Effect of noise level for estimating the spatial uncertainties of workobject localization

In this paper, we present a method to estimate spatial uncertainties of a localized workobject using Bayesian estimation. We approch the problem of a sensor eye-in-hand calibration with error covariances by comparing the covariance propagation with Monte Carlo simulation and actual tests when the system noise level is changing. The spatial uncertainties are analysed using eigenvalues of the covariances in the direction of the respective eigenvectors. Results from the comparison between the different methods gives encouraging results and we believe that covariance propagation can be used in uncertainty estimation in different levels of noise