CCDoN: Local features for high-speed and reliable 6-DoF pose estimation of randomly stacked objects

This paper introduces a high-speed 3-D object recognition method using a novel feature description. Features proposed in this study consist of three values. One is the Difference of Normals (DoN) feature value that has been proposed by Ioannou. The other two represent information about curvature. These features are named Combination of Curvatures and Difference of Normals (CCDoN) Features. These features are used for recognition of position and pose of multiple objects that are stacked randomly. Because they are low-dimensional, high-speed matching can be achieved. Moreover, high-speed and reliable matching is achieved by using only effective features selected on the basis of their estimated distinctiveness. Experimental results using real datasets have demonstrated that the processing time is about 81 times faster than that of the conventional Spin Image method. Furthermore, the proposed method achieves a 98.2% recognition rate, which is 46.6% higher than that of the Spin Image method.