Radiotekhnika
Publishing house Radiotekhnika

"Publishing house Radiotekhnika":
scientific and technical literature.
Books and journals of publishing houses: IPRZHR, RS-PRESS, SCIENCE-PRESS


Тел.: +7 (495) 625-9241

 

The combined SLAM indoor navigation method based on GP-LVM

DOI 10.18127/j20700814-201905-15

Keywords:

M.S. Shchekotov – Research Scientist, St. Petersburg Institute for Informatics and Automation of RAS
E-mail: shekotov@iias.spb.su


The problems associated with the use of indoor localization methods, based on Wi-Fi radio signals, is a time-consuming procedure for configuring and placing equipment, which includes the construction of a room map, creating a radio map or calibrating the radio signal propagation model. To solve this problem the SLAM method based on the Gaussian process latent variable (GP-LVM) is proposed, which includes the phase of the training set formation, as well as the phase of simultaneous navigation and mapping.
Purpose of the work is development of a combined SLAM indoor navigation method based on the Gaussian process latent variable, which provides navigation of the user indoors and at the same time allows you to build maps of Wi-Fi radio signals. The method includes the requirements for the correlation of the signal strength values of the nearest user localization points, for which the parameters of the correlation function are configured based on the training set, which is formed by means of crowd calculations.
A combined SLAM indoor navigation method based on the Gaussian process latent variable for collaborative mapping of Wi-Fi radio signals is proposed. The proposed method is based on a training set formation based on the linear regression, as well as on the dimension reduction for simultaneous navigation and map construction. As a training set, the readings of internal sensors of the smartphone (steps and angles of rotation) and the Wi-Fi signal strength obtained using crowd calculations are used. The resulting training set is used to determine the parameters of the correlation function that determines the correlation between the points of localization of the user. The procedure of gamification of the process of determining the user's entrance to the room and search for Wi-Fi access points is also proposed.
The proposed method allows not to carry out the preliminary time-consuming procedures for building maps of premises and radio signals.

References:
  1. Kuusik A., Roche S., Weis F. SMARTMUSEUM: cultural content recommendation system for mobile users. Proc. of Fourth International Conference on Computer Sciences and Convergence Information Technology. 2009. P. 477−482.
  2. 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. V. 4. P. 1−14.
  3. Mekelleche F., Haffaf H. Classification and comparison of range-based localization techniques in wireless sensor networks. Journal of Communications. 2017. V. 12. № 4. P. 221−227.
  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. V. 45(3−4). P. 181−198.
  5. Dayekh S., Affes S., Kandil N., Nerguizian C. Cooperative localization in mines using fingerprinting and neural networks. Proc. of IEEE Wireless Communications and Networking Conference. 2010. P. 1−6.
  6. Qiao G.-Z., Zeng J.-C. Localization algorithm of beacon nodes chain deployment based on coal mine underground wireless sensor networks. Meitan Xuebao – Journal of the China Coal Society. 2010. V. 35(7). P. 1229−1233.
  7. Savic V., Wymeersch H., Larsson E.G. Simultaneous sensor localization and target tracking in mine tunnels. Proc. of the 16th International Conference on Information Fusion. 2013. P. 1427−1433.
  8. De Blasio G., Quesada-Arencibia A., García C.R., Molina-Gil J.M., Caballero-Gil C. Study on an indoor positioning system for harsh environments based on Wi-Fi and Bluetooth low energy. Sensors. 2017. V. 17(6).
  9. Röhrig C., Heß D., Künemund F. Constrained Kalman filtering for indoor localization of transport vehicles using floor-installed HF RFID transponders. Proc. of the 9th IEEE International Conference on RFID (IEEE RFID 2015). 2015. P. 113−120.
  10. 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. V. 8. № 7. P. 3597−3607.
  11. 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. V. 12(8). P. 11187−11204.
  12. 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. P. 104.
  13. 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.
  14. Sun Z., Farley R., Kaleas T., Ellis J., Chikkappa K. Cortina: collaborative context-aware indoor positioning employing RSS and RToF techniques. Proc. IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). 2011. P. 340−343.
  15. Sivers M., Fokin G., Dmitriev P., Kireev A., Volgushev D., Ali A.A. H. Indoor positioning in WiFi and NanoLOC networks. Proc. of International Conference on Next Generation Wired/Wireless Networking Conference on Internet of Things and Smart Spaces. 2016.
  16. 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. Proc of IEEE International Symposium on Antennas and Propagation (APSURSI). 2016. P. 1445−1446.
  17. Yang S.-H., Kim H.-S., Son Y.-H., Han S.-K. Three-dimensional visible light indoor localization using AOA and RSS with multiple optical receivers. Journal of Lightwave Technology. 2014. V. 32. № 14. P. 2480−2485.
  18. Deliang L., Kaihua L., Yongtao M., Jiexiao Y. Joint TOA and DOA localization in indoor environment using virtual stations. IEEE Communications Letters. 2014. V. 18. № 8. P. 1423−1426.
  19. Zhao X., Xiao Z., Markham A., Trigoni N., Ren Y. Does BTLE measure up against WiFi? A Comparison of indoor location performance. Proc. of the European Wireless 2014: 20th European Wireless Conference. 2014. P. 1−6.
  20. Röbesaat J., Zhang P., Abdelaal M., Theel O. An improved BLE indoor localization with Kalman-based fusion: an experimental study. Sensors. 2017. V. 17(5).
  21. Alsehly F., Mohd Sabri R., Sevak Z., Arslan T. Improving indoor positioning accuracy through a Wi-Fi handover algorithm. Proc. of International Technical Meeting of the Institute of Navigation. 2010. P. 822−829.
  22. Liu Wen & Xiao Fu & Deng Zhongliang Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments. Sensors. 2016. V. 16. P. 2055.
  23. Ferris Brian & Fox Dieter & D. Lawrence Neil WiFi-SLAM using Gaussian process latent variable models. Proceedings of IJCAI 2007. № 7. P. 2480−2485.
  24. Mirowski Piotr & Ho Tin & Yi Saehoon & Macdonald William SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals. 2013 International Conference on Indoor Positioning and Indoor Navigation. IPIN 2013. P. 1−10.
  25. Luo C., Hong H., Chan M.C. PiLoc: a Self-Calibrating Participatory Indoor Localization System. Proc. of 13th International Symposium on Information Processing in Sensor Networks. 2014. P. 143−153.
  26. 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. V. 17. № 1. P. 141−154.
  27. D. Lawrence Neil Gaussian Process Latent Variable Models for Visualizations of High Dimensional Data. Advances in Neural Information Processing Systems. 2004. № 16.
  28. Shchekotov Maksim & Pashkin Michael & Smirnov Alexander Indoor Navigation Ontology for Smartphone Semi-Automatic Self-Calibration Scenario. 24th Conference of Open Innovations Association. 2019. P. 388−394.
June 24, 2020
May 29, 2020

© Издательство «РАДИОТЕХНИКА», 2004-2017            Тел.: (495) 625-9241                   Designed by [SWAP]Studio