Flexible calibration method for visual measurement using an improved target with vanishing constraints.

Area of Science:

• Computer Vision
• Robotics
• Machine Learning

Background:

• Accurate extrinsic parameter calibration is crucial for stereo vision systems.
• Conventional calibration targets and methods can be complex and costly to produce and implement.
• Existing methods may lack robustness in real-world scenarios.

Purpose of the Study:

• To propose an improved camera calibration method for accurate extrinsic parameter calculation.
• To develop a novel calibration target that is easier to manufacture and less expensive.
• To enhance the robustness and flexibility of the calibration process for binocular vision sensors.

Main Methods:

• Designed a novel calibration target with two groups of parallel lines at an angle range of 80° to 90°.
• Developed a new optimization function with a robust penalty factor for extrinsic parameter estimation.
• Implemented the improved method for binocular vision sensor calibration.

Main Results:

• The novel target reduces production difficulty and manufacturing costs.
• The new optimization function provides more robust extrinsic parameter estimation.
• The proposed calibration method demonstrates increased flexibility and robustness compared to Zhang's method.

Conclusions:

• The improved calibration method offers a more practical and reliable solution for determining extrinsic camera parameters.
• The novel target design and optimization function contribute to enhanced performance in binocular vision systems.
• This work advances the field of camera calibration for applications requiring precise spatial understanding.