Multi-class fruit classification using RGB-D data for indoor robots

Abstract

In this paper we present an effective and robust system to classify fruits under varying pose and lighting conditions tailored for an object recognition system on a mobile platform. Therefore, we present results on the effectiveness of our underlying segmentation method using RGB as well as depth cues for the specific technical setup of our robot. A combination of RGB low-level visual feature descriptors and 3D geometric properties is used to retrieve complementary object information for the classification task. The unified approach is validated using two multi-class RGB-D fruit categorization datasets. Experimental results compare different feature sets and classification methods and highlight the effectiveness of the proposed features using a Random Forest classifier.