Optimal least-squares solution to the hand-eye calibration problem
This addresses a specific problem in robotics and computer vision for improving calibration accuracy and efficiency, though it appears incremental as it builds on existing methods.
The paper tackles the noisy hand-eye calibration problem by proposing a least-squares formulation using dual-quaternions, introducing efficient algorithms to find the exact optimal solution and simple analytic approximations, with claims of being the most efficient approach.
We propose a least-squares formulation to the noisy hand-eye calibration problem using dual-quaternions, and introduce efficient algorithms to find the exact optimal solution, based on analytic properties of the problem, avoiding non-linear optimization. We further present simple analytic approximate solutions which provide remarkably good estimations compared to the exact solution. In addition, we show how to generalize our solution to account for a given extrinsic prior in the cost function. To the best of our knowledge our algorithm is the most efficient approach to optimally solve the hand-eye calibration problem.