Self-Improving Indoor Localization By Profiling Outdoor Movement On Smartphones

Smartphones are equipped with many low-cost sensors. As a result, opportunities open for smartphones to serve as a platform for many challenging ubiquitous applications, including indoor localization. By employing accelerometers on smartphones, dead reckoning is an intuitive and common approach to generate a user's indoor motion trace. Nevertheless, dead reckoning often deviates from the ground truth due to noise in the sensing data. We propose iLoom, an indoor localization approach that benefits by transferring learning from tracking outdoor motions to the indoor environment. Via sensing data on a smartphone, iLoom constructs two datasets: relatively accurate outdoor motions from GPS and less accurate indoor motions from accelerometers. Then, iLoom leverages an Acceleration Range Box to improve a user's acceleration value used for computing dead reckoning. After using a transfer learning algorithm to the two datasets, iLoom boosts the Acceleration Range Box to achieve better indoor localization results. In addition, iLoom exploits indoor GPS exception cases and pedometer to further improve dead reckoning. Through case studies on 15 volunteers for the indoor and outdoor scenarios, we show iLoom is a non infrastructure and low -training complexity indoor positioning approach that achieved a localization accuracy of 0.28-0.51m in multiple scenarios.