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Comment by aethertap | Hacker News Reader

I spent quite a bit of time working through the Kalman filter content in Sebastian Thrun's book "Probabilistic Robotics"[1] a while back. I ended up making some notes [2] of the process that might be of interest to others if you're trying to get a grasp of everything that's going on with that process. One other person on the Internet that I know of thought they were useful, so I'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 wanted to know how it all worked before starting to make a curriculum). The book is really good if you're wanting to make robots that can navigate uncertain environments.

Edit: I wish I'd had access to this article when I was going through that process. This is really well done.

1. http://amzn.com/0262201623

2. https://github.com/aethertap/probabilistic-robotics

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There is also a class at Stanford that builds up all the theory of the Kalman filter, starting with elementary probability [1]. The slides are all posted, and while they wouldn't be great to learn the material from, they're an excellent reference (and go into more depth on multivariate Gaussians and estimation theory than Probabilistic Robotics).

1. http://engr207b.stanford.edu/

Weird that Sebastian Thrun would come up again - he's on the front page stepping down as Udacity CEO right now [1], too, and I'd never heard of the guy before.

1 https://news.ycombinator.com/item?id=11562468

That's because he has had tremendous impacts on computer science and robotics for many years now as a pioneering professor at CMU and Stanford. He was a leading force in the DARPA grand challenge which showcased self driving car technologies long before it was cool. He led the self driving car project at Google-X, developed street view, and cofounded Udacity.

It's not weird; the guy is brilliant and has had many large impacts to the community as a whole.

In that case it's weird that I wasn't familiar with the name.

I suggest building one with any Matrix libraries you have available. It is a great exercise (if sometimes frustrating). There is nothing like covariance matrices blowing up in your face to teach you the machinations of the Kalman Filter.

Also a great exercise in scientific computing.

I was wondering who are you and why are you doing this? Are you doing this profit? Doesn't seem like it. You seem to be a great guy! Keep up the good work. World needs more people like you.

Thanks for the kind words! I'm not doing it for profit, just the desire to share something that took an unexpected level of effort for me to sort out.

Eventually I plan to open source the robotics curriculum that spawned this project, along with some other stuff related to starting a new school (something I'm working on with a team that was originally inspired by the XQ Superschool competition [1]). I want to run it through its paces with some real children first to make sure it's not completely off base though.

1. http://xqsuperschool.org/

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