  Object recognition and grasping plays a key role in robotic systems,
especially for the autonomous robots to implement object sorting tasks in a
warehouse. In this paper, we present a global texture-shape 3D feature
descriptor which can be utilized in a system of object recognition and
grasping, and can perform object sorting tasks well. Our proposed descriptor
stems from the clustered viewpoint feature histogram (CVFH), which relies on
the geometrical information of the whole 3D object surface only, and can not
perform well in recognizing the objects with similar geometrical information.
Therefore, we extend the CVFH descriptor with texture and color information to
generate a new global 3D feature descriptor. The proposed descriptor is
evaluated in tasks of recognizing and classifying 3D objects by applying
multi-class support vector machines (SVM) in both public 3D image dataset and
real scenes. The results of evaluation show that the proposed descriptor
achieves a significant better performance for object recognition compared with
the original CVFH. Then, the proposed descriptor is applied in our object
recognition and grasping system, showing that the proposed descriptor helps the
system implement the object recognition, object grasping and object sorting
tasks well.
