M. S. Shchekotov SLAM Method of Indoor Navigation Based on Bluetooth Beacon Localization
M. S. Shchekotov SLAM Method of Indoor Navigation Based on Bluetooth Beacon Localization

One of the problems associated with the implementation of indoor location detection systems is the time-consuming procedure of equipment adjustment, which includes indoor map construction, radio signal map creation and calibrating signal propagation model. Thus, the equipment adjustment is a time-consuming and expensive process that must be performed again when there are changes in equipment configuration and allocation. The developed method provides navigation of the user inside a room and at the same time allows to build radio map and put Bluetooth beacons on the map of a room. The user's navigation inside the room is provided using a combination of PDR based on the built-in smartphone sensors, multilateration and fingerprinting. To solve the problem of determining the location of Bluetooth beacon, the Random Forest algorithm is used, which uses signal levels, user rotation angles and distance to Bluetooth beacon as a training dataset. Based on the radio map and Bluetooth beacon locations, the geometric parameters of a room are estimated.


indoor localization, machine learning, SLAM, crowdsourcing.

PP. 70-81.

DOI 10.14357/20718632210307

1. Shen J., Huang B., Kang X., Jia B. and Li W. Localization of access points based on the Rayleigh lognormal model // 2018 IEEE Wireless Communications and Networking Conference (WCNC). 2018. С. 1-6.
2. Guangbing Z., Jing L., Shugong X., Shunqing Z., Shige M., Kui X. An EKF-based multiple data fusion for mobile robot indoor localization // Assembly Automation. 2021.
3. Yucel, H., Elibol, G., Yayan U. Wi-Fi Based Indoor Positioning System For Mobile Robots By Using Particle Filter
4. Surmann H., Nüchter A., Hertzberg J. An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments // Robotics and Autonomous Systems. 2003. № 45(3-4), pp. 181–198.
5. Yunlei Z., Gong X., Liu K., Shuai Zhang S. Localization and Tracking of an Indoor Autonomous Vehicle Based on the Phase Difference of Passive UHF RFID Signals // Sensors. 2021. № 9.
6. Kuusik A., Roche S., Weis F. SMARTMUSEUM: cultural content recommendation system for mobile users // Proceedings of Fourth International Conference on Computer Sciences and Convergence Information Technology. 2009. pp. 477-482.
7. official website, Available at: (accessed July, 2021)
8. Bluepath official website, Available at:
retail.php (accessed July, 2021)
9. Веб-сайт Navigine, Available at: (accessed July, 2021). 
10. Meena B.S., Laskar R.U., Hemachandran K. Indoor Localization-Based Office Automation System Using IOT Devices // Intelligent Computing in Engineering. Advances in Intelligent Systems and Computing. 2020. № 1125.
11. Interact official website, Available at: https://www.interactlighting.
com/global/what-is-possible/interact-office/indoornavigation (accessed July, 2021).
12. Insoft official website, Available at: (accessed July, 2021)
13. Hesslein N., Wesselhöft M., Hinckeldeyn J., Kreutzfeldt J. Industrial Indoor Localization: Improvement of Logistics Processes Using Location Based Services // Advances in Automotive Production Technology – Theory and Application. 2021.
14. Niu, Q., Yang, X., & Yin, Y. IPL: Image-Assisted Person Localization for Underground Coal Mines // Sensors. 2018. № 18(11).
15. Jinyue Z., Jianing G., Haiming X., Xiangchi L., Daxin Z. A Framework for an Intelligent and Personalized Fire Evacuation Management System // Sensors. 2019. № 19.
16. Tang Z., Zhao Y., Yang L., Qi S., Fang D., Chen X., Gong X., Wang Z. Exploiting wireless received signal strength indicators to detect evil-twin attacks in smart homes // Mobile Information Systems. 2017. № 4, C. 1–14.
17. Cisco official website, Available at:
hyperlocation-solution/index.html (accessed July,
18. Cisco official website, Available at:
(accessed July, 2021)
