Gyroscope Bias

Gyroscope bias, the systematic error in angular rate measurements, significantly impacts the accuracy of inertial navigation systems and related applications. Current research focuses on developing efficient calibration techniques, employing methods like deep learning, least-squares algorithms, and Kalman filtering to estimate and compensate for this bias, often integrating data from multiple gyroscopes or sensor fusion with accelerometers and magnetometers. These advancements are crucial for improving the precision of motion tracking in various fields, including robotics, wearable health monitoring, and autonomous vehicles, where accurate orientation estimation is paramount.