Fast 6D Pose Estimation for Texture-less Objects from a single RGB image

A fundamental step to solve bin-picking and grasping problems is the accurate estimation of an object 3D pose. Such visual task usually rely on profusely textured objects: standard procedures such as detection of interest points or computation of appearance-based descriptors are favoured by using a highly informative surface. However, texture-less objects or their parts (i.e., those whose surface texture is poorly conditioned) are common in any environment but still challenging to deal with. This is due the fact that the distribution of surface brightness makes difficult to compute interest points or appearance-based descriptors. In this paper, we propose a method to estimate the 3D pose for texture-less objects given a coarse initialization: the pose is estimated using using edge correspondences, where the similarity measure is encoded using a pre-computed linear regression matrix. Furthermore, we also propose a method to increase the robustness of the estimated pose against background and object clutter. We validate both methods by using synthetic and real image sequences with objects with known ground truth.