Skip to main content
SpringerLink
Log in

A Survey of Machine Learning Techniques for Indoor Localization and Navigation Systems

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

In the recent past, we have witnessed the adoption of different machine learning techniques for indoor positioning applications using WiFi, Bluetooth and other technologies. The techniques range from heuristically derived hand-crafted feature-based traditional machine learning algorithms, feature selection algorithms to the hierarchically self-evolving feature-based Deep Learning algorithms. The transient and chaotic nature of the WiFi/Bluetooth fingerprint data along with different signal sensitivity of different device configurations presents numerous challenges that influence the performance of the indoor localization system in the wild. This article is intended to offer a comprehensive state-of-the-art survey on machine learning techniques that have recently been adopted for localization purposes. Hence, we review the applicability of machine learning techniques in this domain along with basic localization principles, applications, and the underlying problems and challenges associated with the existing systems. We also articulate the recent advances and state-of-the-art machine learning techniques to visualize the possible future directions in the research field of indoor localization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Abbas, M., Elhamshary, M., Rizk, H., Torki, M., Youssef, M.: WiDeep: WiFi-based accurate and robust indoor localization system using deep learning. In: 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom). https://doi.org/10.1109/PERCOM.2019.8767421, pp. 1–10 (2019)

  2. Abdelnasser, H., Mohamed, R., Elgohary, A., Alzantot, M.F., Wang, H., Sen, S., Choudhury, R.R., Youssef, M.: SemanticSLAM: Using environment landmarks for unsupervised indoor localization. IEEE Trans. Mob. Comput. 15(7), 1770–1782 (2016). https://doi.org/10.1109/TMC.2015.2478451

    Article  Google Scholar 

  3. Aguilar, W.G., Rodríguez, G.A., Álvarez, L., Sandoval, S., Quisaguano, F., Limaico, A.: On-board visual SLAM on a UGV using a RGB-D camera. In: International Conference on Intelligent Robotics and Applications, pp. 298–308. Springer (2017). https://doi.org/10.1007/978-3-319-65298-6_28

  4. Akram, B.A., Akbar, A.H., Shafiq, O.: Hybloc: Hybrid indoor Wi-Fi localization using soft clustering-based random decision forest ensembles. IEEE Access 6, 38251–38272 (2018). https://doi.org/10.1109/ACCESS.2018.2852658

    Article  Google Scholar 

  5. Akré, J.M., Zhang, X., Baey, S., Kervella, B., Fladenmuller, A., Zancanaro, M.A., Fonseca, M.: Accurate 2-D localization of RFID tags using antenna transmission power control. In: Wireless Days (WD), vol. 2014, pp. 1–6. IEEE, IFIP (2014). https://doi.org/10.1109/WD.2014.7020802

  6. Albuquerque, D.F., Gonçalves, E.S., Pedrosa, E.F., Teixeira, F.C., Vieira, J.N.: Robot self position based on asynchronous millimetre wave radar interference. In: 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN). https://doi.org/10.1109/IPIN.2019.8911809, pp. 1–6 (2019)

  7. Alhammadi, A., Alraih, S., Hashim, F., Rasid, M.F.A.: Robust 3D indoor positioning system based on radio map using Bayesian network. In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), pp. 107–110 (2019). https://doi.org/10.1109/WF-IoT.2019.8767318

  8. Araneda, A., Soto, A.. In: Ibero-American Conference on Artificial Intelligence. https://doi.org/10.1007/978-3-540-30498-2_54, pp. 545–554. Springer (2004)

  9. Azizyan, M., Constandache, I., Roy Choudhury, R.: SurroundSense: Mobile phone localization via ambience fingerprinting. In: Proceedings of the 15th Annual International Conference on Mobile Computing and Networking, Association for Computing Machinery, pp. 261–272. MobiCom ’09, New York (2009). https://doi.org/10.1145/1614320.1614350

  10. Başak, A.A., Sazli, M.H.: Accurate indoor localization with optimized fingerprinting algorithm. In: 2017 5th International Istanbul Smart Grid and Cities Congress and Fair (ICSG), pp. 149–152 (2017). https://doi.org/10.1109/SGCF.2017.7947621

  11. Bahl, P., Padmanabhan, V.N.: RADAR: An In-building RF-based user location and tracking system. In: Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064), vol. 2, pp. 775–784 (2000). https://doi.org/10.1109/INFCOM.2000.832252

  12. Benini, A., Mancini, A., Longhi, S.: An IMU/UWB/vision-based Extended Kalman Filter for mini-UAV Localization in Indoor Environment using 802.15.4a Wireless Sensor Network. J. Intell. Robot. Syst. 70(1-4), 461–476 (2013). https://doi.org/10.1007/s10846-012-9742-1

    Article  Google Scholar 

  13. Breiman, L.: Bagging predictors. Machine Learn. 24(2), 123–140 (1996). https://doi.org/10.1007/BF00058655

    Article  MATH  Google Scholar 

  14. Calderoni, L., Ferrara, M., Franco, A., Maio, D.: Indoor Localization in a Hospital Environment using Random Forest Classifiers. Expert Syst. Appl. 42 (1), 125–134 (2015). https://doi.org/10.1016/j.eswa.2014.07.042

    Article  Google Scholar 

  15. Chang, C., Wang, S., Wang, C.: Exploiting moving objects: Multi-Robot simultaneous localization and tracking. IEEE Trans. Autom. Sci. Eng. 13(2), 810–827 (2016). https://doi.org/10.1109/TASE.2015.2426203

    Article  Google Scholar 

  16. Chen, J., Zhang, Y., Xue, W.: Unsupervised Indoor localization based on smartphone sensors, iBeacon and Wi-Fi. Sensors 18(5), 1378 (2018). https://doi.org/10.3390/s18051378

    Article  Google Scholar 

  17. Chen, Z., Wang, J.: GROF: Indoor localization using a multiple-bandwidth general regression neural network and outlier filter. Sensors 18(11), 3723 (2018). https://doi.org/10.3390/s18113723

    Article  Google Scholar 

  18. Chen, Z., Zou, H., Jiang, H., Zhu, Q., Soh, Y.C., Xie, L.: Fusion of WiFi, smartphone sensors and landmarks using the Kalman filter for indoor localization. Sensors 15(1), 715–732 (2015). https://doi.org/10.3390/s150100715

