Scalable magnetic field SLAM in 3D using Gaussian process maps

Manon Kok, Arno Solin

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

20 Citations (Scopus)
24 Downloads (Pure)

Abstract

We present a method for scalable and fully 3D magnetic field simultaneous localisation and mapping (SLAM) using local anomalies in the magnetic field as a source of position information. These anomalies are due to the presence of ferromagnetic material in the structure of buildings and in objects such as furniture. We represent the magnetic field map using a Gaussian process model and take well-known physical properties of the magnetic field into account. We build local maps using three-dimensional hexagonal block tiling. To make our approach computationally tractable we use reduced-rank Gaussian process regression in combination with a Rao-Blackwellised particle filter. We show that it is possible to obtain accurate position and orientation estimates using measurements from a smartphone, and that our approach provides a scalable magnetic field SLAM algorithm in terms of both computational complexity and map storage.

Original languageEnglish
Title of host publicationProceedings of the 21st International Conference on Information Fusion 2018 (FUSION 2018)
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages1353-1360
ISBN (Electronic)978-0-9964527-7-9
ISBN (Print)978-0-9964527-6-2
DOIs
Publication statusPublished - 2018
Event21st International Conference on Information Fusion, FUSION 2018 - Cambridge, United Kingdom
Duration: 10 Jul 201813 Jul 2018

Conference

Conference21st International Conference on Information Fusion, FUSION 2018
CountryUnited Kingdom
CityCambridge
Period10/07/1813/07/18

Bibliographical note

Accepted Author Manuscript

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