An Automated Indoor Localization System for Online Bluetooth Signal Strength Modeling Using Visual-Inertial SLAM

Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost Bluetooth localization with the least data acquisition and network configuration overhead. The proposed system incorporates a sophisticated visual-inertial localization algorithm for a fully automated collection of Bluetooth signal strength data. A suitable collection of measurements can be quickly and easily performed, clearly defining which part of the space is not yet well covered by measurements. The obtained measurements, which can also be collected via the crowdsourcing approach, are used within a constrained nonlinear optimization algorithm. The latter is implemented on a smartphone and allows the online determination of the beacons’ locations and the construction of path loss models, which are validated in real-time using the particle swarm localization algorithm. The proposed system represents an advanced innovation as the application user can quickly find out when there are enough data collected for the expected radiolocation accuracy. In this way, radiolocation becomes much less time-consuming and labor-intensive as the configuration time is reduced by more than half. The experiment results show that the proposed system achieves a good trade-off in terms of network setup complexity and localization accuracy. The developed system for automated data acquisition and online modeling on a smartphone has proved to be very useful, as it can significantly simplify and speed up the installation of the Bluetooth network, especially in wide-area facilities.

[1]  T. Steihaug The Conjugate Gradient Method and Trust Regions in Large Scale Optimization , 1983 .

[2]  James R. Bergen,et al.  Visual odometry , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[3]  Igor Skrjanc,et al.  Fusion of visual odometry and inertial navigation system on a smartphone , 2015, Comput. Ind..

[4]  Naser El-Sheimy,et al.  Autonomous smartphone-based WiFi positioning system by using access points localization and crowdsourcing , 2015, Pervasive Mob. Comput..

[5]  Reza Safabakhsh,et al.  A novel stability-based adaptive inertia weight for particle swarm optimization , 2016, Appl. Soft Comput..

[6]  Nobuyoshi Komuro,et al.  Indoor Positioning Method based on BLE Location Fingerprint with Statistics Approach , 2019, 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE).

[7]  Eslam Essa,et al.  Improve Performance of Indoor Positioning System using BLE , 2019, 2019 14th International Conference on Computer Engineering and Systems (ICCES).

[8]  Kyle O’Keefe,et al.  Trilateration With BLE RSSI Accounting for Pathloss Due to Human Obstacles , 2019, 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[9]  Dieter Brückmann,et al.  Efficient calibration for robust indoor localization based on low-cost BLE sensors , 2019, 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS).

[10]  R. Schafer,et al.  What Is a Savitzky-Golay Filter? , 2022 .

[11]  Gabriel de Blasio,et al.  Beacon-Related Parameters of Bluetooth Low Energy: Development of a Semi-Automatic System to Study Their Impact on Indoor Positioning Systems , 2019, Sensors.

[12]  Meng-Hiot Lim,et al.  A survey of problems and approaches in wireless-based indoor positioning , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[13]  Kumbesan Sandrasegaran,et al.  Smartphone-Based Indoor Positioning Using BLE iBeacon and Reliable Lightweight Fingerprint Map , 2020, IEEE Sensors Journal.

[14]  Thomas F. Coleman,et al.  A Preconditioned Conjugate Gradient Approach to Linear Equality Constrained Minimization , 2001, Comput. Optim. Appl..

[15]  Xiaoji Niu,et al.  Fast Signals of Opportunity Fingerprint Database Maintenance with Autonomous Unmanned Ground Vehicle for Indoor Positioning , 2018, Sensors.

[16]  Ruizhi Chen,et al.  A Low-Cost Single-Anchor Solution for Indoor Positioning Using BLE and Inertial Sensor Data , 2019, IEEE Access.

[17]  Ronald W. Schafer,et al.  What Is a Savitzky-Golay Filter? [Lecture Notes] , 2011, IEEE Signal Processing Magazine.

[18]  Jorge J. Moré,et al.  Computing a Trust Region Step , 1983 .

[19]  Hsi-Pin Ma,et al.  A Low Complexity Low Power Indoor Positioning System Based on Wireless Received Signal Strength , 2018, 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom).

[20]  Rytis Maskeliunas,et al.  Fuzzy Logic Type-2 Based Wireless Indoor Localization System for Navigation of Visually Impaired People in Buildings , 2019, Sensors.

[21]  BranchMary Ann,et al.  A Subspace, Interior, and Conjugate Gradient Method for Large-Scale Bound-Constrained Minimization Problems , 1999 .

[22]  Kyle O'Keefe,et al.  Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence , 2020, Sensors.

[23]  B. Silva,et al.  On Monocular Visual Odometry for Indoor Ground Vehicles , 2012, 2012 Brazilian Robotics Symposium and Latin American Robotics Symposium.

[24]  Domenico Formica,et al.  Performance Evaluation of Bluetooth Low Energy: A Systematic Review , 2017, Sensors.

[25]  Mohamed H. Abd El Azeem,et al.  An Enhanced Indoor Positioning Technique Based on a Novel Received Signal Strength Indicator Distance Prediction and Correction Model , 2021, Sensors.

[26]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[27]  Hande Alemdar,et al.  Obstruction-Aware Signal-Loss-Tolerant Indoor Positioning Using Bluetooth Low Energy , 2021, Sensors.

