Visual Robotic Object Grasping Through Combining RGB-D Data and 3D Meshes

In this paper, we present a novel framework to drive automatic robotic grasp by matching camera captured RGB-D data with 3D meshes, on which prior knowledge for grasp is pre-defined for each object type. The proposed framework consists of two modules, namely, pre-defining grasping knowledge for each type of object shape on 3D meshes, and automatic robotic grasping by matching RGB-D data with pre-defined 3D meshes. In the first module, we scan 3D meshes for typical object shapes and pre-define grasping regions for each 3D shape surface, which will be considered as the prior knowledge for guiding automatic robotic grasp. In the second module, for each RGB-D image captured by a depth camera, we recognize 2D shape of the object in it by an SVM classifier, and then segment it from background using depth data. Next, we propose a new algorithm to match the segmented RGB-D shape with predefined 3D meshes to guide robotic self-location and grasp by an automatic way. Our experimental results show that the proposed framework is particularly useful to guide camera based robotic grasp.