Neural Inertial

Neural inertial methods leverage deep learning to improve the accuracy and efficiency of inertial navigation systems, primarily focusing on overcoming limitations of traditional inertial measurement units (IMUs) like drift and magnetometer interference in indoor environments. Current research employs recurrent neural networks, transformers, and hybrid architectures to process IMU data, often incorporating techniques like time-frequency analysis and body-frame differentiation to enhance performance. This work is significant for enabling more accurate and robust localization and motion tracking in various applications, including robotics, wearable technology, and the Internet of Things, particularly where GPS is unavailable or unreliable.