Corner Point-Based Coarse–Fine Method for Surface-Mount Component Positioning

Component pick-and-place technology has been widely used to improve production efficiency and reduce common defects. The vision-driven measurement system of a component pick-and-place machine requires an appropriate positioning algorithm with low computational complexity, high accuracy, and high generalizability. To satisfy these attributes is rather challenging. This paper focuses on the online component positioning problem based on corner points. Thus, we propose a robust, accurate, and efficient universal algorithm that incorporates preprocessing, coarse positioning, and fine positioning stages. Two types of model key points are introduced for interpreting the model component. To enhance positioning accuracy and robustness against illumination changes, the Harris corners and subpixel corner points are extracted from the images of real components. In the coarse positioning step, distance and shape feature matching methods are introduced to, respectively, compute the coarse and correct correspondences between type I model key points and Harris corner points. After the corresponding point pairs have been obtained, the coarse and fine positioning problems are formulated as least squares error problems. The effectiveness of the proposed method was verified by applying the method in several real component positioning experiments.