Neural inertial navigation system on pedestrian
文献类型：期刊论文
| 作者 | Huang, Fengrong1; Gao, Min1; Liu, Qinglin1; Tang, Fulin2 ; Wu, Yihong2
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| 刊名 | MEASUREMENT SCIENCE AND TECHNOLOGY
 |
| 出版日期 | 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 |
| WOS记录号 | WOS:001026182000001 |
| 出版者 | IOP Publishing Ltd |
| 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.Hebei Univ Technol, Sch Mech Engn, Tianjin 300400, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, 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. |
入库方式： OAI收割
来源：自动化研究所
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