19. Heidari M., Alsindi N. A., Pahlavan K. UDP identification and error mitigation in ToA-Based indoor localization systems using neural network architecture // IEEE Ttranslations on Wireless Communications. 2009. № 7, C. 3597–3607.
20. Kabir Md. H., Kohno R. A hybrid TOA-fingerprinting based localization of mobile nodes using UWB signaling for non line-of-sight conditions // Sensors. 2012. №12(8), pp. 11187-11204.
21. Liu D., Wang Y., He P., Zhai Y., Wang H. TOA localization for multipath and NLOS environment with virtual station // EURASIP Journal on Wireless Communications and Networking. 2017. С. 104.
22. Xinrong L., Pahlavan K., Latva-aho M., Ylianttila M. Comparison of indoor geolocation methods in DSSS and OFDM wireless LAN systems sign in or purchase // Vehicular Technology Conference. 2000.
23. Sun Z., Farley R., Kaleas T., Ellis J., Chikkappa K. Cortina: collaborative context-aware indoor positioning employing RSS and RToF techniques // Proceedings IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). 2011. С. 340–343.
24. Sivers M., Fokin G., Dmitriev P., Kireev A., Volgushev D., Ali A. A. H. Indoor positioning in WiFi and NanoLOC networks // Proceedings of International Conference on Next Generation Wired/Wireless Networking Conference on Internet of Things and Smart Spaces. 2016.
25. Hanssens B., Plets D., Tanghe E., Oestges C., Gaillot D. P., Liénard M., Martens L., Joseph W. An indoor localization technique based on ultra-wideband AoD/AoA/ToA estimation // Proceedings of IEEE International Symposium on Antennas and Propagation (APSURSI). 2016. С. 1445–1446.
26. Yang S.-H., Kim H.-S., Son Y.-H., Han S.-K. Threedimensional visible light indoor localization using AOA and RSS with multiple optical receivers // Journal of Lightwave Technology. 2014. №. 32 (14), C. 2480–2485.
27. Deliang L., Kaihua L., Yongtao M., Jiexiao Y. Joint TOA and DOA localization in indoor environment using virtual stations // IEEE Communications Letters. 2014. № 18(8), C. 1423–1426.
28. Zhao X., Xiao Z., Markham A., Trigoni N., Ren Y. Does BTLE measure up against WiFi? A Comparison of indoor location performance // Proceedings of the European Wireless 2014: 20th European Wireless Conference. 2014. С. 1–6.
29. Röbesaat J., Zhang P., Abdelaal M., Theel O. An improved BLE indoor localization with Kalman-based fusion: an experimental study // Sensors. 2017. № 17(5). doi:10.3390/s17050951
30. Alsehly F., Mohd Sabri R., Sevak Z., Arslan T. Improving indoor positioning accuracy through a Wi-Fi handover algorithm // Proceedings of International Technical Meeting of the Institute of Navigation. 2010. С. 822–829.
31. Wen L., Fu X., Zhongliang D. Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments // Sensors. 2016. №16. 10.3390/s16122055.
32. Ferris, B., Fox D., D. Lawrence N. WiFi-SLAM using Gaussian process latent variable models // Proceedings of IJCAI. 2007. №7. С. 2480-2485.
33. Mirowski P., Tin H., Saehoon Y., William M. SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals // 2013 International Conference on Indoor Positioning and Indoor Navigation. 2013. С. 1-10. 10.1109/IPIN.2013.6817853.
34. Luo C., Hong H., Chan M. C., PiLoc: a Self-Calibrating Participatory Indoor Localization System // Proceedings of 13th International Symposium on Information Processing in Sensor Networks. 2014. С.143-153. doi: 10.1109/IPSN.2014.6846748.
35. Luo C., Hong H., Chan M. C., Li J. Zhang X., Ming Z. MPiLoc: Self-Calibrating Multi-Floor Indoor Localization Exploiting Participatory Sensing // IEEE Transactions on Mobile Computing. 2018. № 17(1), C. 141 - 154. doi: 10.1109/TMC.2017.2698453.
36. Shchekotov M., Pashkin M., Smirnov A. Indoor Navigation Ontology for Smartphone Semi-Automatic Self- Calibration Scenario // FRUCT. 2019. pp. 388-394. 10.23919/FRUCT.2019.8711902.
37. Goehle, Geoff. Gamification and Web-based Homework // PRIMUS. 2013. № 23. 10.1080/10511970.2012.736451.
38. Scikit-learn website, Available at: https://scikitlearn. org/stable/ (accessed July, 2021).
39. Scikit-learn website, Available at:https://scikitlearn.
n.RandomizedSearchCV.html (accessed July, 2021).

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