Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes

Arno Solin, Manon Kok, Niklas Wahlstrom, Thomas B. Schon, Simo Sarkka

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

12 Sitaatiot (Scopus)
113 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
Sivut1112-1127
JulkaisuIEEE Transactions on Robotics
Vuosikerta34
Numero4
DOI - pysyväislinkit
TilaJulkaistu - 2018
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

Sormenjälki Sukella tutkimusaiheisiin 'Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

  • Laitteet

    Science-IT

    Mikko Hakala (Manager)

    Perustieteiden korkeakoulu

    Laitteistot/tilat: Facility

  • Siteeraa tätä