Incrementally-deployable Indoor Navigation with Automatic Trace Generation - Shu, Yuanchao; Li, Zhuqi; Karlsson, Börje F.; Lin, Yiyong; Moscibroda, Thomas; Shin, Kang (IEEE International Conference on Computer Communications (INFOCOM), 2019)
Despite years of research attention, localization-based indoor navigation has not found wide-spread practical use, largely due to the high burden on deployment and bootstrapping. Lightweight peer-to-peer navigation systems that use a leader-follower model have recently been proposed to alleviate these burdens. However, typical peer-to-peer navigation suffers from poor scalability and flexibility as navigation is only possible over pre-collected leader paths. In this paper, we present FOLLOWUS, an easily-deployable (bootstrap-free) and scalable indoor navigation system. In addition to robust navigation through real-time trace-following, FOLLOWUS integrates cloud services to process and combine traces at large scale. Optionally, it can also leverage floor plans to further enhance navigation efficiency. We design and implement FOLLOWUS, including mobile app and cloud services. Experimental results from a company-internal beta release show that 91% of FOLLOWUS’ spatial errors on reaching destinations to be 3m or less, and 95% of navigation instructions are shown to users within a 4step error margin during navigation.