Flexible camera calibration using not-measured imperfect target.

Area of Science:

• Computer Vision
• Robotics
• Metrology

Background:

• Accurate camera calibration is crucial for 3D reconstruction and robotic applications.
• Traditional methods often rely on precisely measured, perfect calibration targets, which are not always feasible.
• Imperfect or unmeasured targets pose significant challenges to achieving accurate camera calibration.

Purpose of the Study:

• To propose a novel constrained optimization method for camera calibration.
• To enable absolute scale calibration without precise measurements of the calibration target.
• To improve accuracy and precision compared to traditional calibration approaches.

Main Methods:

• A constrained optimization process is employed, leveraging the inherent geometry of the calibration target.
• Statistical constraints based on the pattern design are integrated into the optimization.
• The method calibrates the camera to an absolute scale without direct measurement of the target.

Main Results:

• The proposed method significantly reduces reprojection errors compared to traditional approaches that use imperfect targets.
• Experimental results demonstrate smaller reprojection errors and higher measurement precision.
• The approach successfully calibrates cameras to an absolute scale even with unmeasured targets.

Conclusions:

• Constrained optimization offers a robust solution for camera calibration with imperfect targets.
• The method achieves superior accuracy and precision, enhancing its applicability in real-world scenarios.
• This technique advances the field of camera calibration, particularly for systems requiring absolute scale information.