LAUSR

Indoor-location-based services rely on indoor maps, which are yet widely available despite numerous efforts from the industry. Existing solutions employ costly hardware (e.g., lidar) to achieve accurate mapping of indoor environments, or resort to crowdsourcing for floor plan generation at the cost of precision due to inaccurate inertial sensing. In this article, we leverage a new opportunity enabled by recent advances in RF-based inertial tracking that achieves centimeter accuracy. We present EZMAP, a high-accuracy, low-cost floor plan construction system that fuses RF and inertial sensing. EZMAP combines the fine-grained yet local information from RF tracking with the coarse grained but global contexts from inertial sensing (e.g., magnetic field strength), which together makes for an accurate map. Our system employs a robot for trajectory collection and requires only a single access point to be arbitrarily installed in the space, both of which are widely available nowadays. Furthermore, it can generate a map even only a small amount of data is available, allowing it to scale for different buildings, such as malls, office buildings, and homes with little cost. We validate the performance using a Dji RoboMaster S1 robot with commodity WiFi in three different buildings. The results show that our system can efficiently generate faithful maps for the targeted areas. With the ubiquity of the WiFi infrastructure and the rise of home robots, we believe our approach will pave the way for pervasive indoor maps services.