A new uncertainty estimation method was researched based on Monte Carlo evaluation,for some uncertainties of large-scale measurement could not be analyzed by conventional methods,especially for special fitting task.In proposed estimation,the simulated sample was obtained by simulating each measuring error source randomly and denoted as discrete point-clouds by computer vision,so as the uncertainty of given measuring object could be evaluated.By taking analyzing large-scale circular section part by laser tracker for example,the optimized measurement concepts including point symmetry,equal distribution and radius constraint were given.Finally,the optimized evaluation method was used in measuring practical tunnel components by laser tracker,results show that the uncertainty is decreased to 0.032 6 mm after radius constraint optimization,which is priority to average fitting uncertainty of circle center of 2.5525 mm by traditional method.It is proved that Monte Carlo evaluation and discrete point-cloud representation can evaluate accurately and intuitively the uncertainty for large-scale object and the optimum sampling strategy can improve the measuring precision.