An accurate indoor map matching algorithm based on activity detection and crowdsourced Wi-Fi

  • WenPing Yu
  • JianZhong ZhangEmail author
  • JingDong Xu
  • YuWei Xu


Map matching has been widely investigated in indoor pedestrian navigation to improve positioning accuracy and robustness. This paper proposes an accurate map matching algorithm based on activity detection and crowdsourced Wi-Fi (AiFiMatch). Firstly, by taking indoor road segments between activity-related locations as nodes, and the activity type from one road segment to another as directed edge, the indoor floor plan is abstracted as a directed graph. Secondly, the smartphone’s motion sensors are utilized to detect different activities based on a decision tree and then the pedestrian’s walking trajectory is divided into subtrajectory sequence according to location-related activities. Finally, the sub-trajectory sequence is matched to the directed graph of indoor floor plan to position the pedestrian by using a Hidden Markov Model (HMM). Simultaneously, Wi-Fi fingerprints are bound to road segments based on timestamp. Through crowdsourcing, a radio map of indoor road segments is constructed. The radio map in turn inversely promotes the HMM based map matching algorithm. AiFiMatch is evaluated by the experiments using smartphones in a teaching building. Experimental results show that the pedestrian can be accurately tracked even without knowing the starting position and AiFiMatch is robust to a certain degree of step length and heading direction errors.


map matching hidden markov model activity detection crowdsourced Wi-Fi 


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  1. 1.
    Wu J, Wang J, Wang L, et al. Dynamics and control of a planar 3-DOF parallel manipulator with actuation redundancy. Mechanism Machine Theor, 2009, 44: 835–849CrossRefzbMATHGoogle Scholar
  2. 2.
    Wu J, Yu G, Gao Y, et al. Mechatronics modeling and vibration analysis of a 2-DOF parallel manipulator in a 5-DOF hybrid machine tool. Mechanism Machine Theor, 2018, 121: 430–445CrossRefGoogle Scholar
  3. 3.
    Zampella F, Jimenez Ruiz A R, Seco Granja F. Indoor positioning using efficient map matching, RSS measurements, and an improved motion model. IEEE Trans Veh Technol, 2015, 64: 1304–1317.CrossRefGoogle Scholar
  4. 4.
    Bahl P, Padmanabhan V N, Balachandran A. Enhancements to the RADAR user location and tracking system. Technical Report No. MSR-TR-2000-12. Redmond, WA: Microsoft Corporation, 2000. 775–784Google Scholar
  5. 5.
    Fontana R J, Gunderson S J. Ultra-wideband precision asset location system. In: Proceedings of the 2002 IEEE Conference on Ultra Wideband Systems and Technologies. Baltimore, MD: IEEE, 2002. 147–150Google Scholar
  6. 6.
    Ni L M, Liu Y, Lau Y C, et al. LANDMARC: Indoor location sensing using active RFID. Wireless Networks, 2004, 10: 701–710CrossRefGoogle Scholar
  7. 7.
    Zafari F, Papapanagiotou I. Enhancing ibeacon based micro-location with particle filtering. In: 2015 IEEE Global Communications Conference (GLOBECOM). San Diego, CA: IEEE, 2015. 1–7Google Scholar
  8. 8.
    Wu J, Wang D, Wang L. A control strategy of a two degrees-offreedom heavy duty parallel manipulator. J Dyn Sys Meas Control, 2015, 137: 061007.CrossRefGoogle Scholar
  9. 9.
    Fu N, Zhang J, Yu W, et al. Crowdsourcing-based wifi fingerprint update for indoor localization. In: Proceedings of the ACM Turing 50th Celebration Conference-China. Shanghai, China, 2017. 34Google Scholar
  10. 10.
    Alaoui F T, Renaudin V, Betaille D. Points of interest detection for map-aided PDR in combined outdoor-indoor spaces. In: Proceedings of the 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN). Sapporo: IEEE, 2017. 1–8Google Scholar
  11. 11.
    Zhou B, Li Q, Mao Q, et al. Activity sequence-based indoor pedestrian localization using smartphones. IEEE Trans Human-Mach Syst, 2015, 45: 562–574CrossRefGoogle Scholar
  12. 12.
    Aly H, Youssef M. semMatch: Road semantics-based accurate map matching for challenging positioning data. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. Seattle, Washington, 2015. 5Google Scholar
  13. 13.
    Zhou B, Li Q, Mao Q, et al. ALIMC: Activity landmark-based indoor mapping via crowdsourcing. IEEE Trans Intell Transp Syst, 2015, 16: 2774–2785.CrossRefGoogle Scholar
  14. 14.
    Abdelnasser H, Mohamed R, Elgohary A, et al. SemanticSLAM: Using environment landmarks for unsupervised indoor localization. IEEE Trans Mobile Comput, 2016, 15: 1770–1782.CrossRefGoogle Scholar
  15. 15.
    Qiu C, Mutka M W. iframe: Dynamic indoor map construction through automatic mobile sensing. Pervasive Mobile Computing, 2017, 38: 346–362CrossRefGoogle Scholar
  16. 16.
    Wang B, Chen Q, Yang L T, et al. Indoor smartphone localization via fingerprint crowdsourcing: Challenges and approaches. IEEE Wireless Commun, 2016, 23: 82–89CrossRefGoogle Scholar
  17. 17.
    Zhang X, Yang Z, Wu C, et al. Robust trajectory estimation for crowdsourcing-based mobile applications. IEEE Trans Parallel Distrib Syst, 2014, 25: 1876–1885.CrossRefGoogle Scholar
  18. 18.
    Xiang L, Tai T Y, Li B, et al. Tack: Learning towards contextual and ephemeral indoor localization with crowdsourcing. IEEE J Sel Areas Commun, 2017, 35: 863–879CrossRefGoogle Scholar
  19. 19.
    Newson P, Krumm J. Hidden Markov map matching through noise and sparseness. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems. Seattle, Washington, 2009. 336–343Google Scholar
  20. 20.
    Seitz J, Jahn J, Boronat J G, et al. A hidden markov model for urban navigation based on fingerprinting and pedestrian dead reckoning. In: 2010 13th Conference on Information Fusion (FUSION). Edinburgh, UK: IEEE, 2010. 1–8Google Scholar
  21. 21.
    Woodman O, Harle R. Pedestrian localisation for indoor environments. In: Proceedings of the 10th International Conference on Ubiquitous Computing. Seoul, 2008. 114–123Google Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • WenPing Yu
    • 1
  • JianZhong Zhang
    • 1
    Email author
  • JingDong Xu
    • 1
  • YuWei Xu
    • 1
  1. 1.College of Computer ScienceNankai UniversityTianjinChina

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