I spent quite a bit of time working through the Kalman filter content in Sebastian Thrun&#x27;s book &quot;Probabilistic Robotics&quot;[1] a while back. I ended up making some notes [2] of the process that might be of interest to others if you&#x27;re trying to get a grasp of everything that&#x27;s going on with that process. One other person on the Internet that I know of thought they were useful, so I&#x27;ll post the link here. This was part of a project I was working on to build a K-8 robotics curriculum (no, not teaching the kids Kalman filters, but <i>I</i> wanted to know how it all worked before starting to make a curriculum). The book is really good if you&#x27;re wanting to make robots that can navigate uncertain environments.<p>Edit: I wish I&#x27;d had access to this article when I was going through that process. This is really well done.<p>1. <a href="http:&#x2F;&#x2F;amzn.com&#x2F;0262201623" rel="nofollow">http:&#x2F;&#x2F;amzn.com&#x2F;0262201623</a><p>2. <a href="https:&#x2F;&#x2F;github.com&#x2F;aethertap&#x2F;probabilistic-robotics" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;aethertap&#x2F;probabilistic-robotics</a>