CANOE: Opportunistic calibration assisted micro navigation in dense open environments

摘要: Crowded musical concerts (and open densely crowded environments in general) pose significant hurdles to people trying navigate their family and friends. Calls get frequently dropped due crowd spikes loud noise makes cellular voice communication cumbersome. Low visibility such dense render traditional navigation solutions ineffective. Global Positioning System (GPS) Pedestrian Dead Reckoning (PDR) based systems are known be error prone inefficient for micro-navigation scenarios. Incorrect delayed position fixes result high convergence time frequent oscillating about the route destination, leading a frustrating user experience, specially crowd. To address dual issue of experience we propose CANOE - novel tunnel methodology which allows users flexibility using own sense wade through crowd, while concurrently best effort opportunistic constrain drifts. It also reduces dependency constantly looking at phone updates information. Extensive simulation results show that our algorithm can achieve 2–4x improvement reduced oscillations under different scenarios as compared state-of-the-art approaches. We conduct live experiments with 22 15 smartphones find contain location drifts within 1–2m signal degraded where PDR range around 6–7m short walks 25m.