High Precision Calibration Algorithm for Binocular Stereo Vision Camera using Deep Reinforcement Learning.

Area of Science:

• Computer Vision
• Robotics
• Machine Learning

Background:

• Camera calibration is crucial for accurate 3D reconstruction in computer vision.
• Existing methods often lack the precision required for advanced applications.
• Binocular stereo vision systems demand precise calibration for reliable depth perception.

Purpose of the Study:

• To develop a high-precision calibration algorithm for binocular stereo vision cameras.
• To leverage deep reinforcement learning for improved calibration accuracy and efficiency.
• To address limitations in current camera calibration techniques.

Main Methods:

• Established a binocular stereo camera model.
• Implemented internal parameter calibration using camera light center and distortion values.
• Utilized deep reinforcement learning with a fitting value function and target network for parameter optimization.
• Performed external parameter calibration through continuous updating and convergence of the deep reinforcement learning structure.

Main Results:

• Achieved calibration errors of 0.36% and 0.35% on different checkerboard sizes.
• Demonstrated high calibration accuracy and parameter calculation precision.
• Showcased rapid convergence of the value function and overall short computation time.
• Validated strong stability of the calibration results.

Conclusions:

• The proposed deep reinforcement learning algorithm significantly enhances binocular stereo vision camera calibration precision.
• The method offers a stable, accurate, and efficient solution compared to traditional approaches.
• This advancement has strong implications for fields requiring precise 3D spatial understanding.