Abstract
We present a lightweight magnetic field simultaneous localisation and mapping (SLAM) approach for drift correction in odometry paths, where the interest is purely in the odometry and not in map building. We represent the past magnetic field readings as a one-dimensional trajectory against which the current magnetic field observations are matched. This approach boils down to sequential loop-closure detection and decision-making, based on the current pose state estimate and the magnetic field. We combine this setup with a path estimation framework using an extended Kalman smoother which fuses the odometry increments with the detected loop-closure timings. We demonstrate the practical applicability of the model with several different real-world examples from a handheld iPad moving in indoor scenes.
Original language | English |
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Title of host publication | Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2024 |
Publisher | IEEE |
Number of pages | 7 |
ISBN (Electronic) | 979-8-3503-6803-1 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2024 - Pilsen, Czech Republic Duration: 4 Sept 2024 → 6 Sept 2024 |
Publication series
Name | IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems |
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ISSN (Print) | 2835-947X |
ISSN (Electronic) | 2767-9357 |
Conference
Conference | 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2024 |
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Country/Territory | Czech Republic |
City | Pilsen |
Period | 4/09/24 → 6/09/24 |
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-careOtherwise 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.