Reliable object recognition using 3-D feature points for minimizing its mismatching

This paper introduces a reliable 3-D object recognition method which can statistically minimize its mismatching. This method basically uses a 3-D object model which is represented as a set of feature points with 3-D coordinate. Each feature point also has an attribute value about local shape around the point. The attribute value is represented as an orientation histogram of normal vector, which can be calculated by using several neighboring feature points around each point. Here, this attribute value means its local shape. By estimating relative similarity of two points of all possible combination in the model, the distinctiveness of each point is defined. 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, position and pose of the target object can be estimated from some correctly matched points. Experimental results using real scene have proved that recognition rate of this method is 93.8%, which is 42.2% higher than the conventional Spin Image method. Also, computing time is about nine times faster than that.