Few-Shot Cross-Domain Indoor Localization via Multi-Modal Feature Refinement

Artikel i vetenskaplig tidskrift, 2026

Fingerprint-based indoor localization is a critical enabling technology for Internet of Things (IoT) applications, where the primary challenges stem from complex environmental variability and prohibitive costs of data collection and labeling. This paper introduces a cross-domain multi-modal indoor localization framework that effectively combines visual and WiFi signals using few-shot learning techniques, achieving improved localization performance with minimal training data. We derive an upper bound on the generalized transfer localization error. Based on this bound, our learning-based approach applies feature-level knowledge distillation from pre-trained localization models. This process systematically calibrates discrepancies in feature distributions between the source and target environments. As a result, the proposed method significantly reduces the dependence on large labeled datasets. Experimental results demonstrate that our proposed method achieves substantial improvements over state-of-the-art localization models, with a mean localization error of 0.247 meters across diverse indoor environments, while requiring substantially fewer labeled samples in the target domain.