Combining contour and shape primitives for object detection and pose estimation of prefabricated parts

Man-made objects such as mechanical construction parts can typically be described as a composition of shape primitives like cylinders, planes, cones and spheres. We propose a robust method for the detection and pose estimation of such objects in 3D point clouds. Our main contribution is to enhance a probabilistic graph-matching approach that detects objects using 3D shape primitives with distinct 2D primitives such as circular contours. With this extension, our method copes with difficult occlusion situations and can be applied for object manipulation in complex scenarios such as grasping from a pile or bin-picking. We demonstrate the performance of our approach in a comparison with a state-of-the-art feature-based method for objects of generic shape and a primitive-based approach using only 3D shapes and no contours.