A Fast Object Recognition and Categorization Technique for Robot Grasping Using the Visual Bag of Words

We present in this paper a real-time method for visual categorization to do robot grasping. We describe an object database with SURF feature points which we quantify with the Kmeans clustering algorithm to make visual words. Then, we train a Support Vector Machine classifier having as entries the distribution of the bag of features extracted earlier. Likewise, we do object recognition using the SVM algorithm. The real-time implementation is done with the OpenCV GPU. The application aims to pick up an object and drop it using our robot manipulator equipped with a camera using our visual system. Finally, the results of our experiments of the object recognition show an average of recognition between 95% and 100%.