IMU: Difference between revisions

Raw data from individual sensors can be noisy (e.g., accelerometers during fast movement) and inaccurate (e.g., gyroscopes drift). [[Sensor fusion]] algorithms, such as [[Kalman filter]]s or complementary filters, are essential.<ref name="Mahony_Filter"> Mahony, R., Hamel, T., & Pflimlin, J. M. (2008). Nonlinear Complementary Filters on the Special Orthogonal Group. IEEE Transactions on Automatic Control, 53(5), 1203–1218. [https://ieeexplore.ieee.org/document/4532514 Abstract Link]</ref> These algorithms intelligently combine the data from the accelerometers, gyroscopes (and magnetometers, if present) to produce a single, more accurate, stable, and low-latency estimate of the device's orientation in real-time.