Generation of an Indoor Navigation Network for the University of Saskatchewan

Abstract

Finding ones way in unknown and unfamiliar environments is a common task. A number of tools ranging from paper maps to location-based services have been introduced to assist human navigation. Undoubtedly, car navigation systems can be considered the most successful example of location based services that widely gained user acceptance. However the concept of car navigation is not always (perhaps rarely) suitable for pedestrian navigation. Moreover, precise localization of moving objects indoors is not possible due to the absence of an absolute positioning method such as GPS. These make accurate indoor tracking and navigation an interesting problem to explore. Many of the methods of spatial analysis popular in outdoor applications can be used indoors. In particular, generation of the indoor navigation network can be an effective solution for a) improving the navigation experience inside complex indoor structures and b) enhancing the analysis of the indoor tracking data collected with existing positioning solutions. Such building models should be based on a graph representation and consist of the number of ‘nodes’ and ‘edges’, where ‘nodes’ correspond to the central position of the room and ‘edge’ represents the medial axis of the hallway polygons, which physically connects these rooms. Similar node-links should be applied stairs and elevators to connect building floors. To generate this model, I selected the campus of University of Saskatchewan as the study area and presented a method that creates an indoor navigation network using ESRI ArcGIS products. First, the proposed method automatically extracts geometry and topology of campus buildings and computes the distances among all entities to calculate the shortest path between them. The system navigates through the University campus and it helps locating classrooms, offices, or facilities. The calculation of the route is based on the Dijkstra algorithm, but could employ any network navigation algorithm. To show the advantage of the generated network, I present results of a study conducted in conjunction with the department of Computer Science. An experiment that included 37 participants was designed to collect the tracking data on a university campus to demonstrate how the incorporation of the indoor navigation model can improve the analysis of the indoor movement data. Based on the results of the study, it can be concluded that the generated indoor network can be applied to raw positioning data in order to improve accuracy, as well as be employed as a stand-alone tool for enhancing of the route guidance on a university campus, and by extension any large indoor space consisting of individual or multiple buildings.