Magnetic Indoor Localization through CNN Regression and Rotation Invariance
Indoor positioning is an essential technology for a wide range of applications in GNSS-denied environments, including indoor navigation and IoT systems. Combining convolutional neural networks (CNNs) and magnetic field-based features offers a low-cost, infrastructure-free solution for precise positioning. While magnetic fingerprints are a promising approach for indoor positioning, models trained on raw 3D magnetometer data are highly sensitive to device orientation. We address this by using two rotation invariant features derived from the 3D magnetic field: the norm (Mn) and the projection onto the gravity axis (Mg). We train a lightweight 7-layer dilated CNN (MagNetS/XL) on magnetic sequ...
| Authors | Helge Rosé, Konstantin Klipp, Tom Koubek et al. |
|---|---|
| Primary category | cs.RO |
| Published | 24 April 2026 |
| Last updated at source | 1 June 2026 |
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