[28]  Maria Anca Balutoiu,et al.  Evaluation of the ARCore Indoor Localization Technology , 2020, 2020 19th RoEduNet Conference: Networking in Education and Research (RoEduNet).

[29]  Wang Qu,et al.  WiMag: Multimode Fusion Localization System based on Magnetic/WiFi/PDR , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[30]  Bogdan Cramariuc,et al.  Automatic Data Acquisition with Robots for Indoor Fingerprinting , 2018, 2018 International Conference on Communications (COMM).

[31]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[32]  Daniel Cremers,et al.  LSD-SLAM: Large-Scale Direct Monocular SLAM , 2014, ECCV.

[33]  Pichaya Supanakoon,et al.  Accuracy Study of Indoor Positioning with Bluetooth Low Energy Beacons , 2020, 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON).

[34]  Sourabh R Misal,et al.  Indoor Positioning System (IPS) Using ESP32, MQTT and Bluetooth , 2020, 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC).

[35]  Robert Piché,et al.  A Survey of Selected Indoor Positioning Methods for Smartphones , 2017, IEEE Communications Surveys & Tutorials.

[36]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.

[37]  Naohiko Kohtake,et al.  Rapid BLE Beacon Localization with Range-Only EKF-SLAM Using Beacon Interval Constraint , 2019, 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[38]  Juan D. Tardós,et al.  ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras , 2016, IEEE Transactions on Robotics.

[39]  Minyi Guo,et al.  Real-Time Locating Systems Using Active RFID for Internet of Things , 2016, IEEE Systems Journal.

[40]  Piotr Korbel,et al.  Influence of User Mobility on the Accuracy of Indoor Positioning with the use of RSSI and Particle Filter Algorithm , 2019, 2019 Signal Processing Symposium (SPSympo).

[41]  Andreas Möller,et al.  Fast relocalization for visual odometry using binary features , 2013, 2013 IEEE International Conference on Image Processing.

[42]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[43]  Hussein M. ElAttar,et al.  An Enhanced Indoor Positioning Technique Based on a Novel Received Signal Strength Indicator Distance Prediction and Correction Model , 2021, Sensors.

[44]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[45]  Kazuhiro Kondo,et al.  Estimation of indoor position and motion direction for smartphones using DNN to BLE beacon signal strength , 2020, 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan).

[46]  A. Aydin Alatan,et al.  Loosely coupled Kalman filtering for fusion of Visual Odometry and inertial navigation , 2013, Proceedings of the 16th International Conference on Information Fusion.

[47]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[48]  Igor Skrjanc,et al.  Indoor RSSI-based Localization using Fuzzy Path Loss Models , 2018, 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[49]  Shin-Dug Kim,et al.  Adaptive Monocular Visual–Inertial SLAM for Real-Time Augmented Reality Applications in Mobile Devices , 2017, Sensors.

[50]  Daniel Cremers,et al.  Direct Sparse Odometry , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[51]  Claudia Linnhoff-Popien,et al.  Visual odometry using motion vectors from visual feature points , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[52]  Paula Fikkert,et al.  Specification of the Bluetooth System , 2003 .

[53]  Vahid Dehghanian,et al.  RSS-INS integration for cooperative indoor positioning , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[54]  Moe Z. Win,et al.  Ranging With Ultrawide Bandwidth Signals in Multipath Environments , 2009, Proceedings of the IEEE.

[55]  Robert Harle,et al.  Easing the survey burden: Quantitative assessment of low-cost signal surveys for indoor positioning , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[56]  Andrew W. Fitzgibbon,et al.  Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.

[57]  Raul Montoliu,et al.  Indoor Positioning for Monitoring Older Adults at Home: Wi-Fi and BLE Technologies in Real Scenarios , 2020, Electronics.

[58]  Dejan Dovzan,et al.  Confidence-Interval-Fuzzy-Model-Based Indoor Localization , 2019, IEEE Transactions on Industrial Electronics.

[59]  Shih-Hau Fang,et al.  An Enhanced ZigBee Indoor Positioning System With an Ensemble Approach , 2012, IEEE Communications Letters.

[60]  Christian Zinner,et al.  Accurate 3D-vision-based obstacle detection for an autonomous train , 2013, Comput. Ind..

[61]  Simo Ali-Löytty,et al.  A comparative survey of WLAN location fingerprinting methods , 2009, 2009 6th Workshop on Positioning, Navigation and Communication.

[62]  Kevin Eckenhoff,et al.  MIMC-VINS: A Versatile and Resilient Multi-IMU Multi-Camera Visual-Inertial Navigation System , 2020, IEEE Transactions on Robotics.

[63]  Jae-Young Pyun,et al.  Trusted K Nearest Bayesian Estimation for Indoor Positioning System , 2019, IEEE Access.

[64]  Thomas F. Coleman,et al.  A Subspace, Interior, and Conjugate Gradient Method for Large-Scale Bound-Constrained Minimization Problems , 1999, SIAM J. Sci. Comput..

[65]  Thomas F. Coleman,et al.  An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds , 1993, SIAM J. Optim..

[66]  Andrew Sammut,et al.  Pedestrian tracking through inertial measurements , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[67]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[68]  José Neves,et al.  Interactive Guiding and Localization Platform , 2014 .

[69]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

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

[71]  Liang Liu,et al.  Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization , 2018, Sensors.

[72]  Jae-Young Pyun,et al.  A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies , 2020, Sensors.