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Subset-based stereo calibration method optimizing triangulation accuracy.

Oleksandr Semeniuta1

  • 1Department of Manufacturing and Civil Engineering, Norwegian University of Science and Technology, Gjøvik, Norway.

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This study introduces a new subset-based method for stereo calibration using chessboard patterns. It improves 3D reconstruction accuracy by selecting optimal calibration parameters from image pairs.

Keywords:
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Area of Science:

  • Computer Vision
  • Robotics
  • Metrology

Background:

  • Accurate stereo calibration is crucial for 3D reconstruction in machine vision systems.
  • Traditional chessboard calibration relies on random data collection, leading to variable results.
  • Existing methods lack robustness against suboptimal image pair selection.

Purpose of the Study:

  • To present a robust subset-based approach for camera and stereo calibration.
  • To enhance the reliability and accuracy of 3D reconstruction through improved calibration.
  • To develop a method for selecting optimal calibration parameters from a dataset of image pairs.

Main Methods:

  • Implemented a subset-based calibration strategy using OpenCV.
  • Utilized multiple calibration runs on randomly selected image subsets.
  • Evaluated calibration performance using triangulation metrics and feature analysis.
  • Applied Principal Component Analysis and clustering for parameter selection.

Main Results:

  • Demonstrated the ability to identify superior calibration parameters from diverse image sets.
  • Quantified the improvement in calibration accuracy using the proposed metric-based evaluation.
  • Successfully selected optimal parameters leading to more reliable 3D reconstructions.
  • Validated the method on industrial camera datasets with chessboard patterns.

Conclusions:

  • The proposed subset-based approach offers a more reliable and accurate method for stereo calibration.
  • This technique mitigates the weaknesses associated with random data collection in traditional calibration.
  • The findings are significant for applications requiring precise 3D measurements from stereo vision systems.