Abstract
Due to the limited penetration ability of GPS signal to buildings, indoor high-precision positioning combined with a variety of technologies has been paid more and more attention by researchers. Based on the traditional indoor positioning technology, a new indoor positioning method is proposed in this paper, which combines vision and inertial sensor. In this paper, we will first independently evaluate the quality of inertial positioning and visual positioning results, and then integrate them with complementary advantages to achieve the effect of high-precision positioning.
This work is supported by the National Natural Science Foundation of China (61771186), University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (UNPYSCT-2017125), Distinguished Young Scholars Fund of Heilongjiang University, and postdoctoral Research Foundation of Heilongjiang Province (LBH-Q15121).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Radu, V., Marina, M.K.H.: Indoor smartphone localization via activity aware pedestrian dead reckoning with selective crowdsourced WiFi fingerprinting. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–10. IEEE, Montbeliard-Belfort (2013)
Li, Y., He, Z., Nielsen, J.: Enhancing Wi-Fi based indoor pedestrian dead reckoning with security cameras. In: Fourth International Conference on Ubiquitous Positioning, Indoor Navigation and Location Based Services (UPINLBS), pp. 107–112. IEEE, Shanghai (2016)
Yilmaz, A., Gupta, A.: Indoor positioning using visual and inertial sensors. In: SENSORS, pp. 1–3. IEEE, Orlando (2016)
Werner, M., Hahn, C., Schauer, L:. DeepMoVIPS: visual indoor positioning using transfer learning. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–7. IEEE, Alcala de Henares (2016)
Werner, M., Kessel, M., Marouane, C.: Indoor positioning using smartphone camera. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–6. IEEE, Portugal (2011)
Hile, H., Borriello, G.: Information overlay for camera phones in indoor environments. In: Hightower, J., Schiele, B., Strang, T. (eds.) LoCA 2007. LNCS, vol. 4718, pp. 68–84. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75160-1_5
Kao, W.W., Huy, B.Q.: Indoor navigation with smartphone-based visual SLAM and bluetooth-connected wheel-robot. In: CACS International Automatic Control Conference (CACS), pp. 395–400. IEEE, Nantou (2013)
Ma, L., Fan, Y., Xu, Y., et al.: Pedestrian dead reckoning trajectory matching method for radio map crowdsourcing building in WiFi indoor positioning system. In: IEEE International Conference on Communications (ICC), pp. 1–6. IEEE, Paris (2017)
Kang, W., Han, Y.: SmartPDR: Smartphone-based pedestrian dead reckoning for indoor localization. IEEE Sens. J. 15(5), 2906–2916 (2015)
Beauregard, S., Haas, H.: Pedestrian dead reckoning: a basis for personal positioning. In: Proceedings of the 3rd Workshop on Positioning, Navigation and Communication, pp. 27–35 (2006)
Wu, D., Peng, A., Zheng, L., et al.: A smart-phone based hand-held indoor tracking system. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–7. IEEE, Sapporo (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Xu, G., Qin, D., Zhao, M., Guo, R. (2019). Research on Fusion of Multiple Positioning Algorithms Based on Visual Indoor Positioning. In: Han, S., Ye, L., Meng, W. (eds) Artificial Intelligence for Communications and Networks. AICON 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-030-22971-9_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-22971-9_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22970-2
Online ISBN: 978-3-030-22971-9
eBook Packages: Computer ScienceComputer Science (R0)