Calibration–Free Hand–Eye Calibration: A Structure–from–Motion Approach

A new hand-eye calibration approach for fracture reduction robot

OBJECTIVE

The hand-eye calibration is used to determine the transformation between the end-effector and the camera marker of the robot. But the robot movement in traditional method would be time-consuming, inaccurate and even unavailable in some conditions. The method presented in this article can complete the calibration without any movement and is more suitable in clinical applications.

METHODS

Instead of solving the classic non-linear equation AX = XB, we collected the points on X and Y axes of the tool coordinate system (TCS) with the visual probe and fitted them using the singular value decomposition algorithm (SVD). Then, the transformation was obtained with the data of the tool center point (TCP). A comparison test was conducted to verify the performance of the method.

RESULTS

The average translation error and orientation error of the new method are 0.12 ± 0.122 mm and 0.18 ± 0.112° respectively, while they are 0.357 ± 0.347 mm and 0.416 ± 0.234° correspondingly in the traditional method.

CONCLUSIONS

The high accuracy of the method indicates that it is a good candidate for medical robots, which usually need to work in a sterile environment.

Data Selection for Hand-eye Calibration: A Vector Quantization Approach

This paper presents new vector quantization based methods for selecting well-suited data for hand-eye calibration from a given sequence of hand and eye movements. Data selection can improve the accuracy of classic hand-eye calibration, and make it possible in the first place in situations where the standard approach of manually selecting positions is inconvenient or even impossible, especially when using continuously recorded data. A variety of methods is proposed, which differ from each other in the dimensionality of the vector quantization compared to the degrees of freedom of the rotation representation, and how the rotation angle is incorporated. The performance of the proposed vector quantization based data selection methods is evaluated using data obtained from a manually moved optical tracking system (hand) and an endoscopic camera (eye).