Occupancy Grid Map Generation from OSM Indoor Data for Indoor Positioning Applications

Thomas Graichen; Rebecca Schmidt; Julia Richter; Ulrich Heinkel

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Paper

Occupancy Grid Map Generation from OSM Indoor Data for Indoor Positioning Applications

Topics: Cartography and Geodesy; Geographic Information Retrieval; Geospatial Information and Technologies; GPS and Location Detection; Industrial Applications; Intelligent Data Fusion; Spatial Analysis and Integration; Transportation Engineering; Urban and Regional Planning

In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management GISTAM - Volume 1, 168-174, 2020

Authors: Thomas Graichen ; Rebecca Schmidt ; Julia Richter and Ulrich Heinkel

Affiliation: Professorship Circuit and System Design, Chemnitz University of Technology, Reichenhainer Straße 70, Chemnitz, Germany

Keyword(s): Occupancy Grid Maps, OpenStreetMap, Indoor Maps, Indoor Localisation.

Abstract: 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 algor ithm for automated OGM generation using crowd-sourced OpenStreetMap indoor data. Our experiments with nine different building map datasets demonstrate that the proposed method provides reliable OGM outputs. The proposed algorithm now enables the integration of environmental information into positioning algorithms to finally increase the accuracy of indoor positioning applications. (More)

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Paper citation in several formats:

Graichen, T.; Schmidt, R.; Richter, J. and Heinkel, U. (2020). Occupancy Grid Map Generation from OSM Indoor Data for Indoor Positioning Applications. In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM; ISBN 978-989-758-425-1; ISSN 2184-500X, SciTePress, pages 168-174. DOI: 10.5220/0009348501680174

@conference{gistam20,
author={Thomas Graichen. and Rebecca Schmidt. and Julia Richter. and Ulrich Heinkel.},
title={Occupancy Grid Map Generation from OSM Indoor Data for Indoor Positioning Applications},
booktitle={Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM},
year={2020},
pages={168-174},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009348501680174},
isbn={978-989-758-425-1},
issn={2184-500X},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM
TI - Occupancy Grid Map Generation from OSM Indoor Data for Indoor Positioning Applications
SN - 978-989-758-425-1
IS - 2184-500X
AU - Graichen, T.
AU - Schmidt, R.
AU - Richter, J.
AU - Heinkel, U.
PY - 2020
SP - 168
EP - 174
DO - 10.5220/0009348501680174
PB - SciTePress