Multi-sensor fusion approach for improving map-based indoor pedestrian localization

Hsiang Yun Huang, Chia Yeh Hsieh, Kai Chun Liu, Hui Chun Cheng, Steen J. Hsu, Chia Tai Chan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


The interior space of large-scale buildings, such as hospitals, with a variety of departments, is so complicated that people may easily lose their way while visiting. Difficulties in wayfinding can cause stress, anxiety, frustration and safety issues to patients and families. An indoor navigation system including route planning and localization is utilized to guide people from one place to another. The localization of moving subjects is a critical-function component in an indoor navigation system. Pedestrian dead reckoning (PDR) is a technology that is widely employed for localization due to the advantage of being independent of infrastructure. To improve the accuracy of the localization system, combining different technologies is one of the solutions. In this study, a multi-sensor fusion approach is proposed to improve the accuracy of the PDR system by utilizing a light sensor, Bluetooth and map information. These simple mechanisms are applied to deal with the issue of accumulative error by identifying edge and sub-edge information from both Bluetooth and the light sensor. Overall, the accumulative error of the proposed multi-sensor fusion approach is below 65 cm in different cases of light arrangement. Compared to inertial sensor-based PDR system, the proposed multi-sensor fusion approach can improve 90% of the localization accuracy in an environment with an appropriate density of ceiling-mounted lamps. The results demonstrate that the proposed approach can improve the localization accuracy by utilizing multi-sensor data and fulfill the feasibility requirements of localization in an indoor navigation system.

Original languageEnglish
Article number3786
JournalSensors (Switzerland)
Issue number17
StatePublished - 1 Sep 2019


  • Indoor pedestrian localization
  • Inertial sensor
  • Light sensor
  • Multi-sensor fusion


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