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Conference Papers Year : 2018

Machine Learning-Based Real-Time Indoor Landmark Localization

Zhongliang Zhao
  • Function : Author
  • PersonId : 1052592
Jose Carrera
  • Function : Author
  • PersonId : 1052593
Joel Niklaus
  • Function : Author
  • PersonId : 1052594
Torsten Braun
  • Function : Author
  • PersonId : 998253


Nowadays, smartphones can collect huge amounts of data from their surroundings with the help of highly accurate sensors. Since the combination of the Received Signal Strengths of surrounding access points and sensor data is assumed to be unique in some locations, it is possible to use this information to accurately predict smartphones’ indoor locations. In this work, we apply machine learning methods to derive the correlation between smartphones’ locations and the received Wi-Fi signal strength and sensor values. We have developed an Android application that is able to distinguish between rooms on a floor, and special landmarks within the detected room. Our real-world experiment results show that the Voting ensemble predictor outperforms individual machine learning algorithms and it achieves the best indoor landmark localization accuracy of 94% in office-like environments. This work provides a coarse-grained indoor room recognition and landmark localization within rooms, which can be envisioned as a basis for accurate indoor positioning.
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Dates and versions

hal-02269720 , version 1 (23-08-2019)





Zhongliang Zhao, Jose Carrera, Joel Niklaus, Torsten Braun. Machine Learning-Based Real-Time Indoor Landmark Localization. International Conference on Wired/Wireless Internet Communication (WWIC), Jun 2018, Boston, MA, United States. pp.95-106, ⟨10.1007/978-3-030-02931-9_8⟩. ⟨hal-02269720⟩
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