I will discuss the generalized approach to sample points on an arbitrary geometry.
I will discuss the math and intuition behind the 3D altitude filter, linear Kalman filter, extended Kalman filter, error-state Kalman filter, and Lie group calculus for deriving the math for orientation estimation.
I will summarize what vanishing points (and their “Gaussian sphere representation”) are, how to represent them, what information they encode, how to find them, and why they are useful.