Localization and Indoor Navigation for Visually Impaired Using Bluetooth Low Energy

Wireless personal networks such as infrared, Bluetooth, or wireless local area networks such as Wi-Fi or a combination of both are widely used for navigation. Real-world deployment of these systems offers various challenges. In this work, we propose a localization and indoor navigation solution using short range, low-energy Bluetooth-emitting devices, called “Beacons”, which are used for identifying the location of different structures based on its position. An experimental analysis for effective placement of beacons, the data structures used, and the algorithm that would assist in navigation are discussed in detail. The algorithm is realized as a self-sufficient navigation solution for visually impaired using an Android application.

[1]  Kazuhiro Kondo,et al.  Indoor positioning method for augmented audio reality navigation systems using iBeacons , 2015, 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE).

[2]  Chieko Asakawa,et al.  Environmental Factors in Indoor Navigation Based on Real-World Trajectories of Blind Users , 2018, CHI.

[3]  Demetrios G. Sampson,et al.  Blind MuseumTourer: A System for Self-Guided Tours in Museums and Blind Indoor Navigation , 2018 .

[4]  Vidhya Balasubramanian,et al.  A Robust Approach for Maintenance and Refactoring of Indoor Radio Maps , 2014, ADHOC-NOW.

[5]  Chin-Lung Hsu,et al.  An empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives , 2016, Comput. Hum. Behav..

[6]  Maxim Shchekotov,et al.  Indoor localization methods based on Wi-Fi lateration and signal strength data collection , 2015, 2015 17th Conference of Open Innovations Association (FRUCT).

[7]  Carles Gomez,et al.  Overview and Evaluation of Bluetooth Low Energy: An Emerging Low-Power Wireless Technology , 2012, Sensors.

[8]  Antti Ylä-Jääski,et al.  ViNav: A Vision-Based Indoor Navigation System for Smartphones , 2019, IEEE Transactions on Mobile Computing.

[9]  Sandip Das,et al.  Efficient algorithm for placing a given number of base stations to cover a convex region , 2006, J. Parallel Distributed Comput..

[10]  R. Faragher,et al.  An Analysis of the Accuracy of Bluetooth Low Energy for Indoor Positioning Applications , 2014 .

[11]  Ignas Niemegeers,et al.  A survey of indoor positioning systems for wireless personal networks , 2009, IEEE Communications Surveys & Tutorials.

[12]  Tarun Sharma,et al.  NAVI: Navigation aid for the visually impaired , 2016, 2016 International Conference on Computing, Communication and Automation (ICCCA).

[13]  김만두,et al.  시력 손상과 시각 장애(Visual Impairment and Blindness) , 2011 .

[14]  David S. De Lorenzo,et al.  Indoor navigation using Wi-Fi fingerprinting combined with pedestrian dead reckoning , 2018, 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[15]  Matthew A. Cooper,et al.  Algorithm on Converting a 2D Scanning LiDAR to 3D for use in Autonomous Indoor Navigation , 2018, ICRA 2018.

[16]  Paul J. M. Havinga,et al.  Opportunistic beacon networks: Information dissemination via wireless network identifiers , 2016, 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[17]  Tun-Wen Pai,et al.  Indoor localization and navigation using smartphone sensory data , 2018, Ann. Oper. Res..

[18]  S. Sharad,et al.  An attack perceptive approach for reliable and secure wireless connectivity between medical devices in public environment , 2015 .

[19]  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).