Semantically enriched navigation for indoor environments

Abstract

Location-based mobile services have been in use, and studied, for a long time. With the proliferation of wireless networking technologies, users are mostly interested in advanced services that render the surrounding environment (i.e., the building) highly intelligent and significantly facilitate their activities. In this paper our focus is on indoor navigation, one of the most important location services. Existing approaches for indoor navigation are driven by geometric information and neglect important aspects, such as the semantics of space and user capabilities and context. The derived applications are not intelligent enough to catalytically contribute to the pervasive computing vision. In this paper, a novel navigation mechanism is introduced. Such navigation scheme is enriched with user profiles and the adoption of an ontological framework. These enhancements introduce a series of technical challenges that are extensively discussed throughout the paper.

Evaluating suitability of the least risk path algorithm to support cognitive wayfinding in indoor spaces: An empirical study

“…Simplest path algorithm (Mark, 1986) Path length + intersection complexity Simplest instruction algorithm (Richter & Duckham, 2008) Intersection complexity + spatial chunking Least risk path algorithm (Grum, 2005) Path length (50%) + Risk value (50%) These 'cognitive' algorithms have the aim to simplify wayfinding by providing routes that are easier to follow, more intuitively correct, and in general more adhering to how people conceptualize routes to unfamiliar users (Tsetsos et al, 2006). Several cognitive studies have indeed indicated that during routing, humans value equally as much the form and complexity of route instructions as the total path length (Duckham & Kulik, 2003).…”

Towards a Semantic Spatial Model for Pedestrian Indoor Navigation

“…This could be very useful in practise, for applications which require a more detailed processing of context information. The notion of distance could be understood in a variety of different ways, depending on the semantics [19,20] of the application and its context (encoded in these attributes). Consider the following examples:…”

Multi-Level Indoor Navigation Ontology for High Assurance Location-Based Services

“…However, this ontology is limited for presenting a map only on one floor, not linked to the other floors in multi-floor buildings. A hybrid modeling technique is proposed in OntoNav [14] by modeling geometric and semantic information for user navigation. The ontology OntoNav addresses the linking of other floors in the building; users need to select the path rule for traveling, and the restriction of the route may be hard for maintenance.…”