3-D Feature Point Matching for Object Recognition Based on Estimation of Local Shape Distinctiveness

Abstract

AbstractIn this paper, we propose a reliable 3-D object recognition method that can statistically minimize object mismatching. Our method basically uses a 3-D object model that is represented as a set of feature points with 3-D coordinates. Each feature point also has an attribute value for the local shape around the point. The attribute value is represented as an orientation histogram of a normal vector calculated by using several neighboring feature points around each point. Here, the important thing is this attribute value means its local shape. By estimating the relative similarity of two points of all possible combinations in the model, we define the distinctiveness of each point. In the proposed method, only a small number of distinctive feature points are selected and used for matching with all feature points extracted from an acquired range image. Finally, the position and pose of the target object can be estimated from a number of correctly matched points. Experimental results using actual scenes have demonstrated that the recognition rate of our method is 93.8%, which is 42.2% higher than that of the conventional Spin Image method. Furthermore, its computing time is about nine times faster than that of the Spin Image method.Keywordsobject recognition3-D feature point matchingrobot visionpoint cloud data3-D descriptorbin-picking