Neural inertial navigation system on pedestrian

doi:10.1088/1361-6501/ace377

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Neural inertial navigation system on pedestrian
	Huang, Fengrong 2; Gao, Min 2; Liu, Qinglin 2; Tang, Fulin 1; Wu, Yihong 1
刊名	MEASUREMENT SCIENCE AND TECHNOLOGY
	2023-10-01
卷号	34 期号:10 页码:14
关键词	localization deep learning pedestrian indoor navigation IEKF inertial navigation
ISSN号	0957-0233
DOI	10.1088/1361-6501/ace377
通讯作者	Tang, Fulin(gl2022312@163.com)
英文摘要	The recent research shows that data-driven inertia navigation technology can significantly alleviate the drift error of micro-electro-mechanical system inertial measurement unit (MEMS-IMU) in pedestrian localization. However, most existing methods must rely on attitude information provided by external procedure (such as smartphone API), which violates the original intention of full autonomy of inertial navigation, and attitude information is also inaccurate. To address the problem, we propose a pedestrian indoor neural inertial navigation system that does not rely on external information and is only based on low-cost MEMS-IMU. First, a deep learning based neural inertial network was designed to estimate attitude. Then, in order to obtain position estimation with both global and local accuracy, an invariant extended Kalman filter (IEKF) framework was proposed, where 3D displacement and its uncertainty regressed by a deep residual network are utilized to update IEKF. Extensive experimental results on a public dataset and a self-collected dataset show that the proposed method provides accurate attitude estimation and outperforms state-of-the-art methods in position estimation, demonstrating the superiority of our method in reliability and accuracy.
WOS关键词	ORIENTATION-ESTIMATION
WOS研究方向	Engineering ; Instruments & Instrumentation
语种	英语
出版者	IOP Publishing Ltd
WOS记录号	WOS:001026182000001
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53624]
专题	多模态人工智能系统全国重点实验室
通讯作者	Tang, Fulin
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Hebei Univ Technol, Sch Mech Engn, Tianjin 300400, Peoples R China
推荐引用方式 GB/T 7714	Huang, Fengrong,Gao, Min,Liu, Qinglin,et al. Neural inertial navigation system on pedestrian[J]. MEASUREMENT SCIENCE AND TECHNOLOGY,2023,34(10):14.
APA	Huang, Fengrong,Gao, Min,Liu, Qinglin,Tang, Fulin,&Wu, Yihong.(2023).Neural inertial navigation system on pedestrian.MEASUREMENT SCIENCE AND TECHNOLOGY,34(10),14.
MLA	Huang, Fengrong,et al."Neural inertial navigation system on pedestrian".MEASUREMENT SCIENCE AND TECHNOLOGY 34.10(2023):14.