Inertial Data
Inertial data, derived from accelerometers and gyroscopes, is crucial for estimating motion and position, particularly in applications where GPS is unavailable or unreliable. Current research focuses on improving the accuracy and robustness of inertial-based systems by integrating data from other sensors (e.g., cameras, LiDAR, 5G), employing advanced algorithms like Kalman filters, graph-based optimization, and deep learning architectures (including recurrent neural networks and transformers), and addressing challenges such as sensor noise, drift, and varying environmental conditions. This work is significant for advancing applications in robotics, autonomous navigation, human activity recognition, and other fields requiring precise and reliable motion tracking.