A HYBRID STEREO FEATURE MATCHING ALGORITHM FOR STEREO VISION-BASED BIN PICKING

Abstract

Stereo vision-based bin picking systems require accurate 3D information to be recovered from 2D stereo images. To achieve this goal, we have developed a hybrid coarse-to-fine algorithm for stereo feature matching, which is based on the 2D six-parameter affine transformation and local similarity evaluation. With this algorithm, the coarse matching is performed by the 2D six-parameter affine transformation to get rough feature matches, imposing a strong constraint to further search instead of the traditional epipolar constraint. To obtain precise matches, the perspective effect is dealt with fine stereo feature matching by performing local similarity evaluation on the attribute vectors of features. Experimental results proving the performance of the stereo feature matching algorithm are also presented.