From mapping to indoor semantic queries: Enabling zero-effort indoor environmental sensing. (15th February 2017) - Luo, Chengwen; Cheng, Long; Hong, Hande; Sankaran, Kartik; Chan, Mun Choon; Li, Jianqiang; Ming, Zhong
Abstract: Understanding indoor environment in an automatic way is of great importance to mobile and pervasive computing. In this paper, we present Zeus, a smartphone-based opportunistic sensing system that automatically constructs indoor maps by merging crowdsourced walking trajectories captured through smartphone inertial sensing. Most importantly, widely used indoor semantics, such as stairs, escalators, elevators and doors, are also automatically detected and annotated to the constructed maps in the same inference process. Since the final inferred maps provide locations of the different indoor semantics together with localization database, Zeus enables real-time location-based semantic queries. The evaluation result shows that Zeus accurately infers semantic-annotated indoor maps, and provide accurate semantic query in different indoor environments.