The Performance Analysis of Multi-Sensor Integration Using Smartphone’s Sensors for Seamless Urban and Indoor Navigation

Dinh T. Le, Kuan -Y. Lin, Syun Tsai

Abstract: The widespread popularity of smartphone and the increasing in quality of its sensors make it possible to provide the locationbased service for anyone at anytime, anywhere. However, it is hard to utilize their actual positioning and navigation performance capabilities due to the disparate sensors, software technologies adopted among manufacturers, and the high influence of environmental conditions. In this research, we proposed a framework to seamlessly navigate people from land vehicle in urban area to pedestrian in indoor environment. The scheme for land vehicle navigation is the integration of measurements from inertial measurement unit (IMU), GNSS chipset, and camera through the extended Kalman filter (EKF). In which, visual measurement from camera is pre-processed by ORB-SLAM, re-scaled by the assistance of GNSS measurement, and transformed to velocity before utilized to update the integration filter. For indoor environment, a pedestrian dead reckoning (PDR)/ORB-SLAM integrated system is used to navigate pedestrian. In order to verify the performance of proposed framework, the field tests were conducted in the downtown area of Tainan city, Taiwan with land vehicle and indoor environment with pedestrian. Experimental results indicate that the framework performs well in both phases. It demonstrated that the proposed framework makes smartphone capable of seamlessly navigate people in both outdoor and indoor environments.
Published in: Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020)
September 21 - 25, 2020
Pages: 2299 - 2310
Cite this article: Le, Dinh T., Lin, Kuan -Y., Tsai, Syun, "The Performance Analysis of Multi-Sensor Integration Using Smartphone’s Sensors for Seamless Urban and Indoor Navigation," Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020), , September 2020, pp. 2299-2310.
https://doi.org/10.33012/2020.17545
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