A Novel Corner Detection Algorithm Applied to Vision-Based Alignment Systems

Abstract

In vision-based alignment systems, it is very important to detect the corner positions in images. It is based on these positions that the systems can calculate the offset between the workpieces and achieve their assembly alignment using robots. A simple and fast corner detection method is proposed, which can effectively overcome the data redundancy problem in traditional corner detection algorithms. First, the reduced image is figured out by scale transformation and down-sampling, and the corresponding edge binary image is obtained by gradient analysis. Then, all the edge coordinates are detected by the designed edge extraction operator, and an initial corner point is obtained by fitting analysis of these edge points. Due to the error arising from the above transformation and fitting, the point is not the target one. Finally, the Hough transform is used to detect the local linear features near the point, and the target point is determined by the linear intersection analysis. The experimental results show that the algorithm can effectively extract the target corner, which is basically consistent with the manually calibrated target point.