Pose estimation of textureless objects in cluttered environments

In this paper, we propose a method for pose estimation of multiple (textureless) objects of the same type in heavily cluttered environments. The method as such could be used as a component for building more flexible automated visual inspection systems, removing the need for precise mechanical manipulation and enable inspection in settings previously thought unfeasible. The method consists of three phases. In the first phase, template matching is used to calculate similarity measure maps for different object poses. Template matching combines both edge and surface normal information to improve the pose estimation accuracy. Second, a large number of pose hypotheses are generated with a non-parametric clustering of the similarity measure maps and finally, the best hypotheses are iteratively selected. Method was evaluated and compared with the current state-of-the-art on two synthetic and one real-world datasets. The results show that the proposed system performs better than the current state-of-the art for pose estimation in industrial environments.