InLoc: An end-to-end robust indoor localization and routing solution using mobile phones and BLE beacons

This paper discusses `InLoc', an accurate and a robust positioning and tracking system using commercial mobile devices, with an integrated feature of route finding for the user from a source to the desired destination. The system exploits easily available building floor maps in raster form, with an easy conversion to vector model, eliminating the need of specially designed vector maps, thereby making the system scalable for large indoor maps and more implementation friendly. This also enables the vector map to be used in both Particle Filter based IMU tracking, and routing. Additionally, an efficient method for independent fusion of location information from phone IMU sensors and Bluetooth Low Energy (BLE) beacons is demonstrated. The method caters both dynamic and static properties of the system state. Furthermore, the paper proposes a novel approach for estimating the distance from BLE beacons using RSSI (Received Signal Strength Indication) measurement. InLoc can be readily used for any size of building floors for applications like tracking, routing and guiding system, emergency evacuation, meeting planners etc. requiring no separate effort to rebuild the vector map from scratch. The system yields a mean tracking error of less than 0.4m in location, and yields 0.9m as an average positioning error using fusion.

[1]  Athanasios V. Vasilakos,et al.  Analysis and status quo of smartphone-based indoor localization systems , 2014, IEEE Wireless Communications.

[2]  Klaus Wehrle,et al.  FootPath: Accurate map-based indoor navigation using smartphones , 2011, 2011 International Conference on Indoor Positioning and Indoor Navigation.

[3]  Injong Rhee,et al.  Towards Mobile Phone Localization without War-Driving , 2010, 2010 Proceedings IEEE INFOCOM.

[4]  Balaraman Ravindran,et al.  Accurate mobile robot localization in indoor environments using bluetooth , 2010, 2010 IEEE International Conference on Robotics and Automation.

[5]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[6]  Avik Ghose,et al.  Exploiting IMU Sensors for IOT Enabled Health Monitoring , 2016, IoTofHealth@MobiSys.

[7]  Yu Zhou,et al.  An efficient least-squares trilateration algorithm for mobile robot localization , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Matti Siekkinen,et al.  How low energy is bluetooth low energy? Comparative measurements with ZigBee/802.15.4 , 2012, 2012 IEEE Wireless Communications and Networking Conference Workshops (WCNCW).

[9]  Moustafa Youssef,et al.  UPTIME: Ubiquitous pedestrian tracking using mobile phones , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[10]  E. B. Van der Laan,et al.  Radio Propagation Aided Indoor Localization: Indoor localization by applying Proportionate Measurement Localization (PML) using Bluetooth Low Energy tags , 2014 .

[11]  Robert Harle,et al.  Location Fingerprinting With Bluetooth Low Energy Beacons , 2015, IEEE Journal on Selected Areas in Communications.

[12]  Matei Stroila,et al.  Route Visualization in Indoor Panoramic Imagery with Open Area Maps , 2012, 2012 IEEE International Conference on Multimedia and Expo Workshops.

[13]  Xiao Liu,et al.  A Comprehensive Study of Bluetooth Fingerprinting-Based Algorithms for Localization , 2013, 2013 27th International Conference on Advanced Information Networking and Applications Workshops.

[14]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[15]  Yuwei Chen,et al.  Bayesian Fusion for Indoor Positioning Using Bluetooth Fingerprints , 2012, Wireless Personal Communications.

[16]  Youngnam Han,et al.  SmartPDR: Smartphone-Based Pedestrian Dead Reckoning for Indoor Localization , 2015, IEEE Sensors Journal.

[17]  Feng Zhao,et al.  A reliable and accurate indoor localization method using phone inertial sensors , 2012, UbiComp.

[18]  Anshul Rai,et al.  Zee: zero-effort crowdsourcing for indoor localization , 2012, Mobicom '12.

[19]  Yong Ren,et al.  Does BTLE measure up against WiFi? A comparison of indoor location performance , 2014 .

[20]  Zhenyong Wang,et al.  The Design and Development of Indoor 3D Routing System , 2014, J. Softw..

[21]  Jie Zhang,et al.  Indoor localization on mobile phone platforms using embedded inertial sensors , 2013, 2013 10th Workshop on Positioning, Navigation and Communication (WPNC).

[22]  Peilin Liu,et al.  An improved indoor localization method using smartphone inertial sensors , 2013, International Conference on Indoor Positioning and Indoor Navigation.

[23]  Hua Lu,et al.  Graph Model Based Indoor Tracking , 2009, 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware.