High-speed and reliable object recognition based on low-dimensional local shape features

In this paper, we propose a high-speed 3-D object recognition method using new feature values. Features for the object recognition method 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. We use these three-dimensional features to recognize the position and pose of multiple objects stacked randomly. Because they are low-dimensional, high-speed matching can be achieved. We have also reduced the computing time needed for data matching by using only effective points selected on the basis of their estimated distinctiveness. Experimental results using actual scenes have demonstrated that the computing time is about 93 times faster than that of the conventional SHOT method. Furthermore, the proposed method achieves a 98.2% recognition rate, which is 17.9% higher than that of the SHOT method. Also, we confirmed that the proposed method achieves higher-speed matching and higher recognition success rate than the conventional methods.