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Mapless Indoor Navigation Based on Landmarks

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Distributed, Ambient and Pervasive Interactions. Smart Living, Learning, Well-being and Health, Art and Creativity (HCII 2022)

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Abstract

With the increasing user demands for the ubiquitous availability of location-based services, and the acknowledgement of their substantial business prospects, researchers have extensively studied indoor navigation techniques that do not require extra infrastructures to provide accurate indoor navigation. Recently, Landmark, which is available in many scenarios without additional deployment cost, are integrated in plenty of works. However, existing feature extraction and selection methods to detect landmark involve manual feature engineering, which is time-consuming, laborious, and prone to error and additional Wi-Fi fingerprint is used to identify landmark. Therefore, this paper proposes an indoor navigation based on landmark, which is recognized by an unsupervised feature learning method to automatically extracts and selects the features, without extra deployment cost. The proposed method jointly trains denoising autoencoder implemented by convolutional neural network and LSTM neural networks produces a compact feature representation of the data to identify landmark. Besides, the relative distance between different landmarks is estimated by PDR to generate the indoor landmark map with the help of multidimensional scaling technique. The effectiveness of the proposed framework is verified through the experiments in the context of practical buildings. Experimental results show that the proposed method, which learns useful features automatically outperforms conventional classifiers that require the hand-engineering of features. We also show that the proposed method can build mostly correct indoor maps and provides efficient directions to users without extra infrastructure.

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References

  1. Kunhoth, J., Karkar, A., Al-Maadeed, S., Al-Ali, A.: Indoor positioning and wayfinding systems: a survey. HCIS 10(1), 1–41 (2020). https://doi.org/10.1186/s13673-020-00222-0

    Article  Google Scholar 

  2. Davidson, P., Piché, R.: A survey of selected indoor positioning methods for smartphones. IEEE Commun. Surv. Tutor. 19(2), 1347–1370, Secondquarter (2017). https://doi.org/10.1109/COMST.2016.2637663

  3. Brena, R.F.: Evolution of indoor positioning technologies: a survey, J. Sensors 2017, 2630413, 21 p (2017). https://doi.org/10.1155/2017/2630413

  4. Konomi, S., Gao, L., Mushi, D.: An intelligent platform for offline learners based on model-driven crowdsensing over intermittent networks. In: Rau, P.-L. (ed.) HCII 2020. LNCS, vol. 12193, pp. 300–314. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49913-6_26

    Chapter  Google Scholar 

  5. Lin, T., Li, L., Lachapelle, G.: Multiple sensors integration for pedestrian indoor navigation. In: 2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–9 (2015). https://doi.org/10.1109/IPIN.2015.7346785

  6. Fellner, I., Huang, H., Gartner, G.: Turn left after the WC, and use the lift to go to the 2nd floor”—generation of landmark-based route instructions for indoor navigation. ISPRS Int. J. Geo-Inf. 6, 183 (2017). https://doi.org/10.3390/ijgi6060183

    Article  Google Scholar 

  7. Yao, G., Wang, W., Chen, X.: FreeNavi: landmark-based mapless indoor navigation based on WiFi fingerprints. In: 2017 IEEE 85th Vehicular Technology Conference: VTC2017-Spring. IEEE (2017)

    Google Scholar 

  8. Wang, H., Sen, S., Elgohary, A., Farid, 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 (MobiSys 2012), pp. 197–210. Association for Computing Machinery, New York (2012). https://doi.org/10.1145/2307636.2307655

  9. Gu, F., Valaee, S., Khoshelham, K., Shang, J., Zhang, R.: Landmark graph-based indoor localization. IEEE Internet of Things J. 7(9), 8343–8355 (2020), https://doi.org/10.1109/JIOT.2020.2989501

  10. Gu, F., et al.: Indoor localization improved by spatial context—a survey. ACM Comput. Surv. 52, 64:1–64:35 (2019). https://doi.org/10.1145/3322241

