AI RESEARCH
Magnetic Indoor Localization through CNN Regression and Rotation Invariance
arXiv CS.LG
•
ArXi:2604.22896v1 Announce Type: cross 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.