industrial parts.
  • The system achieved sufficient speed for real-time application in factory environments.

Conclusions:

  • The proposed B2B-TL descriptor and MEAM method provide an accurate and efficient solution for robotic bin picking.
  • This approach enables reliable grasping of industrial parts, even those with incomplete 3D data.
  • The developed online pipeline is suitable for practical implementation in industrial settings.