
Kalman and Bayesian Filters in Python (2018) - r0f1
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
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rlayton2
This book is quite good. I was recently looking for a more production-ready
kalman filters, but the ones I found tend to be either abandoned, lacking lots
of features, or not mature code bases. Are there any robust libraries for
Kalman filters out there?

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WanderPanda
I think I've used the OpenCV Kalman filter implementation at some point. Maybe
that suits your needs

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gyre007
OpenCV only provides Linear Kalman Filter afaik. For nonlinear estimation you
need to use Extended or Unscented (this one is often used in Drone).

I wrote estimation library in Go [1] last year which implements a lot of
Kalman Filter alternatives and optimisations + smoothing

[https://github.com/milosgajdos83/go-
estimate](https://github.com/milosgajdos83/go-estimate)

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michelpp
There is also an excellent series (55 vids!) by Michel van Biezen on Kalman
filters:

[https://www.youtube.com/watch?v=CaCcOwJPytQ](https://www.youtube.com/watch?v=CaCcOwJPytQ)

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kacamak
Much easier to use them in Simulink, but it's good to see alternatives.

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msadowski
I had this book in my backlog for years. The fact that the book is interactive
would immensely help me understand the concept.

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Jabbermonkey
While Kalman and Bayesian Filters in Python is a superb resource, probably the
best out there, my recommendation for anyone new to the field would be to do
Sebastian Thrun's free Artificial Intelligence for Robotics course [1] as an
intro, then go through Labbe's work afterwards.

Thrun's course is more accessible and even more hands-on than Labbe's content.
As a bonus he also covers Particle Filters,PID control, Search and SLAM (which
cam out of Thrun's PhD thesis).

[1] [https://www.udacity.com/course/artificial-intelligence-
for-r...](https://www.udacity.com/course/artificial-intelligence-for-robotics
--cs373)

