Model shape oriented robust matching of dot cloud data based and its application to defect recognition

We propose a robust algorithm for matching three-dimensional dot cloud data in an effort to detect defects during manufacturing processes. We apply our proposed method to inspect a complex three-dimensional die-cast product. Our approach recognizes the difference between two data sets as a defect after matching the data sets. Moreover, our method improves matching accuracy by detecting geometrical features such as edge points, and by using such property values as gradients. Fundamental experiments using real three-dimensional dot cloud data show that the method is effective as a defect inspection system.