    Article  Google Scholar 

  19. Chriki, A., Touati, H., Snoussi, H.: SVM-based indoor localization in wireless sensor networks. In: 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1144–1149 (2017). https://doi.org/10.1109/IWCMC.2017.7986446

  20. Cooper, M., Biehl, J., Filby, G., Kratz, S.: Loco: Boosting for Indoor Location Classification Combining Wi-Fi and BLE. Pers. Ubiquit. Comput. 20(1), 83–96 (2016). https://doi.org/10.1007/s00779-015-0899-z

    Article  Google Scholar 

  21. Cui, W., Liu, Q., Zhang, L., Wang, H., Lu, X., Li, J.: A robust mobile robot indoor positioning system based on Wi-Fi. Int. J. Adv. Robot. Syst. 17(1), 1729881419896660 (2020). https://doi.org/10.1177/1729881419896660

    Article  Google Scholar 

  22. Diaz, E.M., Ahmed, D.B., Kaiser, S.: A review of indoor localization methods based on inertial sensors. In: Geographical and Fingerprinting Data to Create Systems for Indoor Positioning and Indoor/Outdoor Navigation, pp. 311–333. Elsevier, Amsterdam (2019). https://doi.org/10.1016/B978-0-12-813189-3.00016-2

  23. Dissanayake, M.W.M.G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans. Robot. Autom. 17(3), 229–241 (2001). https://doi.org/10.1109/70.938381

    Article  Google Scholar 

  24. Elfes, A.: Occupancy grids: A probabilistic framework for robot perception and navigation (1991)

  25. Emanuel, D.C., Mahan, L.G., Ungerbuehler, R.H.: Apparatus and method for asset tracking. US Patent 8,565,913 (2013)

  26. Fang, S., Lin, T.: Principal component localization in indoor WLAN environments. IEEE Trans. Mob. Comput. 11(1), 100–110 (2012). https://doi.org/10.1109/TMC.2011.30

    Article  Google Scholar 

  27. Faragher, R., Harle, R.: SmartSLAM-An efficient smartphone indoor positioning system exploiting machine learning and opportunistic sensing. In: ION GNSS, vol. 13, pp. 1–14 (2013)

  28. Farid, Z., Nordin, R., Ismail, M., Abdullah, N.F.: Hybrid Indoor-based WLAN-WSN localization scheme for improving accuracy based on artificial neural network. Mob. Inf. Syst. 2016, 1–11 (2016). https://doi.org/10.1155/2016/6923931

    Google Scholar 

  29. Feng, Y., Minghua, J., Jing, L., Xiao, Q., Ming, H., Tao, P., Xinrong, H.: An improved indoor localization of WiFi based on support vector machines. Int. J. Future Generat. Commun. Netw 7(5), 191–206 (2014)

    Article  Google Scholar 

  30. Feng, Y., Minghua, J., Jing, L., Xiao, Q., Ming, H., Tao, P., Xinrong, H.: Improved AdaBoost-based Fingerprint Algorithm for WiFi Indoor Localization. In: 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference. https://doi.org/10.1109/ITAIC.2014.7064997, pp. 16–19 (2014)

  31. Fischer, C., Gellersen, H.: Location and navigation support for emergency responders: a survey. IEEE Pervas. Comput. 9(1), 38–47 (2010). https://doi.org/10.1109/MPRV.2009.91

    Article  Google Scholar 

  32. Gala, D., Lindsay, N., Sun, L.: Multi-sound-source localization for small autonomous unmanned vehicles with a self-rotating Bi-Microphone Array. arXiv:180405111 (2018a)

  33. Gala, D., Lindsay, N., Sun, L.: Three-dimensional sound source localization for unmanned ground vehicles with a self-rotational two-microphone array. In: Proceedings of the 5th International Conference of Control, Dynamic Systems and Robotics, Niagara Falls, ON, Canada, pp. 7–9 (2018b). https://doi.org/10.11159/cdsr18.104

  34. Gala, D., Lindsay, N., Sun, L.: Realtime active sound source localization for unmanned ground robots using a Self-Rotational Bi-Microphone array. J. Intell. Robot. Syst. 95(3-4), 935–954 (2019). https://doi.org/10.1007/s10846-018-0908-3

    Article  Google Scholar 

  35. Gao, X., Zhang, T.: Unsupervised learning to detect loops using deep neural networks for visual SLAM system. Autonomous Robots 41(1), 1–18 (2017). https://doi.org/10.1007/s10514-015-9516-2

    Article  MathSciNet  Google Scholar 

  36. Ghosh, D., Roy, P., Chowdhury, C., Bandyopadhyay, S.: An Ensemble of Condition based Classifiers for Indoor Localization. In: 2016 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). https://doi.org/10.1109/ANTS.2016.7947872, pp. 1–6 (2016)

  37. Harik, E.H.C., Guérin, F., Guinand, F., Brethé, J., Pelvillain, H.: Towards an autonomous warehouse inventory scheme. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8 (2016). https://doi.org/10.1109/SSCI.2016.7850056

  38. Harle, R.K., Hopper, A.: Deploying and evaluating a location-aware system. In: Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services, Association for Computing Machinery, New York, NY, USA, MobiSys ’05, pp. 219–232 (2005). https://doi.org/10.1145/1067170.1067194

  39. Haute, T.V., Poorter, E.D., Lemic, F., Handziski, V., Wirström, N., Voigt, T., Wolisz, A., Moerman, I.: Platform for Benchmarking of RF-Based indoor localization solutions. IEEE Commun. Magazine 53(9), 126–133 (2015)

    Article  Google Scholar 

  40. He, C., Guo, S., Wu, Y., Yang, Y.: A novel radio map construction method to reduce collection effort for indoor localization. Measurement 94, 423–431 (2016). https://doi.org/10.1016/j.measurement.2016.08.021

    Article  Google Scholar 

  41. Hernández, A.C., Gómez, C., Crespo, J., Barber, R.: Object detection applied to indoor environments for mobile robot navigation. Sensors 16(8), 1180 (2016). https://doi.org/10.3390/s16081180

    Article  Google Scholar 

  42. Hossain, A.M., Soh, W.S.: A survey of calibration-free indoor positioning systems. Comput. Commun. 66, 1–13 (2015). https://doi.org/10.1016/j.comcom.2015.03.001

