IOAM: a novel sensor fusion-based wearable for localization and mapping

Renjie Wu, Boon Giin Lee, Matthew Pike, Linzhen Zhu, Xiaoqing Chai, Liang Huang, Xian Wu

Research output: Journal PublicationArticlepeer-review

3 Citations (Scopus)

Abstract

With the development of indoor location-based services (ILBS), the dual foot-mounted inertial navigation system (DF-INS) has been extensively used in many fields involving monitoring and direction-finding. It is a widespread ILBS implementation with considerable application potential in various areas such as firefighting and home care. However, the existing DF-INS is limited by a high inaccuracy rate due to the highly dynamic and non-stable stride length thresholds. The system also provides less clear and significant information visualization of a person’s position and the surrounding map. This study proposes a novel wearable-foot IOAM-inertial odometry and mapping to address the aforementioned issues. First, the person’s gait analysis is computed using the zero-velocity update (ZUPT) method with data fusion from ultrasound sensors placed on the inner side of the shoes. This study introduces a dynamic minimum centroid distance (MCD) algorithm to improve the existing extended Kalman filter (EKF) by limiting the stride length to a minimum range, significantly reducing the bias in data fusion. Then, a dual trajectory fusion (DTF) method is proposed to combine the left- and right-foot trajectories into a single center body of mass (CBoM) trajectory using ZUPT clustering and fusion weight computation. Next, ultrasound-type mapping is introduced to reconstruct the surrounding occupancy grid map (S-OGM) using the sphere projection method. The CBoM trajectory and S-OGM results were simultaneously visualized to provide comprehensive localization and mapping information. The results indicate a significant improvement with a lower root mean square error (RMSE = 1.2 m) than the existing methods.

Original languageEnglish
Article number6081
JournalRemote Sensing
Volume14
Issue number23
DOIs
Publication statusPublished - Dec 2022

Keywords

  • data fusion
  • indoor location-based service
  • inertial navigation system
  • wearable sensing

ASJC Scopus subject areas

  • General Earth and Planetary Sciences

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