  11. Zhou, B., Li, Q., Mao, Q., Tu, W., Zhang, X., Chen, L.: Alimc: activity landmark-based indoor mapping via crowdsourcing. IEEE Trans. Intell. Transp. Syst. 16(5), 2774–2785 (2015)

    Article  Google Scholar 

  12. Xiong, J., Jamieson, K.: ArrayTrack: a fine-grained indoor location system. In: Proceedings of the 10th USENIX Conference on Networked Systems Design and Implementation (NSDI 2013), pp. 71–84. USENIX Association (2013)

    Google Scholar 

  13. Seybold, J.S.: Introduction to RF Propagation. Wiley-Interscience, Hoboken (2005)

    Google Scholar 

  14. El-Kafrawy, H., Youssef, M., El-Keyi, A.: Impact of the human motion on the variance of the received signal strength of wireless links. In: Proceedings of the 22nd Personal In-door and Mobile Radio Communications (PIMRC).pp. 1208–1212. IEEE (2011)

    Google Scholar 

  15. Bahl, P., Padmanabhan, V.N. RADAR: an in-building RF-based user location and tracking system. In: Proceedings of the Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2000), vol. 2, pp. 775–784.Tel Aviv, Israel, 26–30 March 2000

    Google Scholar 

  16. Youssef, M., Ashok A.: The horus WLAN location determination system. In: Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services (MobiSys 2005), pp. 205–218, Association for Computing Machinery, New York (2014). https://doi.org/10.1145/1067170.1067193

  17. Abdullah, O.A., Abdel-Qader, I.: Machine learning algorithm for wireless indoor localization. In: Farhadi, H. (ed.) Machine Learning -Advanced Techniques And Emerging Applications, IntechOpen (2018). https://doi.org/10.5772/intechopen.74754

  18. Mirowski, P., Steck, H., Whiting, P., Palaniappan, R., MacDonald, M., Ho, T.K.: KL-divergence kernel regression for non-Gaussian fingerprint based localization. In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (2011)

    Google Scholar 

  19. Abdullah, O., Abdel-Qader, I., Bazuin, B.: A probability neural network-Jensen-Shannon divergence for a fingerprint based localization. In: 2016 Annual Conference on Information Science and Systems (CISS), Princeton, NJ, USA, pp. 286–291 (2016). https://doi.org/10.1109/CISS.2016.7460516

  20. Liu, T., Zhang, X., Zhang, H., Tahir, N., Fang, Z.: A structure landmark-based radio signal mapping approach for sustainable indoor localization. Sustainability 13 (2021)

    Google Scholar 

  21. Shin, H., Chon, Y., Cha, H.: Unsupervised construction of an indoor floor plan using a smartphone. IEEE Trans. Syst. Man, Cybern. Part C Appl. Rev. 42(6), 889–898. (2012)

    Google Scholar 

  22. Abdelnasser, H., Mohamed, R., Elgohary, A.: SemanticSLAM: using environment landmarks for unsupervised indoor localization. IEEE Trans. Mob. Comput. 15(7), 1770–1782 (2016)

    Article  Google Scholar 

  23. Ma, L., Fang, T., Qin, D.: WalkSLAM: a walking pattern-based mobile SLAM solution. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds.) CSPS 2018. LNEE, vol. 516, pp. 1347–1354. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-6504-1_160

    Chapter  Google Scholar 

  24. Gentner, C., Avram, D.: WiFi-RTT-SLAM: simultaneously estimating the positions of mobile devices and WiFi-RTT access points. In: Proceedings of the 34th International Technical Meeting of the Satellite, September, pp. 3142–3148. Division of the Institute of Navigation (ION GNSS+ 2021), St. Louis (2021)

    Google Scholar 

  25. Chen, L., Wu, J., Yang, C.: Meshmap: a magnetic field-based indoor navigation system with crowdsourcing support. IEEE Access 8, 39959–39970 (2020)

    Article  Google Scholar 

  26. 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)

