Unsupervised Feature Extraction from RGB-D Data for Object Classification: a Case Study on the YCB Object and Model Set

Object recognition has attracted increasing attention of researchers due to its numerous applications. For instance, it enables robots to carry out tasks like searching for an object in an unstructured environment or retrieving a tool for a human co-worker. In this study, we present a new technique for unsupervised feature extraction from red, green, blue, plus depth (RGB-D) data, which is then combined with several classifiers to perform object recognition. Specifically, our architecture segments all objects in a table top scene through an unsupervised clustering technique. It focuses separately on each object to extract both shape and visual features. We conduct experiments on a subset of 20 objects selected from the YCB object and model set and evaluate the performance of several classifiers. The most effective one achieves an accuracy of 99.7% when trained and tested on samples acquired with the same conditions (equipment and environment). Results degrade when the system is trained with YCB data and tested with data acquired from a Kinect sensor in online laboratorial implementation.