Kong Ling Bao, Ren Ming Jun, Xu Min
Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Fudan University, Shanghai 200433, China.
Institute of Robotics, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Sensors (Basel). 2017 May 12;17(5):1110. doi: 10.3390/s17051110.
The measurement of ultra-precision freeform surfaces commonly requires several datasets from different sensors to realize holistic measurements with high efficiency. The effectiveness of the technology heavily depends on the quality of the data registration and fusion in the measurement process. This paper presents methods and algorithms to address these issues. An intrinsic feature pattern is proposed to represent the geometry of the measured datasets so that the registration of the datasets in 3D space is casted as a feature pattern registration problem in a 2D plane. The accuracy of the overlapping area is further improved by developing a Gaussian process based data fusion method with full consideration of the associated uncertainties in the measured datasets. Experimental studies are undertaken to examine the effectiveness of the proposed method. The study should contribute to the high precision and efficient measurement of ultra-precision freeform surfaces on multi-sensor systems.
超精密自由曲面的测量通常需要来自不同传感器的多个数据集，以高效实现整体测量。该技术的有效性在很大程度上取决于测量过程中数据配准和融合的质量。本文提出了解决这些问题的方法和算法。提出了一种内在特征模式来表示测量数据集的几何形状，从而将数据集在三维空间中的配准转化为二维平面中的特征模式配准问题。通过开发一种基于高斯过程的数据融合方法，充分考虑测量数据集中的相关不确定性，进一步提高了重叠区域的精度。进行了实验研究以检验所提方法的有效性。该研究应有助于多传感器系统上超精密自由曲面的高精度和高效测量。
