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Gunduz, M.; Işıkdağ, Ümit; Başaraner, Melih

doi:10.5194/isprsarchives-xli-b4-289-2016

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…Taking a look at LBS targeted at industrial applications there are commercially available systems providing the services positioning and identification as well as geofencing for indoor production environments. Two examples of this are Ubisense SmartFactory [13] and Quantitec IntraNav [14]. Both solutions are extensible via their SDK, but remain proprietary solutions.…”

Section: State Of the Art And Related Workmentioning

confidence: 99%

Semantically enriched spatial modelling of industrial indoor environments enabling location-based services

Wendt¹,

Brand²,

Schüppstuhl³

2018

Tagungsband Des 3. Kongresses Montage Handhabung Industrieroboter

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This paper presents a concept for a software system called RAIL representing industrial indoor environments in a dynamic spatial model, aimed at easing development and provision of location-based services. RAIL integrates data from different sensor modalities and additional contextual information through a unified interface. Approaches to environmental modelling from other domains are reviewed and analyzed for their suitability regarding the requirements for our target domains; intralogistics and production. Subsequently a novel way of modelling data representing indoor space, and an architecture for the software system are proposed.

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Section: State Of the Art And Related Workmentioning

confidence: 99%

Semantically enriched spatial modelling of industrial indoor environments enabling location-based services

Wendt¹,

Brand²,

Schüppstuhl³

2018

Tagungsband Des 3. Kongresses Montage Handhabung Industrieroboter

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“…Over the last few decades, indoor maps have attracted increasing attention from both commercial enterprises and scientific institutions [7]. Some popular indoor map services include Google Maps, Apple Maps, and Bing Maps, which offer indoor maps of popular public places in the form of ISPRS Int.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scale Representation of Point-of-Interest (POI) Features in Indoor Map Visualization

Xiao

Yang

et al. 2020

IJGI

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As a result of the increasing popularity of indoor activities, many facilities and services are provided inside buildings; hence, there is a need to visualize points-of-interest (POIs) that can describe these indoor service facilities on indoor maps. Over the last few years, indoor mapping has been a rapidly developing area, with the emergence of many forms of indoor representation. In the design of indoor map applications, cartographical methodologies such as generalization and symbolization can make important contributions. In this study, a self-adaptive method is applied for the design of a multi-scale and personalized indoor map. Based on methods of map generalization and multi-scale representation, we adopt a scale-adaptive strategy to visualize the building structure and POI data on indoor maps. At smaller map scales, the general floor distribution and functional partitioning of each floor are represented, while the POI data are visualized by simple symbols. At larger map scales, the detailed room distribution is displayed, and the service information of the POIs is described by detailed symbols. Different strategies are used for the generalization of the background building structure and the foreground POI data to ensure that both can satisfy real-time performance requirements. In addition, for better personalization, different POI data, symbols or color schemes are shown to users in different age groups, with different genders or with different purposes for using the map. Because this indoor map is adaptive to both the scale and the user, each map scale can provide different map users with decision support from different perspectives.

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

2020

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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.

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Untitled

Cited by 4 publications

References 35 publications

Semantically enriched spatial modelling of industrial indoor environments enabling location-based services

Semantically enriched spatial modelling of industrial indoor environments enabling location-based services

A Multi-Scale Representation of Point-of-Interest (POI) Features in Indoor Map Visualization

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

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