Environment adaptive 3D object recognition and pose estimation by cognitive perception engine

In this paper, we propose novel evidence selection and collection method based on Bayesian theorem for object recognition and pose estimation in real environment. To recognize and estimate 3D object pose accurately, photometric and geometric evidences such as color blob, SIFT points and lines, can be utilized as single or multiple features in a sequence of images. However, to guarantee dependability in visual perception, the system have to cope with environmental variation that includes change of illumination, amount of texture, and distance to object. So, we made monitoring system to observe the change of environment. The main contribution of this paper is to develop and improve the recognition strategy by proper evidence selection and collection by using Bayesian rule that can be working robustly in various environmental conditions. The experimental results with a single stereo camera show the feasibility and effectiveness of the proposed method in an environment containing both textured and texture-less objects.