IOAM: A Novel Sensor Fusion-Based Wearable for Localization and Mapping
Remote Sensing, November 2022
Renjie Wu, Boon Giin Lee, Matthew Pike, Linzhen Zhu, Xiaoqing Chai, Liang Huang, Xian Wu. 2022. IOAM: A Novel Sensor Fusion-Based Wearable for Localization and Mapping. In Remote Sensing. DOI:https://doi.org/10.3390/rs14236081
Renjie Wu and Boon Giin Lee and Matthew Pike and Linzhen Zhu and Xiaoqing Chai and Liang Huang and Xian Wu. (2022). IOAM: A Novel Sensor Fusion-Based Wearable for Localization and Mapping. Remote Sensing. https://doi.org/10.3390/rs14236081
Renjie Wu and Boon Giin Lee and Matthew Pike and Linzhen Zhu and Xiaoqing Chai and Liang Huang and Xian Wu. "IOAM: A Novel Sensor Fusion-Based Wearable for Localization and Mapping." Remote Sensing, 2022. https://doi.org/10.3390/rs14236081
Renjie Wu, Boon Giin Lee, Matthew Pike, Linzhen Zhu, Xiaoqing Chai, Liang Huang, Xian Wu. 2022. IOAM: A Novel Sensor Fusion-Based Wearable for Localization and Mapping. Remote Sensing. doi:10.3390/rs14236081
Renjie Wu and Boon Giin Lee and Matthew Pike and Linzhen Zhu and Xiaoqing Chai and Liang Huang and Xian Wu, "IOAM: A Novel Sensor Fusion-Based Wearable for Localization and Mapping," Remote Sensing, 2022. doi: 10.3390/rs14236081
@article{remote-sensing-2022,
title={IOAM: A Novel Sensor Fusion-Based Wearable for Localization and Mapping},
author={Renjie Wu and Boon Giin Lee and Matthew Pike and Linzhen Zhu and Xiaoqing Chai and Liang Huang and Xian Wu},
journal={Remote Sensing},
year={2022},
doi={10.3390/rs14236081}
}
indoor location-based service (ILBS), inertial navigation system (INS), wearable sensing, data fusion, ultrasound mapping, dual trajectory fusion (DTF), minimum centroid distance (MCD)
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.