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Multilayer Perceptron-Based Error Compensation for Automatic On-the-Fly Camera Orientation Estimation Using a Single

Xingyou Li1, Hyoungrae Kim2, Vijay Kakani3

  • 1Electrical and Computer Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Republic of Korea.

Sensors (Basel, Switzerland)
|February 10, 2024
PubMed
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This summary is machine-generated.

This study presents a new multilayer perceptron (MLP) method for accurate real-time camera orientation estimation in autonomous vehicles, improving pitch and yaw angle accuracy using lane lines.

Area of Science:

  • Computer Vision
  • Robotics
  • Machine Learning

Background:

  • Accurate camera orientation is crucial for autonomous vehicle navigation.
  • Existing methods often struggle with real-time performance and accuracy under varying conditions.
  • Cameras with zero roll angle are common in automotive applications.

Purpose of the Study:

  • To develop and evaluate a novel multilayer perceptron (MLP) error compensation method for real-time camera orientation estimation.
  • To enhance the accuracy of estimating pitch and yaw angles using a single vanishing point and road lane lines.
  • To validate the method's effectiveness in both simulated and real-world driving scenarios.

Main Methods:

  • Utilized a multilayer perceptron (MLP) for error compensation in camera orientation estimation.
Keywords:
autonomous vehiclescamera extrinsic parameterscamera orientation estimationvanishing point

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  • Employed a single vanishing point and road lane lines as primary inputs.
  • Integrated two Kalman filter models with image point (u, v) and derived angle inputs.
  • Focused on cameras with a 0° roll angle, typical for autonomous vehicles.

    • Main Results:

    • The proposed MLP method significantly improved the accuracy of camera orientation estimations.
    • Performance metrics (avgE, minE, maxE, ssE, Stdev) demonstrated superior results compared to existing techniques.
    • The system showed consistent accuracy across diverse scenarios in both simulator and real-vehicle tests.

    Conclusions:

    • The developed MLP error compensation method offers a robust and precise solution for real-time camera orientation estimation.
    • The approach is adaptable and accurate, showing significant promise for enhancing autonomous vehicle systems.
    • This method provides a reliable foundation for advanced driver-assistance systems and fully autonomous driving.