A Hybrid Framework Based on Warped Hierarchical Tree for Pose Estimation of Texture-Less Objects

Abstract

The pose of texture-less objects is very important for intelligent manufacturing and intelligent assembly. Existing methods cannot accurately estimate pose when partial features are missing or cluttered due to shading, reflection, and occlusion. We propose a hybrid framework based on the warped hierarchical tree for pose estimation which integrates template matching and sparse representation classification in this paper. Firstly, the template is formed by the prospectively cumulated orientation feature (PCOF), which is a probabilistic representation of orientations extracted from template images. And the warped hierarchical tree can be built offline according to the parameters for projecting the 3D object and the similarity between templates. Then the online searching can be repeated through the warped hierarchical tree until the template candidates have been found. Finally, the pose corresponding to the best-fitting template can be obtained by sparse representation classification based on the dictionaries consisted of the spreading orientations of template candidate images. The experiment results show the effectiveness of our method when partial features are missing or cluttered.