Magnetic field norm SLAM using Gaussian process regression in foot-mounted sensors

Frida Viset, Jan Tommy Gravdahl, Manon Kok

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

5 Citations (Scopus)
34 Downloads (Pure)


We propose an application of magnetic field norm simultaneous localisation and mapping to measurements from a foot-mounted sensor for pedestrian navigation. The algorithm is, to the best of the authors’ knowledge, the first three dimensional drift-compensating indoor navigation method using only accelerometer, gyroscope and magnetometer measurements that does not rely on assumptions about the spatial structure of the indoor environment. We use a Rao-Blackwellized particle filter to simultaneously and recursively estimate the magnetic field norm map using reduced rank Gaussian process regression, and the position and orientation of the sensor. Our experiments demonstrate that our algorithm results in a drift-free position estimate using measurements collected from a foot-mounted sensor while walking around inside a hallway.
Original languageEnglish
Title of host publicationProceedings of the European Control Conference (ECC 2021)
ISBN (Electronic)978-9-4638-4236-5
ISBN (Print)978-1-6654-7945-5
Publication statusPublished - 2021
Event2021 European Control Conference (ECC) - Virtual , Netherlands
Duration: 29 Jun 20212 Jul 2021


Conference2021 European Control Conference (ECC)

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


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