Indoor geolocation based on earth magnetic field

One of the most recent and popular tourism application is the virtual visits (virtual tour guide) which are based on Augmented/Virtual Reality technology (AR/VR). Such systems suffer from the lack of precise indoor geolocation of visitors within the cultural heritage site. This issue can be resolved by using smartphone (and tablets) embedded sensors, which can catch a huge number of data on visitor behaviour (acceleration, orientation, etc.). This data can be fused using dedicated methods (Extended Kalman Filter (EKF), Particle Filter, etc.) in order to estimate visitor position. However, such algorithms are highly dependent on: sampling time, sensors technologie, number of used sensors, etc. With this kind of solution the error in position estimation growth with the covered distance. In this paper, we present an hybrid solution for reducing error in time without additional infrastructure such Wifi, etc. For this, we present an indoor geolocation solution based on smartphone inertial sensors and earth magnetic field. The proposed method is divided into three phases: heading estimation using 3 sensors (accelerometer, compass and gyroscope) with an Extended Kalman Filter, steps detection and step length estimation, error correction using fingerprinting. The proposed solution is implemented on a smartphone (Samsung-Galaxy S7 based on Android OS) and maintain the error under of 1.5m.