Abstract
We present a mapping algorithm to compute large-scale magnetic field maps in indoor environments with approximate Gaussian process (GP) regression. Mapping the spatial variations in the ambient magnetic field can be used for 10-calization algorithms in indoor areas. To compute such a map, GP regression is a suitable tool because it provides predictions of the magnetic field at new locations along with uncertainty quantification. Because full GP regression has a complexity that grows cubically with the number of data points, approximations for GPs have been extensively studied. In this paper, we build on the structured kernel interpolation (SKI) framework, speeding up inference by exploiting efficient Krylov subspace methods. More specifically, we incorporate SKI with derivatives (D-SKI) into the scalar potential model for magnetic field modeling and compute both predictive mean and covariance with a complexity that is linear in the data points. In our simulations, we show that our method achieves better accuracy than current state-of-the-art methods on magnetic field maps with a growing mapping area. In our large-scale experiments, we construct magnetic field maps from up to 40000 three-dimensional magnetic field measurements in less than two minutes on a standard laptop.
Original language | English |
---|---|
Title of host publication | Proceedings of the 26th International Conference on Information Fusion, FUSION 2023 |
Publisher | IEEE |
Number of pages | 7 |
ISBN (Electronic) | 979-8-8903-4485-4 |
DOIs | |
Publication status | Published - 2023 |
Event | 26th International Conference on Information Fusion, FUSION 2023 - Charleston, United States Duration: 27 Jun 2023 → 30 Jun 2023 |
Conference
Conference | 26th International Conference on Information Fusion, FUSION 2023 |
---|---|
Country/Territory | United States |
City | Charleston |
Period | 27/06/23 → 30/06/23 |
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.
Keywords
- Gaussian process regression
- indoor localization
- magnetic field maps
- structured kernel interpolation