Invariant Kalman

Invariant Kalman filtering is a state estimation technique leveraging Lie group theory to improve the accuracy and consistency of estimations, particularly in nonlinear systems like those found in robotics and autonomous navigation. Current research focuses on applying this framework to various platforms, including legged and wheeled robots, unmanned aerial vehicles, and multi-robot systems, often incorporating sensor fusion from IMUs, GNSS, LiDAR, and proprioceptive data. This approach addresses challenges posed by nonlinearity and uncertainty in sensor measurements, leading to more robust and reliable state estimation crucial for advanced control and autonomous operation in diverse environments.