Attitude Estimation

Attitude estimation focuses on accurately determining the orientation of an object in three-dimensional space, a crucial task across diverse fields like robotics, aerospace, and navigation. Current research emphasizes robust and efficient algorithms, including Kalman filters (both additive and multiplicative variants), deep learning models (e.g., convolutional neural networks and spiking neural networks), and optimization-based approaches, often incorporating data from multiple sensor types (e.g., IMUs, GNSS, cameras). These advancements improve accuracy, reduce computational cost, and enhance robustness to noise and disturbances, leading to improved performance in applications ranging from autonomous vehicle control to satellite navigation.