Realtime object recognition using decision tree learning

Abstract

An object recognition process in general is designed as a domain specific, highly specialized task. As the complexity of such a process tends to be rather inestimable, machine learning is used to achieve better results in recognition. The goal of the process presented in this paper is the computation of the pose of a visible robot, i.e. the distance, angle, and orientation. The recognition process itself, the division into subtasks, as well as the results of the process are presented. The algorithms involved have been implemented and tested on a Sony Aibo. © Springer-Verlag Berlin Heidelberg 2005.