FDIO: Extended Kalman Filter-Aided Deep Inertial Odometry-Reference-Cited by-同舟云学术

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FDIO: Extended Kalman Filter-Aided Deep Inertial Odometry

Published:2023-07-08 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Advanced Robotics and Mechatronics (ICARM)
language:
Short-container-title:

Author:

Wang Yingying¹,Cheng Hu¹,Meng Max Q.-H.²

Affiliation:

1. The Chinese University of Hong Kong,Robotics, Perception and Artificial Intelligence Lab,Department of Electronic Engineering,Hong Kong SAR,China

2. Southern University of Science and Technology,Department of Electronic and Electrical Engineering,Shenzhen,China

Funder

National Key R&D program of China

Hong Kong RGC

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10218726/10218397/10218871.pdf?arnumber=10218871

Reference29 articles.

1. RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, & New Methods

2. IONet: Learning to Cure the Curse of Drift in Inertial Odometry

3. A novel multi-feature based on-site calibration method for lidar-imu systems;liu;IEEE Transactions on Industrial Electronics,2019

4. Keyframe-based visual–inertial odometry using nonlinear optimization

5. Inertial Odometry Using Hybrid Neural Network With Temporal Attention for Pedestrian Localization

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