Continuous hand-eye calibration using 3D points
This addresses the problem of balancing calibration accuracy and flexibility for robotics and computer vision applications, representing an incremental improvement over existing methods.
The paper tackles the trade-off between accuracy and flexibility in hand-eye calibration by proposing a two-step method: a closed-form solution for the hand-eye transformation that improves accuracy and robustness, and a formulation that reduces dependency on the calibration object to a single 3D point, eliminating the need for orientation estimation. This results in a versatile method for continuous calibration.
The recent development of calibration algorithms has been driven into two major directions: (1) an increasing accuracy of mathematical approaches and (2) an increasing flexibility in usage by reducing the dependency on calibration objects. These two trends, however, seem to be contradictory since the overall accuracy is directly related to the accuracy of the pose estimation of the calibration object and therefore demanding large objects, while an increased flexibility leads to smaller objects or noisier estimation methods. The method presented in this paper aims to resolves this problem in two steps: First, we derive a simple closed-form solution with a shifted focus towards the equation of translation that only solves for the necessary hand-eye transformation. We show that it is superior in accuracy and robustness compared to traditional approaches. Second, we decrease the dependency on the calibration object to a single 3D-point by using a similar formulation based on the equation of translation which is much less affected by the estimation error of the calibration object's orientation. Moreover, it makes the estimation of the orientation obsolete while taking advantage of the higher accuracy and robustness from the first solution, resulting in a versatile method for continuous hand-eye calibration.