CNN

End-to-end deep learning framework for real-time inertial attitude estimation using 6DoF IMU. Achieves 40% accuracy improvement over traditional methods, evaluated on 7 datasets (120+ hours, 200+ km of IMU data). Generalizable across various sampling rates and motion patterns.

Comprehensive review of deep learning applications for autonomous navigation covering obstacle detection, scene perception, path planning, and control. Synthesizes state-of-the-art frameworks for mobile robots, autonomous vehicles, UAVs, and space vehicles.