Abstract
The applicability of characteristics of local magnetic fields for more precise determination of localization of subjects and/or objects in indoor environments, such as railway stations, airports, exhibition halls, showrooms, or shopping centers, is considered. An investigation has been carried out to find out whether and how low-cost magnetic field sensors and mobile robot platforms can be used to create maps that improve the accuracy and robustness of later navigation with smartphones or other devices.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Saxena, A., Zawodniok, M.: Indoor positioning system using geomagnetic field. In: IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, Montevideo, Uruguay (2014)
Iatcheva, I.I., Andreev, A.D., Stancheva, R.D., Lilyanova, I.T.: Electromagnetic flow meter field distribution maximizing device sensitivity. Mater. Sci. Forum 856, 157–162 (2016)
Shockley, J.A., Raquet, J.F.: Navigation of Ground vehicles using magnetic field variations. Navigation 61, 237–252 (2014)
Li, B., Gallagher, T., Dempster, A., Rizos, C.: How feasible is the use of magnetic field alone for indoor positioning? In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sydney, Australia (2012)
Hensel, S., Marinov, M.B., Schmitt, M.: System setup for synchronized visual-inertial localization and mapping. In: 2020 XXIX International Scientific Conference Electronics (ET), Sozopol, Bulgaria (2020)
Frassl, M., Angermann, M., Lichtenstern, M., Robertson, P., Julian, B., Doniec, M.: Magnetic maps of indoor environments for precise localization of legged and non-legged locomotion. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan (2013)
Filipov, F.: Forschung und Implementierung von Technologien für die Navigation in Innenräumen, Sofia: Dissertation, Technische Universität Sofia (2016)
Kim, H.S., Seo, W., Baek, K.R.: Indoor positioning system us-ing magnetic field map navigation and an encoder system. Sensors 17(3), 651 (2017)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006)
Jain, A., Nghiem, T., Morari, M., Mangharam, R.: Learning and control using gaussian processes. In: 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS), Porto, Portugal (2018)
Quigley, M., Gerkey, B., Smart, W.: Programming Robots with ROS. O’Reilly Media, Sebastopol (2015)
Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. Rob. 23, 34–46 (2007)
Minasny, B., McBradley, B.: Spatial prediction of soil properties using EBLUP with the Matérn covariance function. Geoderma 140, 323–456 (2007)
Hensel, S., Marinov, M.B.: Estimation of magnetic field maps with mobile platforms. In: Proceedings of the 7th FDIBA Conference, Sofia, 30 November–1 December 2017
Kokolanski, Z.: Hardware techniques for improving the calibration performance of direct resistive sensor-to-microcontroller interface. Metrol. Meas. Syst. 20(1), 529–542 (2013)
Klein, L.: Sensor and Data Fusion. SPIE-International Society for Optical Engineering, Washington (2004)
Acknowledgments
This research is supported by the Bulgarian National Science Fund in the scope of the project “Exploration the application of statistics and machine learning in electronics” under contract number КП-06-H42/1.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Hensel, S., Marinov, M.B., Schwilk, T., Nikolov, D. (2021). Application of Gaussian Process Estimation for Magnetic Field Mapping. In: Perakovic, D., Knapcikova, L. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 382. Springer, Cham. https://doi.org/10.1007/978-3-030-78459-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-78459-1_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78458-4
Online ISBN: 978-3-030-78459-1
eBook Packages: Computer ScienceComputer Science (R0)