Pose Estimation of Randomly Organized Stator Housings

Machine vision is today a well-established technology in industry where especially conveyer belt applications are successful. A related application area is the situation where a number of objects are located in a bin and each has to be picked from the bin. This problem is known as the automatic bin-picking problem and has a huge market potential due to the countless situations where bin-picking is done manually. In this paper we address a general bin-picking problem present at a large pump manufacturer, Grundfos, where many objects with circular openings are handled each day. We pose estimate the objects by finding the 3D opening based on the elliptic projections into two cameras. The ellipses from the two cameras are handled in a unifying manner using graph theory together with an approach that links a pose and an ellipse via the equation for a general cone. Tests show that the presented algorithm can estimate the poses for a large variety of orientations and handle both noise and occlusions.