A Fine-grained Geospatial Representation and Framework for Large-Scale Indoor Environments

This thesis describes a system and method for extending the current paradigm of geographic information systems (GIS) to support indoor environments. It introduces features and properties of indoor multi-building environments that do not exist in other geographic environments or are not characterized in existing geospatial models, and proposes a comprehensive representation for describing such spatial environments. Specifically, it presents enhanced notions of spatial containment and graph topology for indoor environments, and extends existing geometric and semantic constructs. Furthermore, it describes a framework to: automatically extract indoor spatial features from a corpus of semi-structured digital floor plans; populate the aforementioned indoor spatial representation with these features; store the spatial data in a descriptive yet extensible data model; and provide mechanisms for dynamically accessing, mutating, augmenting, and distributing the resulting large-scale dataset. Lastly, it showcases an array of applications, and proposes others, which utilize the representation and dataset to provide rich location-based services within indoor environments. Thesis Supervisor: Seth Teller Title: Professor of Computer Science & Engineering