Robust and Accurate Hand-Eye Calibration Method Based on Schur Matric Decomposition

Area of Science:

• Robotics
• Computer Vision
• Calibration Techniques

Background:

• Hand-eye calibration accuracy relies heavily on observation data quality.
• Traditional methods often struggle with data preprocessing and outlier detection.
• Robustness is a key challenge in achieving reliable hand-eye calibration.

Purpose of the Study:

• To propose a novel hand-eye calibration method using Schur decomposition.
• To enhance the accuracy and robustness of hand-eye calibration through improved data preprocessing.
• To address the fundamental problem of outlier detection in observation data.

Main Methods:

• A Schur decomposition-based approach for solving rotation and translation parameters.
• A two-step process: first solving rotation, then translation.
• Development of a discriminant equation for outlier detection based on Schur decomposition.

Main Results:

• Achieved prediction errors of 0.06 arcmin for rotation and 1.01 mm for translation.
• Demonstrated significantly improved performance in outlier detection compared to traditional methods.
• Validated the method's effectiveness through both simulations and experimental results.

Conclusions:

• The proposed Schur decomposition method offers improved accuracy and robustness in hand-eye calibration.
• Effective outlier detection via a discriminant equation enhances data quality and reliability.
• The method provides a minimal configuration for achieving a unique solution.