For what it’s worth, the EKF isn’t much more complicated than the regular KF. You are basically just linearizing your dynamics / sensor model then doing the KF

For what it’s worth, the EKF isn’t much more complicated than the regular KF. You are basically just linearizing your dynamics / sensor model then doing the KF updates with that linear model. However, for quadrotors specifically, there is a huge pain point: the rotations. A nuance of the linear models of the Kalman filter is that everything lives in Euclidean space. However, rotations place you on a manifold — for quaternions specifically this manifold is the set of unit quaternions. If you naively apply an EKF to estimate your quaternion, you will no longer have a unit quaternion and then your estimates will be garbage. There are well-known ways to handle this manifold constraint but they result in some of the ugliest equations I have ever turned into code. As a simple example of the difficulty, consider a state (x, y) that, due to physics or whatever, we know will always be on the unit circle, I.e., if f(x, y) is your dynamics, it outputs a new point on the circle. However, if you linearize f(x, y), these approximate dynamics are not guaranteed to keep you on the unit sphere and you end up with a non-physical state (or state estimate for the extended Kalman filter).