3D object recognition using effective features selected by evaluating performance of discrimination

We propose a reliable 3D position and pose recognition method for complicated scenes including randomly stacked objects. Conventional methods use a small number of features selected by analyzing a target object model for recognition. The small number contributes to high-speed recognition, but actually the features include both 'true' and 'false' features. True features exist only in the target object and not in other parts, so they are valid for correct recognition purposes. On the other hand, false features exist in both the target object and in other parts, such as contacting areas caused by multiple objects. As a result of their matching incorrect parts, misrecognition may occur. To solve this problem, we propose a new method that uses effective features selected by analyzing not only the target object but also contacting areas caused by multiple objects. For predicting contacting areas, we generated very real input scenes by using 3D Computer Graphics (3D-CG) techniques and a physics-based simulator. Features that have high discrimination performance in the feature space are selected and used for the matching process. The method is robust to disturbances such as feature variability and achieves high feature separability; these enable it to achieve good discrimination performance. The method achieves reliable and fast object recognition by using a small number of effective features that have high discrimination performance. Experimental results show that the method's recognition success rate is from 33.6% to 92.9% higher than that of the Vector Pair Matching (VPM) method proposed by Akizuki et al. and that its processing time is within 1.46 seconds.