Abstract: Camera calibration is a process of optimizing the camera parameters. This paper describes an evaluation of different stochastic and heuristic estimators for cost function minimization used in camera calibration. The optimization algorithm is a standard gradient walk on the epipolar-constraint. The results show estimators work similar on the given set of correspondence. Correspondences selected to a given distribution over the frame gives better calibration results, especially the results on the yaw angle estimation show more robust results. In this paper the distribution will uniformly distributed over the frame using binning [1,2]. The data used in this paper shows binning does lower the error behavior in most calibrations. The L$^1$-norm and L$^2$-norm using binning does not reach an error with respect to the ground truth higher 4 pix. The calibrations rejecting binning shows an impulse on the 970 calibration. To avoid this impulse binning is used. Binning influences the calibration result more as the choice of the right m-estimator or the right norm.