Heterogeneous Feature Fusion Approach for Multi-Modal Indoor Localization

The demand for high-precision localization continues to grow rapidly with the development of information technology. Localization techniques based on wireless signals and visible light images have become the mainstream approach for achieving accurate and precise localization. However, directly utilizing multi-modal data for localization often overlooks the complex relationships between different modalities, particularly in terms of spatial and temporal features at varying scales. In this paper, we present a novel high-precision indoor localization method that effectively aligns the spatiotemporal dimensions of different modalities and extracts features efficiently using a shared neural network. To further enhance the extraction of relevant features from the multi-modal data, we propose a fusion network based on multi-modal channels that effectively minimize disparities, thereby significantly improving localization accuracy. Through extensive verification using a prototype system, our proposed solution demonstrates outstanding localization accuracy, achieving an average localization error of only 0.22m.