Fast object recognition methods for the UJI online robot

Within the context of online robots, a considerable amount of research has traditionally focused on the global system functionality, including the way of interaction between the user and the robot. Recent results in different robotics areas have demonstrated the potential of a number of techniques from the Pattern Recognition and Machine Learning domains, although very few work has been specifically addressed to online robots, where the object recognition is directly performed by the user. In this paper, we investigate the feasibility of using a neural network approach to object recognition in the context of online robots, and discuss the main advantages over the application of statistical learning methods. Some experiments with the UJI (Universitat Jaume I) online robot evaluate the performance of different neural network implementations, comparing it to that of some distance-based object recognition algorithms.