The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 391–397, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-391-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 391–397, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-391-2020

  25 Aug 2020

25 Aug 2020

A 3D MAP AIDED DEEP LEARNING BASED INDOOR LOCALIZATION SYSTEM FOR SMART DEVICES

Y. Yang, C. Toth, and D. Brzezinska Y. Yang et al.
  • Dept. of Civil, Environmental and Geodetic Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue Columbus, OH 43210, USA

Keywords: Indoor localization, Deep learning, Wi-Fi fingerprinting, 3D map, Sensor fusion

Abstract. Indoor positioning technologies represent a fast developing field of research due to the rapidly increasing need for indoor location-based services (ILBS); in particular, for applications using personal smart devices. Recently, progress in indoor mapping, including 3D modeling and semantic labeling started to offer benefits to indoor positioning algorithms; mainly, in terms of accuracy. This work presents a method for efficient and robust indoor localization, allowing to support applications in large-scale environments. To achieve high performance, the proposed concept integrates two main indoor localization techniques: Wi-Fi fingerprinting and deep learning-based visual localization using 3D map. The robustness and efficiency of technique is demonstrated with real-world experiences.