Indoor SLAM using a foot-mounted IMU and the local magnetic field

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

Abstract

In this paper, a simultaneous localization and mapping algorithm for tracking the motion of a pedestrian with a foot-mounted inertial measurement unit is proposed. The algorithm uses two maps, namely, a motion map and a magnetic field map. The motion map captures typical motion patterns of pedestrians in buildings that are constrained by e.g. corridors and doors. The magnetic map models local magnetic field anomalies in the environment using a Gaussian process model and uses them as position information. These maps are used in a Rao-Blackwellized particle filter to correct the pedestrian position and orientation estimates from the pedestrian dead-reckoning. The pedestrian dead-reckoning is computed using an extended Kalman filter with zero-velocity updates. The algorithm is validated using experimental sequences and the results show the efficacy of the algorithm in localizing pedestrians in indoor environments.
Original languageEnglish
Title of host publicationProceedings of the 25th International Conference on Information Fusion (FUSION 2022)
PublisherIEEE
Number of pages7
ISBN (Electronic)978-1-7377-4972-1
DOIs
Publication statusPublished - 2022
Event25th International Conference on Information Fusion, FUSION 2022 - Linköping, Sweden
Duration: 4 Jul 20227 Jul 2022

Conference

Conference25th International Conference on Information Fusion, FUSION 2022
Country/TerritorySweden
CityLinköping
Period4/07/227/07/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
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.

Keywords

  • Inertial sensors
  • , magnetic field anomalies
  • indoor localization
  • Rao-Blackwellized particle filter
  • SLAM

Fingerprint

Dive into the research topics of 'Indoor SLAM using a foot-mounted IMU and the local magnetic field'. Together they form a unique fingerprint.

Cite this