GUIDE: Smartphone sensors‐based pedestrian indoor localization with heterogeneous devices

Abstract

SummaryA plethora of indoor localization systems based on Wi‐Fi, radio frequency chips, ultra‐wide‐band, and bluetooth have been proposed, yet these systems do not work when the infrastructure is absent. On the other hand, infrastructure less systems benefit mostly from off‐the‐shelf smartphone sensors and do not need additional hardware. This study shows a similar indoor localization approach which turns smartphone built‐in sensors to good account. We take advantage of magnetic field strength fingerprinting approach to localize a pedestrian indoor. In addition, accelerometer and gyroscope sensors are utilized to find the pedestrian's traveled distance and heading estimation, respectively. Our aim is to solve the problem of device dependence by devising an approach that can perform localization using various smartphones in a similar fashion. We make the use of patterns of magnetic field strength to formulate the fingerprint database to achieve this goal. This approach solves two problems: need to update the database periodically and device dependence. We conduct experiments using Samsung Galaxy S8 and LG G6 for five different buildings with different dimensions in Yeungnam University, Republic of Korea. The evaluation is performed by following three different path geometries inside the buildings. The results show that the proposed localization approach can potentially be used for indoor localization with heterogeneous devices. The errors for path 1 and path 2 are very similar, however, localization error for path 3 is comparatively higher because of the complexity of the path 3. The mean and median errors for Galaxy S8 are 1.37 and 0.88 m while for LG G6, these are 1.84 and 1.21 m, respectively, while considering all buildings and all paths followed during the experiment. Overall, the proposed approach can potentially localize a pedestrian within 1.21 m at 50% and within 1.93 m at 75%, irrespective of the device used for localization. The performance of the proposed approach is compared with the K nearest neighbor (KNN) for evaluation. The proposed approach outperforms the KNN