The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B1-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 181–185, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-181-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 181–185, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-181-2020

  06 Aug 2020

06 Aug 2020

INTEGRATION OF PDR AND IMAGE-BASED POSITIONING AIDED BY ARTIFICIAL NEURAL NETWORKS IN INDOOR ENVIRONMENT

M. C. Hung and K. W. Chiang M. C. Hung and K. W. Chiang
  • Dept. of Geomatics, National Cheng Kung University, No.1, Daxue Road, East District, Tainan, Taiwan

Keywords: Location Based System (LBS), image-based indoor positioning, Artificial Neural Network (ANN), Cascade-Correlation neural Network (CCN), trilateration, Pedestrian Dead Reckoning (PDR)

Abstract. Location based service (LBS) is a popular issue in recent years, which can be applied widely. The most common one is providing the local information and the guide of the Point of Interesting (POI) to users, which means positioning is the necessary technique to put LBS into practice. In an outdoor scenario, the user’s position can be obtained relying on the Global Navigation Satellite System (GNSS), however, the signal of GNSS might be blocked in a building. So, many indoor positioning techniques are developed in the decades, which have the pros and cons respectively. This paper proposes an indoor positioning technique by integrating Pedestrian Dead Reckoning (PDR) with the image-based positioning method, which can decrease the cost significantly because it only needs a camera built-in the smartphone. In the first experiment, we verify the accuracy of positioning by the proposed method, that the mean error in the horizontal direction is about 0.25 meters. In the following experiment, comparing with the misclosure of PDR only and PDR integrated with the proposed method, it can decrease from 8.53% to 1.44%. The improvement is about 83%, therefore, this method is suitable for applying to indoor navigation.