| Title | FDIO: Extended Kalman Filter-Aided Deep Inertial Odometry |
| Author | |
| DOI | |
| Publication Years | 2023
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| ISBN | 979-8-3503-0018-5
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| Source Title | |
| Pages | 482-487
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| Conference Date | 8-10 July 2023
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| Conference Place | Sanya, China
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| Keywords | |
| SUSTech Authorship | Others
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| URL | [Source Record] |
| Data Source | IEEE
|
| PDF url | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10218871 |
| Citation statistics |
Cited Times [WOS]:0
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| Document Type | Conference paper |
| Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/559240 |
| Department | Department of Electrical and Electronic Engineering |
| Affiliation | 1.Department of Electronic Engineering, Robotics, Perception and Artificial Intelligence Lab, The Chinese University of Hong Kong, Hong Kong SAR, China 2.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China |
| Recommended Citation GB/T 7714 |
Yingying Wang,Hu Cheng,Max Q.-H. Meng. FDIO: Extended Kalman Filter-Aided Deep Inertial Odometry[C],2023:482-487.
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| Files in This Item: | There are no files associated with this item. | |||||
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