    Article  Google Scholar 

  43. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1-3), 489–501 (2006). https://doi.org/10.1016/j.neucom.2005.12.126

    Article  Google Scholar 

  44. Huang, Z., Zhu, J., Yang, L., Xue, B., Wu, J., Zhao, Z.: Accurate 3-D position and orientation method for indoor mobile robot navigation based on photoelectric scanning. IEEE Trans. Instrum. Meas. 64(9), 2518–2529 (2015). https://doi.org/10.1109/TIM.2015.2415031

    Article  Google Scholar 

  45. Izidio, D.M.F., Ferreira, A.P.d.A., Barros, E.N.d.S.: Towards better generalization in WLAN positioning systems with genetic algorithms and neural networks. in: proceedings of the genetic and evolutionary computation conference,. In: Association for Computing Machinery, New York, NY, USA, GECCO ’19, pp. 1206–1213 (2019). https://doi.org/10.1145/3321707.3321712

  46. Jia, B., Huang, B., Gao, H., Li, W.: Dimension Rreduction in Radio maps based on the Supervised Kernel Principal Component Analysis. Soft. Comput. 22(23), 7697–7703 (2018). https://doi.org/10.1007/s00500-018-3228-4

    Article  Google Scholar 

  47. Jiang, X., Liu, J., Chen, Y., Liu, D., Gu, Y., Chen, Z.: Feature adaptive online sequential extreme learning machine for lifelong indoor localization. Neural Comput. Applic. 27(1), 215–225 (2014). https://doi.org/10.1007/s00521-014-1714-x

    Article  Google Scholar 

  48. Jung, S., Hwang, S., Shin, H., Shim, D.H.: Perception, guidance, and navigation for indoor autonomous drone racing using deep learning. IEEE Robot. Autom. Lett. 3(3), 2539–2544 (2018). https://doi.org/10.1109/LRA.2018.2808368

    Article  Google Scholar 

  49. Kang, W., Han, Y.: SmartPDR: Smartphone-Based pedestrian dead reckoning for indoor localization. IEEE Sensors J. 15(5), 2906–2916 (2015). https://doi.org/10.1109/JSEN.2014.2382568

    Article  Google Scholar 

  50. Kim, K.S., Lee, S., Huang, K.: A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting. Big Data Analytics 3(1), 4 (2018). https://doi.org/10.1186/s41044-018-0031-2

    Article  Google Scholar 

  51. Kong, Z., Lu, Q.: A brief review of simultaneous localization and mapping. In: IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, pp. 5517–5522 (2017). https://doi.org/10.1109/IECON.2017.8216955

  52. Kriz, P., Maly, F., Kozel, T.: Improving indoor localization using bluetooth low energy beacons. Mob. Inf. Syst. 11 https://doi.org/10.1155/2016/2083094 (2016)

  53. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  54. Li, H., Ng, J.K., Liu, K.: Handling fingerprint sparsity for Wi-Fi based indoor localization in complex environments. In: 2019 IEEE Smartworld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 1109–1116 (2019). https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00210

  55. Li, J., Gao, X., Hu, Z., Wang, H., Cao, T., Yu, L.: Indoor localization method based on regional division with IFCM. Electronics 8(5), 559 (2019). https://doi.org/10.3390/electronics8050559

    Article  Google Scholar 

  56. Li, W., Zhang, T., Kühnlenz, K.: A Vision-guided Autonomous Quadrotor in an Air-ground Multi-robot System. In: 2011 IEEE International Conference on Robotics and Automation, pp. 2980–2985 (2011). https://doi.org/10.1109/ICRA.2011.5979579

  57. Li, X., Wang, J., Liu, C., Zhang, L., Li, Z.: Integrated wifi/PDR/smartphone using an adaptive system noise extended Kalman filter algorithm for indoor localization. ISPRS Int. J. Geo-Inform. 5 (2), 8 (2016). https://doi.org/10.3390/ijgi5020008

    Article  Google Scholar 

  58. Lin, X., Tsai, C., Tai, F.: Cooperative SLAM of an autonomous indoor Quadrotor Flying together with an autonomous ground robot. In: 2019 12th Asian Control Conference (ASCC), pp. 885–889 (2019)

  59. Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of wireless indoor positioning techniques and systems. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 37(6), 1067–1080 (2007). https://doi.org/10.1109/TSMCC.2007.905750

    Article  Google Scholar 

  60. Liu, H., Stoll, N., Junginger, S., Thurow, K.: A new approach to battery power tracking and predicting for mobile robot transportation using wavelet decomposition and ANFIS networks. In: 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO), vol. 2014, pp. 253–258 (2014). https://doi.org/10.1109/ROBIO.2014.7090339

  61. Liu, K., Motta, G., Ma, T., Guo, T.: Multi-floor indoor navigation with geomagnetic field positioning and ant colony optimization algorithm. In: 2016 IEEE Symposium on Service-Oriented System Engineering (SOSE), pp. 314–323 (2016). https://doi.org/10.1109/SOSE.2016.18

  62. Liu, K., Zhang, H., Ng, J.K.Y., Xia, Y., Feng, L., Lee, V.C.S., Son, S.H.: Toward Low-Overhead Fingerprint-Based indoor localization via transfer learning: design, implementation, and evaluation. IEEE Trans. Industrial Informatics. 14(3), 898–908 (2018). https://doi.org/10.1109/TII.2017.2750240

    Article  Google Scholar 

  63. Lo, C., Wu, K., Liu, J.: Wall following and human detection for mobile robot surveillance in indoor environment. In: 2014 IEEE International Conference on Mechatronics and Automation, pp. 1696–1702 (2014). https://doi.org/10.1109/ICMA.2014.6885956

  64. Löffler, C., Riechel, S., Fischer, J., Mutschler, C.: Evaluation criteria for inside-out indoor positioning systems based on machine learning. In: 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8. IEEE (2018). https://doi.org/10.1109/IPIN.2018.8533862

  65. Lohan, E.S., Torres-Sospedra, J., Leppakoski, H., Richter, P., Peng, Z., Huerta, J.: Wi-Fi Crowdsourced fingerprinting dataset for indoor positioning. Data 2(4) (2017)

  66. Lu, G., Yan, Y., Ren, L., Saponaro, P., Sebe, N., Kambhamettu, C.: Where am I in the dark: Exploring active transfer learning on the use of indoor localization based on thermal imaging. Neurocomputing 173, 83–92 (2016). https://doi.org/10.1016/j.neucom.2015.07.106

