Abstract
Although there is a considerable progress in mapping the indoor places, most of the existing techniques are either expensive or difficult to apply. In this paper, we articulate our view on the future of indoor mapping, which is based on customized, crowdsourced and scalable approaches. On the basis of this approach, we discuss the research challenges that we envision to face in this world of customized bootstrapping and diverse techniques and services. We focus our interest in the combination of multiple of indoor mapping generation techniques and discuss challenges and various indoor mapping techniques. We introduce our adaptive method for bootstrapping the procedure of indoor mapping in multiple ways through intermediate services. Those emerged services enable the obtaining of useful data for this procedure, while they increase the quality of those data. We discuss the necessary components for such approach and we give an example of a bootstrapping procedure.
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This work is part of the TUM Living Lab Connected Mobility project and has been funded by the Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie.
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Pipelidis, G., Prehofer, C., Gerostathopoulos, I. (2019). Bootstrapping the Dynamic Generation of Indoor Maps with Crowdsourced Smartphone Sensor Data. In: Ragia, L., Laurini, R., Rocha, J. (eds) Geographical Information Systems Theory, Applications and Management. GISTAM 2017. Communications in Computer and Information Science, vol 936. Springer, Cham. https://doi.org/10.1007/978-3-030-06010-7_5
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DOI: https://doi.org/10.1007/978-3-030-06010-7_5
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