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Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes
Aalto Univ, Dept Comp Sci, Espoo 02150, Finland;IndoorAtlas Ltd, Helsinki 00100, Finland.ORCID iD: 0000-0002-0958-7886
Univ Cambridge, Dept Engn, Cambridge CB2 1PZ, England.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0002-4634-7240
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0001-5183-234X
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2018 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 34, no 4, p. 1112-1127Article in journal (Refereed) Published
Abstract [en]

Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian nonparametric probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior to the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field. The model is shown to work well in practice in different applications. We demonstrate mapping of the magnetic field both with an inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 34, no 4, p. 1112-1127
Keywords [en]
Gaussian process (GP), magnetic field, mapping, Maxwell's equations, online representation
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:uu:diva-363994DOI: 10.1109/TRO.2018.2830326ISI: 000442341000010OAI: oai:DiVA.org:uu-363994DiVA, id: diva2:1260914
Funder
Swedish Research CouncilSwedish Foundation for Strategic Research Available from: 2018-11-05 Created: 2018-11-05 Last updated: 2018-11-05Bibliographically approved

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Solin, ArnoWahlström, NiklasSchön, Thomas B.
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