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Article

Improved Indoor Positioning by Means of Occupancy Grid Maps Automatically Generated from OSM Indoor Data

Professorship Circuit and System Design, Chemnitz University of Technology, Reichenhainer Straße 70, D-09111 Chemnitz, Germany
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Academic Editors: Wolfgang Kainz, Lemonia Ragia, Cédric Grueau and Robert Laurini
ISPRS Int. J. Geo-Inf. 2021, 10(4), 216; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040216
Received: 2 February 2021 / Revised: 13 March 2021 / Accepted: 22 March 2021 / Published: 1 April 2021
In recent years, there is a growing interest in indoor positioning due to the increasing amount of applications that employ position data. Current approaches determining the location of objects in indoor environments are facing problems with the accuracy of the sensor data used for positioning. A solution to compensate inaccurate and unreliable sensor data is to include further information about the objects to be positioned and about the environment into the positioning algorithm. For this purpose, occupancy grid maps (OGMs) can be used to correct such noisy data by modelling the occupancy probability of objects being at a certain location in a specific environment. In that way, improbable sensor measurements can be corrected. Previous approaches, however, have focussed only on OGM generation for outdoor environments or require manual steps. There remains need for research examining the automatic generation of OGMs from detailed indoor map data. Therefore, our study proposes an algorithm for automated OGM generation using crowd-sourced OpenStreetMap indoor data. Subsequently, we propose an algorithm to improve positioning results by means of the generated OGM data. In our study, we used positioning data from an Ultra-wideband (UWB) system. Our experiments with nine different building map datasets showed that the proposed method provides reliable OGM outputs. Furthermore, taking one of these generated OGMs as an example, we demonstrated that integrating OGMs in the positioning algorithm increases the positioning accuracy. Consequently, the proposed algorithms now enable the integration of environmental information into positioning algorithms to finally increase the accuracy of indoor positioning applications. View Full-Text
Keywords: occupancy grid maps; OpenStreetMap; indoor maps; indoor positioning; ultra-wideband occupancy grid maps; OpenStreetMap; indoor maps; indoor positioning; ultra-wideband
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MDPI and ACS Style

Graichen, T.; Richter, J.; Schmidt, R.; Heinkel, U. Improved Indoor Positioning by Means of Occupancy Grid Maps Automatically Generated from OSM Indoor Data. ISPRS Int. J. Geo-Inf. 2021, 10, 216. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040216

AMA Style

Graichen T, Richter J, Schmidt R, Heinkel U. Improved Indoor Positioning by Means of Occupancy Grid Maps Automatically Generated from OSM Indoor Data. ISPRS International Journal of Geo-Information. 2021; 10(4):216. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040216

Chicago/Turabian Style

Graichen, Thomas, Julia Richter, Rebecca Schmidt, and Ulrich Heinkel. 2021. "Improved Indoor Positioning by Means of Occupancy Grid Maps Automatically Generated from OSM Indoor Data" ISPRS International Journal of Geo-Information 10, no. 4: 216. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040216

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