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.
Motivated by the increasing availability of 3D sensors capable of delivering both shape and texture information, this paper presents a novel descriptor for feature matching in 3D data enriched with texture. The proposed approach stems from the theory of a recently proposed descriptor for 3D data which relies on shape only, and represents its generalization to the case of multiple cues associated with a 3D mesh. The proposed descriptor, dubbed CSHOT, is demonstrated to notably improve the accuracy of feature matching in challenging object recognition scenarios characterized by the presence of clutter and occlusions.