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

Abstract

Summary A 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

The IPIN 2019 Indoor Localisation Competition—Description and Results

“…The magnetic field data are used to calculate the current position of the user. The fingerprinting technique presented in [21] is utilised to build the fingerprint database. The floor information from the floor module helps to load the magnetic database which the user is currently at.…”

A New Dataset of People Flow in an Industrial Site with UWB and Motion Capture Systems

“…Information on users' spatial and temporal occurrences and the magnetic fingerprint with indoor location via Wi-Fi RSS are used to estimate the position of people [23]. The use of magnetic data from several smartphones to generate a magnetic pattern is used for better position detection [24] as well as a multisensor fusion, based on the magnetic field [25]. The use of deep neural networks (DN) to perform magnetic field-based indoor localization using heterogeneous devices [26] allows a more reliable system.…”

DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier

“…The reported accuracy is 2-3 m at 50% with two different smartphones. Furthermore, an approach is proposed in [40] which works with PDR and magnetic data to locate a user in the indoor environment. An approach similar to particle filter has been adopted which takes into account the PDR and magnetic position of the user and predicts the final location of the user.…”

“…Very few studies consider device heterogeneity, yet they, in turn, use longer data samples. For example, the authors in [39] consider 14 s data while the authors in [40] employ 8 s data to calculate the final location of the user. Additionally, the use of a smartphone camera consumes the battery very fast and is not an efficient solution.…”