Integrating Multiple Representations of Spatial Knowledge for Mapping, Navigation, and Communication

Abstract

A robotic chauffeur should reason about spatial information with a variety of scales, dimensions, and ontologies. Rich representations of both the quantitative and qualitative char-acteristics of space not only enable robust navigation behav-ior, but also permit natural communication with a human pas-senger. We apply a hierarchical framework of spatial knowl-edge inspired by human cognitive abilities, the Hybrid Spatial Semantic Hierarchy, to common navigation tasks: safe mo-tion, localization, map-building, and route planning. We also discuss the straightforward mapping between the variety of ways in which people communicate with a chauffeur and the framework’s heterogeneous concepts of spatial knowledge. We present pilot experiments with a virtual chauffeur.