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In recent years, object recognition has attracted increasing attention of researchers due to its numerous applications. For instance, object recognition enables collaborative robots to carry out tasks like searching for an object in an unstructured environment or retrieving a tool for a human coworker. 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 first segments all objects in a table top scene through an unsupervised clustering technique. Then, 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.

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