    Article  Google Scholar 

  67. Ma, H., Wang, K.: Fusion of RSS and Phase Shift using the Kalman Filter for RFID Tracking. IEEE Sensors J. 17(11), 3551–3558 (2017). https://doi.org/10.1109/JSEN.2017.2696054

    Article  Google Scholar 

  68. Ma, H., Wang, Y., Wang, K.: Automatic detection of false positive RFID readings using machine learning algorithms. Expert Syst. Appl. 91, 442–451 (2018). https://doi.org/10.1016/j.eswa.2017.09.021

    Article  Google Scholar 

  69. Madigan, D., Einahrawy, E., Martin, R.P., Ju, W.H., Krishnan, P., Krishnakumar, A.: Bayesian indoor positioning systems. In: INFOCOM 2005. 24th Annual Joint Conf of the IEEE Computer and Communications Societies. Proc. IEEE, vol. 2, pp. 1217–1227 (2005). https://doi.org/10.1109/INFCOM.2005.1498348

  70. Mascharka, D., Manley, E.: Machine learning for indoor localization using mobile phone-based sensors. arXiv:150506125 (2015)

  71. McCarthy, M.R.: The BUZZ: Narrowband ultrasonic positioning for wearable computers. PhD thesis, Citeseer (2007). https://doi.org/10.1.1.153.671

  72. Menéndez, P., Campomanes, C., Trawiński, K., Alonso, J.M.: Topology-based indoor localization by means of WiFi fingerprinting with a computational intelligent classifier. In: Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on, pp. 1020–1025. IEEE (2011). https://doi.org/10.1109/ISDA.2011.6121792

  73. Meng, H., Yuan, F., Yan, T., Zeng, M.: Indoor positioning of RBF neural network based on improved fast clustering algorithm combined with LM algorithm. IEEE Access 7, 5932–5945 (2019). https://doi.org/10.1109/ACCESS.2018.2888616

    Article  Google Scholar 

  74. Minami, M., Fukuju, Y., Hirasawa, K., Yokoyama, S., Mizumachi, M., Morikawa, H., Aoyama, T.: Dolphin: A practical approach for implementing a fully distributed indoor ultrasonic positioning system. In: UbiComp 2004: Ubiquitous Computing, pp. 347–365. Springer, Berlin (2004). https://doi.org/10.1007/978-3-540-30119-6_21

  75. Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: A factored solution to the simultaneous localization and mapping problem. Aaai/iaai 593–598 (2002)

  76. Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B., et al.: FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In: IJCAI, pp. 1151–1156 (2003)

  77. Montoliu, R., Sansano, E., Torres-Sospedra, J., Belmonte, O.: IndoorLoc Platform: A public repository for comparing and evaluating indoor positioning systems. In: 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8 (2017). https://doi.org/10.1109/IPIN.2017.8115940

  78. Moravec, H., Elfes, A.: High resolution maps from wide angle sonar. In: Proceedings. 1985 IEEE International Conference on Robotics and Automation, vol. 2, pp. 116–121 (1985). https://doi.org/10.1109/ROBOT.1985.1087316

  79. Nastac, D., Lehan, E., Iftimie, F.A., Arsene, O., Cramariuc, B.: Automatic data acquisition with robots for indoor fingerprinting. In: 2018 International Conference on Communications (COMM). https://doi.org/10.1109/ICComm.2018.8484796, pp. 321–326 (2018)

  80. Ni, L.M., Yunhao, L., Yiu Cho, L., Patil, A.P.: LANDMARC: Indoor Location Sensing using Active RFID. In: Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003), pp. 407–415 (2003). https://doi.org/10.1109/PERCOM.2003.1192765

  81. Nikdel, P., Chen, M., Vaughan, R.: Recognizing and Tracking High-Level, Human-Meaningful Navigation Features of Occupancy Grid Maps. In: 2020 17Th Conference on Computer and Robot Vision (CRV), pp. 62–69. IEEE (2020). https://doi.org/10.1109/CRV50864.2020.00017

  82. Nurminen, H., Ristimäki, A., Ali-Löytty, S., Piché, R.: Particle filter and smoother for indoor localization. In: International Conference on Indoor Positioning and Indoor Navigation, pp. 1–10 (2013). https://doi.org/10.1109/IPIN.2013.6817903

  83. Ouyang, R.W., Wong, A.K., Lea, C., Chiang, M.: Indoor location estimation with reduced calibration exploiting unlabeled data via hybrid Generative/Discriminative learning. IEEE Trans. Mob. Comput. 11(11), 1613–1626 (2012). https://doi.org/10.1109/TMC.2011.193

    Article  Google Scholar 

  84. Padhy, R.P., Verma, S., Ahmad, S., Choudhury, S.K., Sa, P.K.: Deep neural network for autonomous UAV navigation in indoor corridor environments. Procedia computer science 133, 643–650 (2018). https://doi.org/10.1016/j.procs.2018.07.099

    Article  Google Scholar 

  85. Pan, J.J., Pan, S.J., Yin, J., Ni, L.M., Yang, Q.: Tracking mobile users in wireless networks via Semi-Supervised colocalization. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 587–600 (2012). https://doi.org/10.1109/TPAMI.2011.165

    Article  Google Scholar 

  86. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010). https://doi.org/10.1109/TKDE.2009.191

    Article  Google Scholar 

  87. Pandey, A., Pandey, S., Parhi, D.: Mobile robot navigation and obstacle avoidance techniques: A review. Int. Robot. Autom. J. 2(3), 00022 (2017)

    Google Scholar 

  88. Panov, P., Džeroski, S.: Combining bagging and random subspaces to create better ensembles. In: International Symposium on Intelligent Data Analysis, pp. 118–129. Springer (2007). https://doi.org/10.1007/978-3-540-74825-0_11

  89. Paolanti, M., Sturari, M., Mancini, A., Zingaretti, P., Frontoni, E.: Mobile robot for retail surveying and inventory using visual and textual analysis of monocular pictures based on deep learning. In: 2017 European Conference on Mobile Robots (ECMR), pp. 1–6 (2017). https://doi.org/10.1109/ECMR.2017.8098666

