Morphological neural networks for real-time vision based self-localization

In this paper we present some real time results of the implementation on a mobile robot of visual setf-localization algorithms based on Morphological Heteroassociative Memories (MHM). We propose a dual set of min/max MHM storing the views that serve as landmarks for self localization. The binarized input images are subject to erosion in order to increase the robustness of the recall process. We present some empirical results on basic navigation experiments in an indoor environment. We use as the measure of performance of our approach the rate of false recognition, conditioned to some landmark being recognized.