Towards a robust hand-eye calibration using normal flows

Calibrating hand-eye geometry is often based on explicit feature correspondences. This article presents an alternative method that uses the apparent flow induced by the motion of the camera to achieve self-calibration. To make the method more robust against noise, the strategy is to use the orientation of the normal flow field which is more noise-immune, to recover first the direction component of the hand-eye geometry. Outliers in the extracted flow data are identified using some intrinsic properties of the flow field together with the partially recovered hand-eye geometry. The final complete solution is refined using a robust process. The proposed method can also be used for determining the relative geometry of multiple cameras without demanding overlap in the visual fields of the cameras. Experimental results on synthetic data and real image data are shown to illustrate the feasibility of the method.