Kernkonzepte
Deep learning techniques revolutionize inertial positioning by addressing error drifts and enhancing accuracy.
Zusammenfassung
The article explores the application of deep learning in inertial positioning, focusing on sensor calibration, IMU integration, and sensor fusion. It discusses classical inertial navigation mechanisms, domain-specific knowledge in pedestrian tracking, zero-velocity update algorithms, and integrating IMU with other sensors. The content is structured into sections covering various aspects of deep learning for inertial positioning.
- Introduction to Inertial Navigation: Discusses the importance of MEMS IMUs in smartphones and vehicles.
- Sensor Calibration: Explores using deep neural networks to calibrate inertial sensors.
- IMU Integration: Examines how deep learning corrects IMU integration errors.
- Sensor Fusion: Discusses the fusion of visual data with inertial information.
- Pedestrian Inertial Positioning: Focuses on correcting PDR and ZUPT using deep learning.
- IMU/GNSS Integrated Positioning: Explores enhancing GNSS/INS integration with deep learning.
Statistiken
"This work was supported by National Natural Science Foundation of China (NFSC) under the Grant Number of 62103427, 62073331, 62103430, 62103429."
"Changhao Chen is sponsored by the Young Elite Scientist Sponsorship Program by CAST (No. YESS20220181)."
Zitate
"Deep neural network models have been leveraged to calibrate inertial sensor noises."
"With the rapid development of deep learning techniques, learning-based inertial solutions have become even more promising."