Abstract
Abstract. Over the last couple of years, research on indoor environments has gained a fresh impetus; more specifically applications that support navigation and wayfinding have become one of the booming industries. Indoor navigation research currently covers the technological aspect of indoor positioning and the modelling of indoor space. The algorithmic development to support navigation has so far been left mostly untouched, as most applications mainly rely on adapting Dijkstra's shortest path algorithm to an indoor network. However, alternative algorithms for outdoor navigation have been proposed adding a more cognitive notion to the calculated paths and as such adhering to the natural wayfinding behaviour (e.g. simplest paths, least risk paths). These algorithms are currently restricted to outdoor applications. The need for indoor cognitive algorithms is highlighted by a more challenged navigation and orientation due to the specific indoor structure (e.g. fragmentation, less visibility, confined areas…). As such, the clarity and easiness of route instructions is of paramount importance when distributing indoor routes. A shortest or fastest path indoors not necessarily aligns with the cognitive mapping of the building. Therefore, the aim of this research is to extend those richer cognitive algorithms to three-dimensional indoor environments. More specifically for this paper, we will focus on the application of the least risk path algorithm of Grum (2005) to an indoor space. The algorithm as proposed by Grum (2005) is duplicated and tested in a complex multi-storey building. The results of several least risk path calculations are compared to the shortest paths in indoor environments in terms of total length, improvement in route description complexity and number of turns. Several scenarios are tested in this comparison: paths covering a single floor, paths crossing several building wings and/or floors. Adjustments to the algorithm are proposed to be more aligned to the specific structure of indoor environments (e.g. no turn restrictions, restricted usage of rooms, vertical movement) and common wayfinding strategies indoors. In a later stage, other cognitive algorithms will be implemented and tested in both an indoor and combined indoor-outdoor setting, in an effort to improve the overall user experience during navigation in indoor environments.
Highlights
AND PROBLEM STATEMENTOver the last decade, indoor spaces have become more and more prevalent as research topic within geospatial research environments (Worboys, 2011)
Past developments in the modelling and analysis of three-dimensional environments have already given us a better structural understanding of the use and possibilities of indoor environments (Becker et al, 2013; Boguslawski et al, 2011). These evolutions combined with the rapid progress in spatial information services and computing technology (Li and Lee, 2010) have put three-dimensional modelling and analyses more and more in the spotlight
Given the fact that as human beings we spend most of our time indoors (Jenkins et al, 1992), indoor environments have become an indispensable part of current geospatial research
Summary
Indoor spaces have become more and more prevalent as research topic within geospatial research environments (Worboys, 2011). Several researchers have developed a wide variety of indoor navigational models ranging from abstract space models (Becker et al 2009) and 3D models (Coors 2003, Li & He 2008) to pure network models (Jensen et al 2009, Karas et al 2006, Lee 2001, Lee 2004). The main goal of this paper is to translate existing outdoor cognitive algorithms to an indoor environment and compare their efficiency and results in terms of correctness, difference to common shortest path algorithms and their equivalents in outdoor space. This paper is completed with a conclusion on the discussed issues
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