A Multi-IMU Based Self-contained Pedestrian Navigation Algorithm

Yi Han Jen, Chi Hsin Huang, Syun Tsai, Kai Wei Chiang

Research output: Contribution to journalConference articlepeer-review


The demand for navigation and positioning is increasing in various fields nowadays. Although Global Navigation Satellite Systems (GNSS) are currently the most widely used high-accuracy positioning method, their operation rests on signal transmission and reception, which is prone to interference from obstacles such as high-rise buildings, thereby limiting indoor navigation. In addition, in highly dynamic scenarios, the low update rate of the signal cannot track detailed motions. In this case, Inertial Measurement Units (IMUs) play an important role in serving as a complementary component. However, a single high-accuracy IMU is financially prohibitive. On the other hand, the lack of accuracy and stability limits the application of low-cost IMU in navigation. One way to improve the performance of low-cost IMU is to fuse multiple IMUs. This study focuses on the development of pedestrian navigation using multi-sensor integration of low-cost IMUs and magnetometers exploring different integration techniques to compare their performance. The Pedestrian Dead Reckoning (PDR) algorithm is a technique used to estimate the relative motion of pedestrian, which is a commonly used technology for indoor pedestrian navigation. This study use PDR with multi-sensor integration of low-cost IMUs to reduce the position error of pedestrian navigation to within one meter, with the aim of establishing a high-precision, reliable, and low-cost pedestrian navigation algorithm.

Original languageEnglish
Pages (from-to)603-608
Number of pages6
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Issue number1/W1-2023
Publication statusPublished - 2023 May 25
Event12th International Symposium on Mobile Mapping Technology, MMT 2023 - Padua, Italy
Duration: 2023 May 242023 May 26

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Geography, Planning and Development


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