Gain Without Pain: Enabling Fingerprinting-based Indoor Localization using Tracking Scanners

Abstract

Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Towards achieving this goal, WiFi fingerprinting-based indoor localization systems have been proposed. However, fingerprinting involves significant effort; especially when done at high density; and needs to be repeated with any change in the deployment area. While a number of recent systems have been introduced to reduce the calibration effort, these still trade overhead with accuracy. In this paper, we present LiPhi: an accurate system for enabling fingerprinting-based indoor localization systems without the associated data collection overhead. This is achieved by leveraging the sensing capability of transportable laser range scanners (LRSs) to automatically label WiFi signal scans, which can subsequently be used to build (and maintain) localization models. As part of its design, LiPhi has modules to associate WiFi scans with the unlabeled traces obtained from as few as one LRS as well as provisions to train a robust deep learning model. Evaluation of LiPhi using Android phones in two realistic testbeds shows that it can match the performance of manual fingerprinting techniques under the same deployment conditions without the overhead associated with the traditional fingerprinting process. In addition, LiPhi improves upon the median localization accuracy obtained from crowdsourcing-based and fingerprinting-based systems by 181% and 297% respectively, when tested with data collected a few months later.