MapFi: Autonomous Mapping of Wi-Fi Infrastructure for Indoor Localization

Xinyu Tong, Han Wang, Xiulong Liu, Wenyu Qu

Abstract: Wi-Fi CSI-based indoor localization systems can realize decimeter-level localization accuracy. However, these systems require that the location and antenna array orientation of Wi-Fi Access Point (AP) are known in advance, which makes it impractical for large-scale deployment. In this paper, we present MapFi, which can realize autonomous mapping of Wi-Fi infrastructure without labor-intensive site survey. To this end, we focus on addressing three problems. First, as there will be diverse layouts of devices and antennas with respective to numerous and heterogeneous Wi-Fi APs, we propose a general method to estimate AoA and generate the Wi-Fi map. Second, while the existing systems can provide a promising median localization accuracy, tail performance is usually far worse. Consequently, we develop a revision method to reduce tail errors. Third, when deployed in large-scale indoor environment, obstacles and long-distance communication might incur failed CSI collection. Therefore, we segment Wi-Fi APs into groups and finally merge these groups to generate the global Wi-Fi map. We conduct experiments in different scenarios to verify the proposed methods. The experimental results show that we can realize the <inline-formula><tex-math notation="LaTeX">$80\%$</tex-math></inline-formula> localization error within <inline-formula><tex-math notation="LaTeX">$1.15m$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$0.74m$</tex-math></inline-formula> in office room and open space respectively, which is as accurate as localization systems requiring known Wi-Fi map.