Improving floor localization accuracy in 3D spaces using barometer

Abstract

Technologies such as Wifi and BLE have been proven to be effective for indoor localization in two dimensional spaces with sufficiently good accuracy but the same techniques have large margin of errors when it comes to three dimensional spaces. Popular 3D spaces such as malls or airports are marked by distinct structural features - atrium/hollow space and large corridors which reduces spatial variability of WiFi and BLE signal strengths leading to erroneous location prediction. A large fraction of these errors can be attributed to vertical jumps where the predicted location has same horizontal coordinate as the actual location but differs in the vertical coordinate. Smartphones now come equipped with barometer sensor which could be used to solve this problem and create 3D localization solution having better accuracy. Research shows that the barometer can be used to determine relative vertical movement and its direction with nearly 100% accuracy. However exact floor prediction requires repeated calibration of the barometer measurements as pressure values vary significantly across device, time and locations. In this paper we present a method of automatically calibrating smartphone embedded barometers to provide accurate 3D localization. Our method combines a probabilistic learning method with a pressure drift elimination algorithm. We also show that when the floor value is accurately predicted, Wifi localization accuracy improves by 25% for 3D spaces. We validate our techniques in a real shopping mall and provide valuable insights from practical experiences.