A fast and practical method of indoor localization for resource-constrained devices with limited sensing

Abstract

We describe and experimentally demonstrate a practical method for indoor localization using measurements obtained from resource-constrained devices with limited sensing capabilities. We focus on handheld/mobile devices but the method can be useful for a variety of wearable devices. Our system works with sparse WiFi or image-based measurements, avoiding laborious site surveying for dense signal maps and runs in real-time. It uses Conditional Random Fields to infer the most probable sequence of agent positions from a known floor plan, dead reckoning and sparse absolute position estimates. Our solution leverages known topology of the environment by pre-computing allowed motion sequences of an agent, which are then used to constraint the motion inferred from the sensory data. The system is evaluated in a typical office building, demonstrating good accuracy and robustness to sparse, low-quality measurements.