A Novel Multi-Modal Feature-Level Fusion Scheme for High Accurate Indoor Localization

Smartphone based indoor localization has been widely explored to meet the demand for high-precision, cost-effective solutions within indoor positioning systems. Prior methodologies have predominantly concentrated on enhancing the localization accuracy inherent in single-sensor-based localization, thereby potentially constraining their applications. In this article, we introduce an innovative WiFi-visual multi-modal framework designed for achieving high-precision, low-cost indoor localization. The initial stages involve the utilization of two modal-specific encoders for feature extraction. Then, we propose a multi-modal fusion transformer to incorporate the global context and adopt element-wise summation to fuse the two deep features. Finally, we leverage a task-specific decoder for position prediction. During training, WiFi-aided learning is adopted to confer enhanced reliability to the labeling process. The efficacy of the proposed method is assessed in a corridor scenario and a laboratory scenario of a typical building. During testing, we propose backend optimization to get smooth and globally consistent location predictions. Experimental outcomes affirm that the proposed WiFi-visual multi-modal approach introduced herein attains a localization accuracy of less than half a meter, concurrently exhibiting commendable runtime efficiency.