IOAM: A Novel Sensor Fusion-Based Wearable for Localization and Mapping
<- Publications
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
Remote Sensing | 2022
| View on Publisher's Website
Abstract
This paper introduces IOAM, a novel sensor fusion-based wearable system for indoor localization and mapping. The system integrates dual foot-mounted inertial navigation systems (DF-INS) with ultrasound sensors to improve localization accuracy and map reconstruction. The proposed minimum centroid distance (MCD) algorithm dynamically constrains stride length, reducing bias in data fusion, while the dual trajectory fusion (DTF) method combines left- and right-foot trajectories into a single center body of mass (CBoM) trajectory. Additionally, ultrasound-based mapping reconstructs the surrounding occupancy grid map (S-OGM) using sphere projection. Experimental results demonstrate significant improvements in localization accuracy, with a root mean square error (RMSE) of 1.2 meters, outperforming existing methods. The findings highlight the potential of IOAM for applications in firefighting, home care, and other indoor navigation scenarios.