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Evaluation of Keypoint Descriptors for Flight Simulator Cockpit Elements: WrightBroS Database.

Karolina Nurzynska1, Przemysław Skurowski1, Magdalena Pawlyta1

  • 1Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland.

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The WrightBroS project enhances flight simulator training with augmented reality smart glasses. Combining the ORB keypoint localization method and BRISK descriptor achieved nearly 96% accuracy in object recognition.

Keywords:
cockpit devices databasefeature vectors matchingkeypoint descriptorslocal feature classification

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Aerospace Engineering

Background:

  • Augmented reality (AR) systems require rapid object recognition for real-time data overlay.
  • Keypoint descriptors are crucial for accurate object identification in AR applications.
  • Pilot training simulators can benefit from enhanced visualization through AR.

Purpose of the Study:

  • To evaluate keypoint localization methods and descriptors for AR in flight simulators.
  • To identify the most accurate and efficient approach for real-time object recognition.
  • To support the WrightBroS project's goal of an AR-enhanced flight training system.

Main Methods:

  • A dedicated database of 27 flight simulator devices was created.
  • 12 keypoint localization methods and 10 keypoint descriptors were compared.
  • Performance was assessed based on computation time and classification accuracy.

Main Results:

  • The combination of the Oriented FAST and Rotated BRIEF (ORB) method for keypoint localization and the Binary Robust Independent Elementary Features (BRISK) descriptor yielded the best results.
  • This optimal combination achieved an accuracy of nearly 96% on the custom database.
  • The study analyzed computation times for both keypoint positioning and descriptor generation.

Conclusions:

  • The ORB and BRISK combination is highly effective for real-time object recognition in AR flight simulator applications.
  • This finding supports the development of advanced AR systems for pilot training.
  • Further research can optimize these methods for broader AR deployment.