  90. Patel, N., Choromanska, A., Krishnamurthy, P., Khorrami, F.: A deep learning gated architecture for UGV navigation robust to sensor failures. Robot. Autonom. Syst. 116, 80–97 (2019). https://doi.org/10.1016/j.robot.2019.03.001

    Article  Google Scholar 

  91. Poggi, M., Mattoccia, S.: A wearable mobility aid for the visually impaired based on embedded 3d vision and deep learning. In: 2016 IEEE Symposium on Computers and Communication (ISCC), pp. 208–213. IEEE (2016). https://doi.org/10.1109/ISCC.2016.7543741

  92. Priyantha, N.B., Chakraborty, A., Balakrishnan, H.: The cricket location-support system. In: Proceedings of the 6th Annual International Conference on Mobile Computing and Networking, Association for Computing Machinery, New York, NY, USA, MobiCom ’00, pp. 32–43 (2000). https://doi.org/10.1145/345910.345917

  93. Pulkkinen, T., Roos, T., Myllymäki, P.: Semi-supervised Learning for WLAN Positioning. In: Intl Conf on Art. Neural Net, pp. 355–362 (2011). https://doi.org/10.1007/978-3-642-21735-7_44

  94. Rahbar, F., Marjovi, A., Kibleur, P., Martinoli, A.: A 3-D Bio-inspired odor source localization and its validation in realistic environmental conditions. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3983–3989 (2017). https://doi.org/10.1109/IROS.2017.8206252

  95. Rai, A., Chintalapudi, K.K., Padmanabhan, V.N., Sen, R.: Zee: Zero-Effort Crowdsourcing for Indoor Localization. In: Proc. of the 18th annual Intl Conf on Mobile computing and networking, pp. 293–304. ACM (2012). https://doi.org/10.1145/2348543.2348580

  96. Ramadan, M., Sark, V., Gutierrez, J., Grass, E.: NLOS Identification for Indoor Localization using Random Forest Algorithm. In: WSA 2018; 22nd International ITG Workshop on Smart Antennas, pp. 1–5 (2018)

  97. Rezgui, Y., Pei, L., Chen, X., Wen, F., Han, C.: An efficient normalized rank based SVM for room level indoor WiFi localization with diverse devices. Mob. Inf. Syst. 2017 https://doi.org/10.1155/2017/6268797 (2017)

  98. Roy, P., Chowdhury, C.: Indoor localization for Smart-handhelds with stable set of wireless access points. In: 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT), pp. 1–4 (2018). https://doi.org/10.1109/EAIT.2018.8470401

  99. Roy, P., Chowdhury, C.: Smartphone based indoor localization using stable access points. In: Proc. of the Workshop Program of the 19th Intl Conf on Distributed Computing and Networking, ACM, Workshops ICDCN ’18, pp. 17:1–17:6 (2018). https://doi.org/10.1145/3170521.3170538

  100. Roy, P., Chowdhury, C., Ghosh, D., Bandyopadhyay, S.: JUIndoorLoc: A Ubiquitous Framework for Smartphone-Based Indoor Localization Subject to Context and Device Heterogeneity. Wireless Personal Communications https://doi.org/10.1007/s11277-019-06188-2 (2019)

  101. Roy, P., Kundu, M., Chowdhury, C.: Indoor localization using stable set of wireless access points subject to varying granularity levels. In: 2019 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), pp. 491–496 (2019). https://doi.org/10.1109/WiSPNET45539.2019.9032859

  102. Roy, P., Chowdhury, C., Kundu, M., Ghosh, D., Bandyopadhyay, S.: Novel weighted ensemble classifier for Smartphone based indoor localization. Expert Syst. Appl. 164, 113758 (2021). https://doi.org/10.1016/j.eswa.2020.113758. http://www.sciencedirect.com/science/article/pii/S0957417420305820

    Article  Google Scholar 

  103. Salamah, A.H., Tamazin, M., Sharkas, M.A., Khedr, M.: An Enhanced WiFi Indoor Localization System Based on Machine Learning. In: 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8. IEEE (2016). https://doi.org/10.1109/IPIN.2016.7743586

  104. Salamah, A.H., Tamazin, M., Sharkas, M.A., Khedr, M., Mahmoud, M.: Comprehensive investigation on principle component Large-Scale Wi-Fi indoor localization. Sensors 19(7), 1678 (2019). https://doi.org/10.3390/s19071678

    Article  Google Scholar 

  105. Sampedro, C., Rodriguez-Ramos, A., Bavle, H., Carrio, A., de la Puente, P., Campoy, P.: A fully-autonomous aerial robot for search and rescue applications in indoor environments using learning-based techniques. J. Intell. Robotic Syst. 95(2), 601–627 (2019). https://doi.org/10.1007/s10846-018-0898-1

    Article  Google Scholar 

  106. Sarshar, H., Matwin, S.: Using Classification in the Preprocessing Step on Wi-Fi Data as an Enabler of Physical Analytics. IEEE, ICMLA. https://doi.org/10.1109/ICMLA.2016.0170 (2016)

  107. Savvides, A., Han, C.C., Strivastava, M.B.: Dynamic fine-grained localization in ad-hoc networks of sensors. In: Proceedings of the 7th annual international conference on Mobile computing and networking, pp. 166–179 (2001). https://doi.org/10.1145/381677.381693

  108. Scheper, K.Y.W., Karásek, M., De Wagter, C., Remes, B.D.W., De Croon, G.C.H.E.: First autonomous multi-room exploration with an insect-inspired flapping wing vehicle. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 5546–5552 (2018). https://doi.org/10.1109/ICRA.2018.8460702

  109. Schmuck, P., Scherer, S.A., Zell, A.: Hybrid Metric-Topological 3D Occupancy Grid Maps for Large-scale Mapping, 9th IFAC Symposium on Intelligent Autonomous Vehicles IAV 2016, vol. 49, pp. 230–235 (2016). https://doi.org/10.1016/j.ifacol.2016.07.738

  110. Shang, J., Gu, F., Hu, X., Kealy, A.: APFIloc: An Infrastructure-free Indoor Localization Method Fusing Smartphone Inertial Sensors, Landmarks and Map Information. Sensors 15(10), 27251–27272 (2015). https://doi.org/10.3390/s151027251

    Article  Google Scholar 

  111. Shenoy, M.V., Karuppiah, A., Manjarekar, N.: A Lightweight ANN based Robust Localization Technique for Rapid Deployment of Autonomous Systems. J. Ambient. Intell. Humaniz. Comput. 1–16. https://doi.org/10.1007/s12652-019-01331-0 (2019)

