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Pose estimation in automated visual inspection using ANN.

S Hati1, S Sengupta

  • 1Institute for InfoComm Research (I2R), Singapore. subhas@izr.a-star.edu.sg

International Journal of Neural Systems
|January 16, 2003
PubMed
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This study introduces a novel Artificial Neural Network (ANN) for accurate 3D object pose estimation in automated visual inspection. The ANN-based pose estimator demonstrates robustness to dimensional variations, improving accuracy for industrial applications.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Automated Visual Inspection (AVI) requires precise object pose estimation.
  • Classical methods often struggle with variations in object dimensions and pose.
  • Developing robust pose estimators is crucial for industrial automation.

Purpose of the Study:

  • To present an accurate and robust pose estimator for rigid, polyhedral objects using Artificial Neural Networks (ANNs).
  • To develop a pose estimator that is resilient to dimensional deviations within tolerance ranges.
  • To offer an alternative to classical pose estimation techniques in AVI.

Main Methods:

  • Utilized Artificial Neural Networks (ANNs) for pose estimation.
  • Trained the ANN with objects in various poses and within-tolerance dimensional deviations.

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  • Conducted computer simulation experiments using existing ANN configurations.

    • Main Results:

    • Achieved pose estimation accuracy better than 4% of the placement error in simulations.
    • Demonstrated robustness to within-tolerance dimensional errors.
    • The ANN-based approach showed significant advantages over classical methods.

    Conclusions:

    • The proposed ANN-based pose estimator is accurate and robust for polyhedral objects.
    • The method is suitable for Automated Visual Inspection (AVI) applications.
    • ANNs offer a promising alternative for industrial pose estimation tasks.