A New Recognition and Pose Estimation of Tiny Electronic Parts Using Principal Component Analysis and Harris Corner Features

Abstract

The center coordinates and rotated angle of the electronic part must be precisely estimated in case of palletizing the parts by picking and placing the parts to output tray by a robot, especially, the tiny parts like surface mounting device (SMD). A machine vision algorithm must be used in recognizing the type as well as estimating the pose of the parts. In this paper, we propose not only a recognition algorithm of electronic part using Principal Component Analysis [PCA] combined with Harris corner feature (HCF) and least mean square error (LMSE) for the matching of the part with data base, but also a precise pose estimation of the recognized part by using the extracted four corners from HCF combined with lines from Hough transformation (LHT). Applying the proposed algorithm to the test setup of five kinds of SMDs, we verified the usefulness of the proposed algorithm in accurate pose estimation as well as in recognition of type of the parts even in environment with illumination changes.