    Article  Google Scholar 

  27. Gu, F., Khoshelham, K., Shang, J., Yu, F.: Sensory landmarks for indoor localization. In: 2016 Ubiquitous Positioning, Indoor Navigation and Location-Based Services. IEEE. (2016)

    Google Scholar 

  28. Yuce, M.R.: Landmark-assisted compensation of user's body shadowing on RSSI for improved indoor localisation with chest-mounted wearable device. Sensors 21 (2021)

    Google Scholar 

  29. Zhou, B., Yang, J., Li, Q.: Smartphone-based activity recognition for indoor localization using a convolutional neural network. Sensors 19(3) (2019)

    Google Scholar 

  30. Bhattarai, B., Yadav, R.K., Gang, H.S., Pyun, J.Y.: Geomagnetic field based indoor landmark classification using deep learning. IEEE Access 7, 1–1 (2019)

    Google Scholar 

  31. Wang, Y., Zhang, J., Zhao, H., Liu, M., Niu, X.: Spatial structure-related sensory landmarks recognition based on long short-term memory algorithm. Micromachines 12(7), 781 (2021)

    Google Scholar 

  32. Li, T., Han, D., Chen, Y., Zhang, R., Hedgpeth, T.: Indoorwaze: a crowdsourcing-based context-aware indoor navigation system. IEEE Trans. Wirel. Commun. 99, 1–1 (2020)

    Google Scholar 

  33. Jiang, Y., et al.: Hallway based automatic indoor floorplan construction using room fingerprints. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013), pp. 315–324. Association for Computing Machinery, New York (2013). https://doi.org/10.1145/2493432.2493470

  34. Weinberg, H.: Using the ADXL202 in Pedometer and Personal Navigation Applications. Analog Devices Inc., Norwood (2002)

    Google Scholar 

  35. Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. Machine learning. In: Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, 5–9 June 2008

    Google Scholar 

  36. Jiang, J.-R., Subakti, H., Liang, H.-S.: Fingerprint feature extraction for indoor localization. Sensors 21, 5434 (2021). https://doi.org/10.3390/s21165434

    Article  Google Scholar 

  37. Garcia, K.D., et al.: An ensemble of autonomous auto-encoders for human activity recognition. Neurocomputing 439, 271–280 (2021). https://doi.org/10.1016/j.neucom.2020.01.125

  38. Zhao, Y., Yang, R., Chevalier, G., Gong, M.. Xu, X, Zhang, Z.: Deep residual Bidir-LSTM for human activity recognition using wearable sensors. Math. Probl. Eng. 2018, 1–13 (2018). https://doi.org/10.1155/2018/7316954

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Acknowledgments

This work was supported by CSC scholarship, JSPS KAKENHI Grant Numbers 20H00622 and 17KT0154.

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Authors and Affiliations

  1. Graduate School of Information Science and Electrical Engineering, Kyushu University, 744, Motooka, Nishi-ku, 819-0395, Fukuoka, Japan

    Lulu Gao

  2. Faculty of Arts and Science, Kyushu University, 744, Motooka, Nishi-ku, 819-0395, Fukuoka, Japan

    Shin’ichi Konomi

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  1. Lulu Gao
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Correspondence to Lulu Gao .

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Editors and Affiliations

  1. Smart Future Initiative, Frankfurt am Main, Germany

    Norbert A. Streitz

  2. Kyushu University, Fukuoka, Japan

    Shin'ichi Konomi

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Gao, L., Konomi, S. (2022). Mapless Indoor Navigation Based on Landmarks. In: Streitz, N.A., Konomi, S. (eds) Distributed, Ambient and Pervasive Interactions. Smart Living, Learning, Well-being and Health, Art and Creativity. HCII 2022. Lecture Notes in Computer Science, vol 13326. Springer, Cham. https://doi.org/10.1007/978-3-031-05431-0_4

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  • DOI: https://doi.org/10.1007/978-3-031-05431-0_4

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