Towards a generalization of self-localization

Abstract

Self-localization is an important task for humans and autonomous robots as it is the basis for orientation and navigation in a spatial environment and for performing mapping tasks. In robotics, self-localization on the basis of monomodal perceptual information has been investigated intensively. The present chapter looks at self-localization in a more general setting where the reference information may be provided by different types of sensors or by descriptions of locations under a variety of conditions. We introduce some of these conditions and discuss general approaches to identifying locations in perceived environments. Taking into account cognitive considerations, we propose an approach to identify locations on a high, abstract level of representation. The approach combines qualitative and quantitative information to recognize locations described as configurations of shape features. We evaluate this approach in comparison to other approaches in a self-localization task and a generalized localization task based on a schematic map. © 2007 Springer-Verlag Berlin Heidelberg.