Documenting Intent

A Survey of Spatial Models for Indoor Navigation

Abstract

Indoor environments cannot rely on global positioning systems for navigation, which poses a stark contrast to the immediacy and accuracy of positioning and navigation in outdoor environments. The study of indoor navigation has grown in two general topic areas, navigation of indoor space and machine learning of indoor environments. This paper will only review the current research in indoor space navigation and the modes of modeling space for a prescribed route. Literature reviews of indoor positioning have considered the array of approaches within the network and inertial models, the precision of each approach, and each system's fitness in a mass-market application. Yet, with a significant relationship to the built environment, a review of indoor positioning's impact on the field of architecture and more specifically, its relationship to spatial documentation has yet to be considered.