Navigation models are explicit representations of geometrical and topological information of physical environments
that can be utilized formap-matching of indoor positioning data. This research paper presents algorithms
for automated generation of three different types of navigation models, namely, centerline-based network,
metric-based and grid-based navigation models, for map-matching of indoor positioning data. The
abovementioned navigation models have been generated in an automated fashion from Industry Foundation
Classes (IFC)-based building informationmodels (BIM). Specifically,we have 1) built on and targeted addressing
limitations of existing algorithms that generate centerline-based network navigation models for polygonal
shapes, 2) developed an approach to extract 2D geometry and topology from IFC-based BIM for creating
metric-based navigation models, and 3) modified an existing algorithm to generate grid-based navigation
models using geometry and topology extracted fromBIM. The abovementioned three types of navigation models
have been generated for six different testbeds with varying shape, size and density of spaces. We have validated
the generality of the developed algorithms by evaluating the accuracy of geometrical and topological information
contained within the three types of navigation models generated from testbeds with varying spatial
characteristics.