Abstract
Abstract. The demand for indoor navigation is increasingly urgent in many applications such as safe management of underground spaces or location services in complex indoor environment, e.g. shopping centres, airports, museums, underground parking lot and hospitals. Indoor navigation is still a challenging research field, as currently applied indoor navigation algorithms commonly ignore important environmental and human factors and therefore do not provide precise navigation. Flexible and detailed networks representing the connectivity of spaces and considering indoor objects such as furniture are very important to a precise navigation. In this paper we concentrate on indoor navigation considering obstacles represented as polygons. We introduce a specific space subdivision based on a simplified floor plan to build the indoor navigation network. The experiments demonstrate that we are able to navigate around the obstacles using the proposed network. Considering to well-known path-finding approaches based on Medial Axis Transform (MAT) or Visibility Graph (VG), the approach in this paper provides a quick subdivision of space and routes, which are compatible with the results of VG.
Highlights
With the increased urbanization, public buildings such as shopping centres, airports, museums and music halls have become larger and more complex
Various topics are investigated for indoor navigation, but indoor space modelling is considered to be the key issue to provide an appropriate path for navigation
Li and Lee (2008) build a topological semantic location model based on an exit-location matrix, which reveals semantic relationships and semantic distance of indoor environment
Summary
Public buildings such as shopping centres, airports, museums and music halls have become larger and more complex. Liu and Zlatanova (2011) propose the ‘door-to-door’ approach based on Visibility Graph (De Berg et al, 2000), which derives the connections between doors, which are used to create the network for navigation. This network adapts better to the walking behaviour of pedestrians. Goetz and Zipf (2011) use ‘door-to-door’ approach to create a navigation network for path-finding They represent the indoor environment with a model which contains topologic, semantic and geometric information.
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