面向对象的大尺寸测量不确定度分析

测量结果必须说明不确定度。常规尺寸的解析法和标准比对法难以适用于评价大尺寸测量不确定度，尤其是特定拟合任务结果的不确定度。针对大尺寸测量的特殊性，研究基于蒙特卡罗的评价方法评价大尺寸特定测量对象的不确定度，并用计算机可视化直观表示离散点云。利用离线仿真评价测量不确定度，用于设计最优采样策略。以激光跟踪仪测量大型圆形截面工件分析为例，给出测点对称、均匀分布和半径约束等优化测量思想。最后应用于激光跟踪仪测量隧道构件的实例当中。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、直观评价大尺寸测量特定测量对象的不确定度，制定的最优采样策略可提高测量精度。

Task specific uncertainty estimation of large-scale measurement

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.