Developing a Model to Express Spatial Relationships on Omnidirectional Images for Indoor Space Representation to Provide Location-Based Services

The unavailability and fragmentation of spatial data are challenges in creating realistic representations of objects and environments in the real world, especially indoors. Among the numerous methods for representing indoor space, the existing research has shown the efficiency and effectiveness of using omnidirectional images. However, they lack information on spatial relationships, so spatial datasets such as the Node-Relation Structure (NRS) must be used to provide location-based services (LBS). This study proposes a method for embedding topological relationships on omnidirectional images, and correspondingly extracting NRS data to enable the expression of these relationships on the images. These relationships include the connectivity of relations among the indoor subunits, and the containment of relations between the spaces and indoor facilities on the image data. This model allows for the construction of an image-based indoor space representation for providing LBS. This paper also demonstrates an approach to utilizing these datasets through an image-based platform that enables the direct performance of spatial analysis relevant to LBS on the images, and provides the accurate visualization and expression of the spaces and indoor points of interest data representing indoor facilities. This paper also includes an experimental implementation to demonstrate the potential of our model for providing an efficient space representation and the handling of basic spatial queries for indoor space applications.