  112. Shnaps, I., Rimon, E.: Online coverage of planar environments by a battery powered autonomous mobile robot. IEEE Trans. Autom. Sci. Eng. 13(2), 425–436 (2016). https://doi.org/10.1109/TASE.2016.2515165

    Article  Google Scholar 

  113. Slavin, A.J., Martin, J.P., Ramos, D.J.: Fixed property monitoring with moving asset location tracking. US Patent App. 15, 606,922 (2017)

    Google Scholar 

  114. Song, Z., Du, H., Huang, H., Liu, C.: Indoor localization via candidate fingerprints and genetic algorithm. In: Combinatorial Optimization and Applications, pp. 319–333. Springer (2015). https://doi.org/10.1007/978-3-319-26626-8_24

  115. Stevens, T.D.: Wireless tracking and monitoring electronic seal. US Patent 8,456,302 (2013)

  116. Suroso, D.J., Cherntanomwong, P., Sooraksa, P., Takada, J.: Location fingerprint technique using fuzzy c-means clustering algorithm for indoor localization. In: TENCON 2011 - 2011 IEEE Region 10 Conference, pp. 88–92 (2011). https://doi.org/10.1109/TENCON.2011.6129069

  117. Suwannawach, P., Chivapreecha, S.: Reduce RSSI variance for indoor localization system using frequency analysis. Int. J. Future Comput. Commun. 8(2), 1–5 (2019). https://doi.org/10.18178/ijfcc.2019.8.2.536

    Article  Google Scholar 

  118. Tateno, K., Tombari, F., Laina, I., Navab, N.: CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth Prediction. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6565–6574 (2017). https://doi.org/10.1109/CVPR.2017.695

  119. Tejera Hernández, D.C.: An experimental study of k* algorithm. International Journal of Information Engineering & Electronic Business 7(2). https://doi.org/10.5815/ijieeb.2015.02.03 (2015)

  120. Thrun, S., Burgard, W., Fox, D.: A probabilistic approach to concurrent mapping and localization for mobile robots, vol. 5, pp. 253–271 (1998). https://doi.org/10.1023/A:1008806205438

  121. Torres-Sospedra, J., Montoliu, R., Martínez-Usó, A., Avariento, J.P., Arnau, T.J., Benedito-Bordonau, M., Huerta, J.: UJIIndoorLoc: A New multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems. In: Indoor Positioning and Indoor Navigation (IPIN), 2014 Intl Conf on, pp. 261–270. IEEE (2014). https://doi.org/10.1109/IPIN.2014.7275492

  122. Torres-Sospedra, J., Rambla, D., Montoliu, R., Belmonte, O., Huerta, J.: UJIIndoorLoc-Mag: A new database for magnetic field-based localization problems. In: 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–10 (2015). https://doi.org/10.1109/IPIN.2015.7346763

  123. Trawiński, K., Alonso, J.M., Hernández, N.: A multiclassifier approach for topology-based WiFi indoor localization. Soft. Comput. 17(10), 1817–1831 (2013). https://doi.org/10.1007/s00500-013-1019-5

    Article  Google Scholar 

  124. Trogh, J., Joseph, W., Martens, L., Plets, D.: An unsupervised learning technique to optimize radio maps for indoor localization. Sensors 19(4), 752 (2019). https://doi.org/10.3390/s19040752

    Article  Google Scholar 

  125. Turduev, M., Cabrita, G., Kırtay, M., Gazi, V., Marques, L.: Experimental studies on chemical concentration map building by a multi-robot system using bio-inspired algorithms. Autonom. Agents Multi-Agent Syst. 28(1), 72–100 (2014). https://doi.org/10.1007/s10458-012-9213-x

    Article  Google Scholar 

  126. Tzafestas, S.G.: Mobile robot control and navigation: A global overview. J. Intell. Robot. Syst. 91(1), 35–58 (2018). https://doi.org/10.1007/s10846-018-0805-9

    Article  Google Scholar 

  127. Valin, J., Michaud, F., Rouat, J., Letourneau, D.: Robust sound source localization using a microphone array on a mobile robot. In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), vol. 2, pp. 1228–1233 (2003). https://doi.org/10.1109/IROS.2003.1248813

  128. Wang, C., Meng, L., She, S., Mitchell, I.M., Li, T., Tung, F., Wan, W, Meng, M.Q., de Silva, C.W.: Autonomous mobile robot navigation in uneven and unstructured indoor environments. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 109–116 (2017). https://doi.org/10.1109/IROS.2017.8202145

  129. Wang, C., Shi, Z., Wu, F.: An improved particle swarm optimization-based feed-forward neural network combined with RFID Sensors to Indoor Localization. Information 8(1), 9 (2017a)

    Article  Google Scholar 

  130. Wang, D., Wang, T., Zhao, F., Zhang, X.: Improved graph-based semi-supervised learning for fingerprint-based indoor localization. In: 2018 IEEE Global Communications Conference (GLOBECOM), pp. 1–6 (2018). https://doi.org/10.1109/GLOCOM.2018.8647621

  131. Wang, H., Sen, S., Elgohary, A., Farid, M., Youssef, M., Choudhury, R.R.: No Need to War-drive: Unsupervised Indoor Localization. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, Association for Computing Machinery, New York, NY, USA, MobiSys ’12, pp. 197–210 (2012). https://doi.org/10.1145/2307636.2307655

  132. Wang, K., Yu, X., Xiong, Q., Zhu, Q., Lu, W., Huang, Y., Zhao, L.: Learning to improve WLAN indoor positioning accuracy based on DBSCAN-KRF algorithm from RSS fingerprint data. IEEE Access 7, 72308–72315 (2019a). https://doi.org/10.1109/ACCESS.2019.2919329

    Article  Google Scholar 

  133. Wang, W., Li, T., Wang, W., Tu, Z.: Multiple fingerprints-based indoor localization via gbdt: Subspace and rssi. IEEE Access 7, 80519–80529 (2019b). https://doi.org/10.1109/ACCESS.2019.2922995

    Article  Google Scholar 

  134. Wang, X., Gao, L., Mao, S.: CSI Phase fingerprinting for indoor localization with a deep learning approach. IEEE Int. Things J. 3(6), 1113–1123 (2016). https://doi.org/10.1109/JIOT.2016.2558659

