The best introduction I have seen so far is the Udacity course on robotic cars, although it touches very slightly the Kalman filter. I think the exposition, sta

The best introduction I have seen so far is the Udacity course on robotic cars, although it touches very slightly the Kalman filter. I think the exposition, starting with the general case of bayesian filtering and then deriving from it several types of filters is very clear. If you want to dig more I think the best book, by far, is "Applied Optimal Estimation" by Arthur Gelb. (I have not read yet Probabilistic Robotics, though). Finally, a finer point of Kalman filtering which is not normally mentioned is that you don't need your distributions to be gaussian. If your distributions are gaussian the Kalman filter is optimal, if they are not gaussian then the Kalman filter is not generally optimal, but it is still the best linear filter.