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.
|Title of host publication
|Proceedings of the 25th International Conference on Information Fusion (FUSION 2022)
|Number of pages
|Published - 2022
|25th International Conference on Information Fusion, FUSION 2022 - Linköping, Sweden
Duration: 4 Jul 2022 → 7 Jul 2022
|25th International Conference on Information Fusion, FUSION 2022
|4/07/22 → 7/07/22
Bibliographical noteGreen 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
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- Inertial sensors
- , magnetic field anomalies
- indoor localization
- Rao-Blackwellized particle filter