Edge information based object detection and classification

Abstract

This thesis presents work regarding the development 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. Numerous novel improvements are proposed, including 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. Also, a classification model is outlined along with example classifiers and used values. The work is illustrated graphically where applicable.