Enhancing Camera-Based Multimodal Indoor Localization With Device-Free Movement Measurement Using WiFi

Indoor localization is of great significance to a wide range of applications in the era of mobile computing. The maturity of the computer vision techniques and the ubiquity of embedded sensors in commercial off-the-shelf (COTS) smartphones shed the light on the submeter localization services for indoor environment. The state-of-the-art indoor localization works suffer from high-cost deployment and inaccurate results due to the coarse readings from internal measurement units (IMUs) sensors in the smartphones. In this article, we mainly innovate in introducing the WiFi-sensing technology to extract the distance information in a low-cost and device-free manner. Along with the computer vision technology, we model and implement an accurate and easy-to-deploy system for indoor localization. This system enhances indoor localization with multimodal sensing via two images, IMU sensors reading and CSI of WiFi signal. Specifically, we first model and design camera-based, sensor and WiFi-assisted indoor localization and propose several algorithms in this model. We then implement a prototype with smartphones and commercial WiFi devices and evaluate it in several distinct indoor environments. The experimental results show that 92-percentile error is within 0.2 m for indoor targets which sheds light on submeter indoor localization.