Portraits of Kalman Filters

I’ve been reading the book Probabilistic Robotics by Thrun, Burgard, and Fox. It is good but a bit of a mathematical slog. I realized I needed to make notes, real notes that I would return to. Then I got a little carried away.

Chapter 2 was mainly a statistics refresher.

Chapter 3 was a bit more information dense, mainly about different forms of the Kalman filter. I touched on the basic Kalman filter with an applied mindset last fall:

However in Probabilistic Robotics, the authors compared the Kalman to the more basic Bayes filter. I decided comparing and contrasting the formulations would help me remember them. (Note if Bayes filter isn’t something that makes sense, maybe check out my post  A Narwhal’s Guide to Bayes’ Rule.) 


Whew! I hope this helps me remember how Kalmans go together. We’ve talked about the Kalman Filter on the podcast with Tony Rios in 43: A Lot of High-Falutin’ Math and ways to intuitively understand it in 233: Always the Wrong Way. Each time I get a little closer to understanding and application.

Well, that’s as far as I’ve gotten in Probabilistic Robotics. Peeking ahead, Chapter 4 is about “Nonparametric Filters” which I think means particle filters. I have no idea how to explain those other than knowing they are neat. If it makes sense, I’ll make more notes; I hope to draw a lot of itty-bitty point clouds.