Surface Classification of 3D Point Data Based on Spacial Averaging for Small Objects Using 3D Laser Inspection System

摘要

Many researches direct their efforts to solving surface-fitting problem from measured point data. In many industrial situations, surface classification of 3-D multi-planes objects is frequently required. Typical examples are found in dimension control and machine vision applications. As technology advances, the capability of computers has greatly improved. This paper describes our system used to inspect objects and the method used to classify the surfaces for those objects with planar faces from a huge set of cloudy points. First the systems are based on opto-electronic techniques and hence are non-contact. The system is designed for a small objects less than 2 x 2 x 2 cm. The surface classification resolution is based on the principle of spacial geometry of vectors direction in our coordinate system. The system can be considered scanning linearly with a rotation stage of a mirror, on the other hand the object circumferentially rotate on a turntable. After obtained surface profile of 3-D objects, we then apply for a geometrical method to classify the surfaces. The performance of the systems is demonstrated with different sizes test objects.