Abstract
Abstract. Over the last couple of years, applications that support navigation and wayfinding in indoor environments have become one of the booming industries. However, the algorithmic support for indoor navigation has so far been left mostly untouched, as most applications mainly rely on adapting Dijkstra's shortest path algorithm to an indoor network. In outdoor space, several alternative algorithms have been proposed adding a more cognitive notion to the calculated paths and as such adhering to the natural wayfinding behavior (e.g. simplest paths, least risk paths). 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…). 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-story building. Several analyses compare shortest and least risk paths in indoor and in outdoor space. The results of these analyses indicate that the current outdoor least risk path algorithm does not calculate less risky paths compared to its shortest paths. In some cases, worse routes have been suggested. Adjustments to the original algorithm are proposed to be more aligned to the specific structure of indoor environments. 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)
We currently focus on the implementation and adjustment of the least risk path algorithm (LRP algorithm hereafter) as described by Grum (2005)
Testing the applicability of the LRP algorithm in indoor space requires a dataset of an extensive and complex indoor environment to be a valid alternative for the outdoor algorithmic testing
Summary
Indoor spaces have become more and more prevalent as research topic within geospatial research environments (Worboys, 2011). The need for more cognitively rich algorithms is even more pronounced in indoor spaces than outdoors This has its origin in the explicit distinctiveness in structure, constraints and usage between indoor and outdoor environments (Li, 2008; Walton & Worboys, 2009). Algorithms developed to support a smooth navigation will have to consider these intricacies and create route instructions that are more aligned with the human cognitive mapping of indoor spaces. 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.
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