Edge information based object classification for NAO robots

Abstract

This paper presents a research regarding the development of a computationally cheap and reliable edge information based object detection and classification system for use on the NAO humanoid robots. The work covers ground detection, edge detection, edge clustering, and cluster classification, the latter task being equivalent to object recognition. In this work, a new geometric model for ground detection, a joint edge model using two edge detectors in unison for improved edge detection, and a hybrid edge clustering model have been proposed which can be implemented on NAO robots. Also, a classification model is outlined along with example classifiers and used values.