常规尺寸的不确定度评价方法不适用于大尺寸测量,尤其是特定拟合任务中的不确定度评价。针对大尺寸测量的特殊性,研究了基于蒙特卡罗法的不确定度评价方法。通过随机仿真各项测量误差源,得到测量结果的仿真样本,并用计算机可视化直观表示该样本,形成离散不确定度点云,从而评价大尺寸特定测量对象的不确定度。以激光跟踪仪测量大型圆形截面工件为例,给出测点对称、均匀分布和半径约束等优化测量思想,减小测量不确定度。最后,将该方法应用于激光跟踪仪测量隧道构件的实例中,结果表明,常规圆心拟合不确定度为2.552 5 mm,加入半径约束优化测量方案后,不确定度减小至0.032 6 mm。仿真和实际实验表明,蒙特卡罗评价和离散点云表示法可准确、直观地评价特定大尺寸测量对象的不确定度,制定的最优测量方案可提高测量精度。

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.