    Article  Google Scholar 

  135. Wang, X., Gao, L., Mao, S., Pandey, S.: CSI-Based Fingerprinting for Indoor localization: A Deep Learning Approach. IEEE Trans. Veh. Technol. 66 (1), 763–776 (2017b). https://doi.org/10.1109/TVT.2016.2545523

    Google Scholar 

  136. Wang, Y., Xiu, C., Zhang, X., Yang, D.: Wifi Indoor Localization with CSI Fingerprinting-based Random Forest. Sensors 18(9), 2869 (2018). https://doi.org/10.3390/s18092869

    Article  Google Scholar 

  137. Xuyu, W., Lingjun, G., Shiwen, M.: Phasefi: Phase Fingerprinting for Indoor Localization with a Deep Learning Approach. In: 2015 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. IEEE (2015). https://doi.org/10.1109/GLOCOM.2015.7417517

  138. Ward, A., Jones, A., Hopper, A.: A new location technique for the active office. IEEE Pers. Commun. 4(5), 42–47 (1997). https://doi.org/10.1109/98.626982

    Article  Google Scholar 

  139. Wessner, J., Utschick, W.: Extending Occupancy Grid Mapping for Dynamic Environments. In: 2018 IEEE Intelligent Vehicles Symposium, vol. IV, pp. 701–707 (2018). https://doi.org/10.1109/IVS.2018.8500362

  140. Woo, S., Jeong, S., Mok, E., Xia, L., Choi, C., Pyeon, M., Heo, J.: Application of WiFi-based Indoor Positioning System for Labor Tracking at Construction sites: A Case Study in Guangzhou MTR. Autom. Constr. 20(1), 3–13 (2011). https://doi.org/10.1016/j.autcon.2010.07.009

    Article  Google Scholar 

  141. Wu, C., Yang, Z., Liu, Y., Xi, W.: WILL: Wireless indoor localization without site survey. IEEE Trans. Parallel Distribut. Syst. 24(4), 839–848 (2013). https://doi.org/10.1109/TPDS.2012.179

    Article  Google Scholar 

  142. Wu, C., Yang, Z., Liu, Y.: Smartphones based crowdsourcing for indoor localization. IEEE Trans. Mob. Comput. 14(2), 444–457 (2015). https://doi.org/10.1109/TMC.2014.2320254

    Article  Google Scholar 

  143. Wu, C., Yang, Z., Xiao, C.: Automatic radio map adaptation for indoor localization using smartphones. IEEE Trans. Mob. Comput. 17(3), 517–528 (2018). https://doi.org/10.1109/TMC.2017.2737004

    Article  Google Scholar 

  144. Xia, J., Iqbal, U., Noureldin, A., Atia, M.M., Sun, F.: Adaptive Square-root CKF based SLAM Algorithm for Indoor UGVs. In: 2017 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 1942–1946 (2017). https://doi.org/10.1109/ICMA.2017.8016115

  145. Xiao, J., Zhou, Z., Yi, Y., Ni, L.M.: A survey on wireless indoor localization from the device perspective. ACM Computing Surveys (CSUR) 49(2), 25 (2016). https://doi.org/10.1145/2933232

    Google Scholar 

  146. Xu, D., Han, L., Tan, M., Li, Y.F.: Ceiling-Based Visual positioning for an indoor mobile robot with monocular vision. IEEE Trans. Ind. Electron. 56(5), 1617–1628 (2009). https://doi.org/10.1109/TIE.2009.2012457

    Article  Google Scholar 

  147. Xu, L., Feng, C., Kamat, V.R., Menassa, C.C.: An occupancy grid mapping enhanced visual SLAM for real-time locating applications in indoor GPS-denied environments. Autom. Constr. 104, 230–245 (2019). https://doi.org/10.1016/j.autcon.2019.04.011

    Article  Google Scholar 

  148. Xu, Z., Wei, J., Zhu, J., Yang, W.: A robust floor localization method using inertial and barometer measurements. In: 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8 (2017). https://doi.org/10.1109/IPIN.2017.8115952

  149. Xue, W., Qiu, W., Hua, X., Yu, K.: Improved Wi-Fi RSSI measurement for indoor localization. IEEE Sensors J. 17(7), 2224–2230 (2017). https://doi.org/10.1109/JSEN.2017.2660522

    Article  Google Scholar 

  150. Yang, H., Zhang, R., Bordoy, J., Höflinger, F, Li, W., Schindelhauer, C., Reindl, L.: Smartphone-Based Indoor localization system using inertial sensor and acoustic Transmitter/Receiver. IEEE Sensors J. 16(22), 8051–8061 (2016). https://doi.org/10.1109/JSEN.2016.2604424

    Article  Google Scholar 

  151. Yang, J., Zhao, X., Li, Z.: Crowdsourcing indoor positioning by Light-Weight automatic fingerprint updating via ensemble learning. IEEE Access 7, 26255–26267 (2019). https://doi.org/10.1109/ACCESS.2019.2901736

    Article  Google Scholar 

  152. Yang, Z., Wu, C., Zhou, Z., Zhang, X., Wang, X., Liu, Y.: Mobility Increases localizability: A Survey on Wireless Indoor Localization using Inertial Sensors. ACM Computing Surveys (CSUR) 47 (3), 54 (2015). https://doi.org/10.1145/2676430

    Article  Google Scholar 

  153. Yang, Z., Zhang, P., Chen, L.: RFID-Enabled Indoor Positioning Method for a Real-time Manufacturing Execution System using OS-ELM. Neurocomputing 174, 121–133 (2016). https://doi.org/10.1016/j.neucom.2015.05.120

    Article  Google Scholar 

  154. Yassin, A., Nasser, Y., Awad, M., Al-Dubai, A., Liu, R., Yuen, C., Raulefs, R., Aboutanios, E.: Recent advances in indoor localization: a survey on theoretical approaches and applications. IEEE Commun. Surveys Tutor. 19(2), 1327–1346 (2017). https://doi.org/10.1109/COMST.2016.2632427

    Article  Google Scholar 

  155. Yayan, U., Yucel, H.: A low cost ultrasonic based positioning system for the indoor navigation of mobile robots. J. Intell. Robot. Syst. 78(3-4), 541–552 (2015). https://doi.org/10.1007/s10846-014-0060-7

