引用本文: 潘国荣, 郭巍, 周跃寅. 一种基于先验误差分解定权的精密测量拟合算法[J]. 武汉大学学报 ( 信息科学版), 2015, 40(10): 1339-1343.
PAN Guorong, GUO Wei, ZHOU Yueyin. A Precise Measurement Fitting Algorithm Based on Priori Error Decomposition Weighting[J]. Geomatics and Information Science of Wuhan University, 2015, 40(10): 1339-1343.
 Citation: PAN Guorong, GUO Wei, ZHOU Yueyin. A Precise Measurement Fitting Algorithm Based on Priori Error Decomposition Weighting[J]. Geomatics and Information Science of Wuhan University, 2015, 40(10): 1339-1343.

A Precise Measurement Fitting Algorithm Based on Priori Error Decomposition Weighting

• 摘要: 工业测量拟合通常只关心待测工件的尺寸姿态,而不关心其绝对位置,但观测数据却包含了两者的误差。为提取出影响待测工件尺寸姿态的误差分量,提出了基于测量仪器先验误差分解定权的工业测量拟合算法。该算法依据工件形状和观测姿态对空间直线、平面、圆和球拟合计算中的各测点进行先验误差多向分解,并依据各向误差分量的影响量对测点进行加权拟合。实验结果表明,该算法与等权拟合相比,在形状和姿态参数拟合精度上可提高约10%~17%。

Abstract: Usually in industrial measurements, the size and posture of a workpiece instead of its absolute position are the most frequent factors of interest, but observation data include errors in both. To extract the errors that influence the size and posture of the workpiece, this paper presents an industrial fitting algorithm based on decomposition of priori errors to determine the optimal weight for each measured points. This algorithm executes prior error decomposition in multi-directions for spatial lines, planes, circles and spheres according to the shape of the workpiece and measuring postures. Using the decomposed value from the measurement error, the weight of each point was revaluated to do the fitting. Experimental results show that when compared with the equal-weighted fitting algorithm, this new algorithm improved fitting precision about 10%-17% for size and posture parameters.

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