Invariant Extended Kalman

Invariant Extended Kalman Filtering (IEKF) is a state estimation technique leveraging Lie group theory to improve the accuracy and consistency of filtering in nonlinear systems, particularly those involving rotations and poses in robotics and navigation. Current research focuses on applying IEKF to diverse applications, including legged and wheeled robots, unmanned aerial vehicles, and even human motion capture, often integrating it with other techniques like neural networks and disturbance observers to enhance robustness and accuracy. This approach offers significant advantages over traditional Kalman filtering methods in scenarios with significant nonlinearities and challenging environments, leading to improved performance in various fields like autonomous navigation, robotics, and human-computer interaction.