Indoor Localization Fusing Inertial Navigation With Monocular Depth Estimation in Federated Learning Framework With Data Heterogeneity

In the field of indoor positioning, both user privacy and positioning accuracy are of paramount importance. Using mobile phones for indoor positioning is a simple, effective, and cost-efficient method; however, it poses privacy concerns for users and often fails to meet the precision required for most navigation needs. Therefore, within a heterogeneous federated learning (FL) framework, we propose a method that utilizes smartphone Inertial Measurement Unit (IMU) data to obtain two-dimensional vector velocity, which is then further integrated to obtain indoor relative positions. Additionally, we estimate user distance based on commonly available indoor surveillance cameras data. The user receives the distance data forwarded by the edge server and implements fusion localization, which overcomes the limitations of IMU-based positioning. The proposed positioning system, named Federated IMU Monocular Ranging Location (FedIMRLoc), is validated through dataset simulation and real-world indoor environment experiments, demonstrating its superiority. Compared to FedAVG, Central Learning, and FedAVG+UWB, FedIMRLoc exhibited an average improvement in localization accuracy of 64.7%, 30.2%, and 27%.