GraphIPS: Calibration-Free and Map-Free Indoor Positioning Using Smartphone Crowdsourced Data

Abstract

Indoor positioning plays an important role in a variety of applications under Internet of Things (IoT). Conventional WiFi fingerprinting-based indoor positioning systems (IPSs) usually require extensive manual calibrations to construct radio maps. This process severely limits the system scalability and adaptiveness. Pedestrian dead reckoning (PDR) is a popular method that can avoid the calibration process. However, PDR-based IPSs typically suffer from accumulated errors. To tackle this problem, many refinement methods require map information or floorplans which may not be available or up-to-date in practice. With the development of IoT, various types of crowdsourced data become available. In this work, we propose GraphIPS, a calibration-free and map-free IPS which dynamically generates accurate radio maps by utilizing smartphone crowdsourced WiFi and inertial measurement unit (IMU) data. GraphIPS fuses the crowdsourced data into a graph-based formulation and applies the multidimensional scaling (MDS) algorithm to compute the positions of the user's steps. The experimental results show that GraphIPS achieves comparable accuracy to the calibration-based method in a significantly shorter run time than optimization-based methods. In addition to smartphones, GraphIPS is also potentially applicable for the smart wearables with embedded WiFi modules and IMUs.