Crowdsourced radiomap for room-level place recognition in urban environment

The proliferation of WLAN infrastructures has facilitated numerous indoor localization techniques using WLAN fingerprints. In particular, identifying a room or a place in urban environments could be usefully utilized in many application domains such as ubiquitous health. However, it is not straightforward how to bootstrap such a localization system because WLAN fingerprints of all places must be available in advance. In this paper, we propose a crowdsourcing approach for indoor place recognition. The key idea is to build an open participatory system through which users can contribute fingerprints. As the database size increases, it can provide place recognition service. We conducted an extensive experimental study at a university campus to demonstrate the performance of the proposed method in terms of recognition accuracy. We also studied key factors that could undermine the crowdsourcing approach such as fingerprint density, incorrect contribution, uneven contribution, and device heterogeneity.