A Picture is Worth 1,000 Millimeters: Combining Vision and Wi-Fi to Improve Localization

In the past, researchers designed, deployed, and evaluated Wi-Fi based localization techniques in order to locate users and devices without adding extra or costly infrastructure. However, as infrastructure deployments change, one must reexamine the role of Wi-Fi localization. Today, cameras are becoming increasingly deployed, and therefore this work examines how contextual and vision data obtained from cameras can be integrated with Wi-Fi localization techniques. We present an approach called CALM that works on commodity APs and cameras. Our approach contains several contributions: a camera line fitting technique to restrict the search space of candidate locations, single AP and camera localization via a deprojection scheme inspired from 3D cameras, simple and robust AP weighting that analyzes the context of users via the camera, and a new virtual camera methodology to scale analysis. We motivate our scheme by analyzing real camera and AP topologies from a major vendor. Our evaluation over 9 rooms and 102,300 wireless readings shows CALM can obtain decimeter-level accuracy, improving performance over previous Wi-Fi techniques like FTM by $2.7\times$ and SpotFi by $2.3\times$.