    Article  Google Scholar 

  156. Yoo, J., Johansson, K.H.: Semi-supervised learning for mobile robot localization using wireless signal strengths. In: 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8 (2017). https://doi.org/10.1109/IPIN.2017.8115921

  157. Youssef, M., Agrawala, A.: The Horus WLAN location determination system. In: Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services, Association for Computing Machinery, New York, NY, USA, MobiSys ’05, pp. 205–218 (2005). https://doi.org/10.1145/1067170.1067193

  158. Zafari, F., Gkelias, A., Leung, K.: A survey of indoor localization systems and technologies. IEEE Commun. Surveys Tutor. 21(3), 2568–2599 (2017). https://doi.org/10.1109/COMST.2019.2911558

    Article  Google Scholar 

  159. Zhang, M., Wen, Y., Chen, J., Yang, X., Gao, R., Zhao, H.: Pedestrian Dead-Reckoning indoor localization based on OS-ELM. IEEE Access 6, 6116–6129 (2018). https://doi.org/10.1109/ACCESS.2018.2791579

    Article  Google Scholar 

  160. Zhang, W., Wang, L., Qin, Z., Zheng, X., Sun, L., Jin, N., Shu, L.: INBS: An Improved Naive Bayes Simple learning approach for accurate indoor localization. In: 2014 IEEE International Conference on Communications (ICC), pp. 148–153 (2014). https://doi.org/10.1109/ICC.2014.6883310

  161. Zhang, W., Sengupta, R., Fodero, J., Li, X.: DeepPositioning: Intelligent fusion of pervasive magnetic field and WiFi fingerprinting for smartphone indoor localization via deep learning. In: 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 7–13 (2017). https://doi.org/10.1109/ICMLA.2017.0-185

  162. Zhou, B., Li, Q., Mao, Q., Tu, W.: A Robust Crowdsourcing-based Indoor Localization System. Sensors 17(4), 864 (2017). https://doi.org/10.3390/s17040864

    Article  Google Scholar 

  163. Zhou, M., Tang, Y., Tian, Z., Geng, X.: Semi-Supervised Learning for indoor hybrid fingerprint database calibration with low effort. IEEE Access 5, 4388–4400 (2017). https://doi.org/10.1109/ACCESS.2017.2678603

    Article  Google Scholar 

  164. Zhou, Q., Zhou, H., Li, T.: Cost-sensitive feature selection using random forest: Selecting low-cost subsets of informative features. Knowl.-Based Syst. 95, 1–11 (2016). DOI10.1016/j.knosys.2015.11.010. http://www.sciencedirect.com/science/article/pii/S0950705115004372

    Article  Google Scholar 

  165. Zhuang, Y., Yang, J., Li, Y., Qi, L., El-Sheimy, N.: Smartphone-based Indoor Localization with Bluetooth Low Energy Beacons. Sensors 16(5), 596 (2016). https://doi.org/10.3390/s16050596

    Article  Google Scholar 

  166. Zia, K., Iram, H., Aziz-ul-Haq, M., Zia, A.: Comparative study of classification techniques for indoor localization of mobile devices. In: 2018 28th International Telecommunication Networks and Applications Conference (ITNAC), pp. 1–5 (2018). https://doi.org/10.1109/ATNAC.2018.8615220

  167. Zou, H., Wang, H., Xie, L., Jia, Q.S.: An RFID Indoor positioning system by using weighted path loss and extreme learning machine. In: 2013 IEEE 1St International Conference on Cyber-Physical Systems, Networks, and Applications (CPSNA), pp. 66–71. IEEE (2013). https://doi.org/10.1109/CPSNA.2013.6614248

  168. Zou, H., Lu, X., Jiang, H., Xie, L.: A fast and precise indoor localization algorithm based on an online sequential extreme learning machine. Sensors 15 (1), 1804–1824 (2015). https://doi.org/10.3390/s150101804

    Article  Google Scholar 

  169. Zou, H., Huang, B., Lu, X., Jiang, H., Xie, L.: A robust indoor positioning system based on the procrustes analysis and weighted extreme learning machine. IEEE Trans. Wireless Commun. 15(2), 1252–1266 (2016). https://doi.org/10.1109/TWC.2015.2487963

    Article  Google Scholar 

  170. Zou, H., Zhou, Y., Jiang, H., Huang, B., Xie, L., Spanos, C.: Adaptive localization in dynamic indoor environments by transfer kernel learning. In: 2017 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6 (2017). https://doi.org/10.1109/WCNC.2017.7925444

Download references

Acknowledgment

This research work is supported by the State Government Fellowship Scheme of Jadavpur University funded by the Government of West Bengal, India and the project entitled- “Developing Framework for Indoor Location Based Services with Seamless Indoor Outdoor Navigation by expanding Spatial Data Infrastructure”, funded by the Ministry of Science and Technology, Department of Science and Technology, NGP Division, Government of India, ref no. NRDMS/UG/NetworkingProject/e-13/2019(G). We would like to thank the anonymous reviewers and the editor for considering our manuscript and providing valuable reviews which has greatly enhanced the quality of the paper.

Funding

This research work is partially supported by the State Government Fellowship Scheme of Jadavpur University funded by the Government of West Bengal, India and the project entitled- “Developing Framework for Indoor Location Based Services with Seamless Indoor Outdoor Navigation by expanding Spatial Data Infrastructure”, funded by the Ministry of Science and Technology, Department of Science and Technology, NGP Division, Government of India, ref no. NRDMS/UG/NetworkingProject/e-13/2019(G).

Author information

Authors and Affiliations

  1. Computer Science and Engineering, Jadavpur University, Kolkata, India

    Priya Roy & Chandreyee Chowdhury

Authors
  1. Priya Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chandreyee Chowdhury
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Priya Roy conceived the study, performed the literature search, participated in the sequence alignment, drafted the manuscript and revised it critically for important intellectual content. Chandreyee Chowdhury conceived the study, revised it critically for important intellectual content and given final approval of the version to be published. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Priya Roy.

Ethics declarations

Ethics Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Roy, P., Chowdhury, C. A Survey of Machine Learning Techniques for Indoor Localization and Navigation Systems. J Intell Robot Syst 101, 63 (2021). https://doi.org/10.1007/s10846-021-01327-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-021-01327-z

Keywords

Search

Navigation