Integrating Image and Network-Based Topological Data through Spatial Data Fusion for Indoor Location-Based Services

Abstract

Nowadays, the importance and utilization of spatial information are recognized. Particularly in urban areas, the demand for indoor spatial information draws attention and most commonly requires high-precision 3D data. However accurate, most methodologies present problems in construction cost and ease of updating. Images are accessible and are useful to express indoor space, but pixel data cannot be applied directly to provide indoor services. A network-based topological data gives information about the spatial relationships of the spaces depicted by the image, as well as enables recognition of these spaces and the objects contained within. In this paper, we present a data fusion methodology between image data and a network-based topological data, without the need for data conversion, use of a reference data, or a separate data model. Using the concept of a Spatial Extended Point (SEP), we implement this methodology to establish a correspondence between omnidirectional images and IndoorGML data to provide an indoor spatial service. The proposed algorithm used position information identified by a user in the image to define a 3D region to be used to distinguish correspondence with the IndoorGML and indoor POI data. We experiment with a corridor-type indoor space and construct an indoor navigation platform.

Defining a Model for Integrating Indoor and Outdoor Network Data to Support Seamless Navigation Applications

“…IndoorGML is a standard published by OGC for representing, storing, and exchanging indoor spatial information and has been utilized widely in studies for navigation applications [38][39][40][41]. For example, Jung and Lee (2017) [38] extended IndoorGML to accommodate the representation of spaces using omnidirectional images for indoor patrol service.…”

“…For example, Jung and Lee (2017) [38] extended IndoorGML to accommodate the representation of spaces using omnidirectional images for indoor patrol service. Ahn et al ( 2020) [39] used a spatial data fusion approach to integrate said images directly to IndoorGML data. Furthermore, approaches to generate IndoorGML data from IFC and OpenStreetMaps have been presented by Khan et al (2014) [40] and Mirvahabi and Abbaspour (2015) [41], respectively.…”

“…The Transfer_Link is represented by the Transfer_Link class, inheriting from the tran- Spatial relationships can robustly establish connections between datasets without data conversion, eliminating procedures that modify the input data [39]. In an agent navigating from the building to the outdoors, the building exit represented by the indoor node V(U 7 ) will be the last indoor node.…”

Multisource Spatial Data Integration for Use Cases Applications

“…Other data fusion examples exist for 2D maps, mainly for update goals (Duckham, Worboys, 2005;Chen et al, 2013;Devogele, 2002) or for harmonising cross-border maps (Ledoux, Arroyo Ohori, 2017;Noardo et al, 2016). Furthermore, more recent studies take into account more complex information and multi-sensor 3D data (Wallgrün, Dylla, 2010;Dong et al, 2018;Ramos, Remondino, 2015;Zhu, Donia, 2013;Ahn et al, 2020).…”

“…Other cases report methods to merge the geometric information from multiple sources or sensors ('data fusion') (e.g. Dong et al, 2018;Ramos, Remondino, 2015;Zhu, Donia, 2013;Ahn et al, 2020). More complete database integration was also studied, but still for 2D GIS (Devogele et al, 1998, Uitermark et al, 2005.…”