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Kalman-and-Bayesian-Filters-in-Python
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356 | 15,625 | |
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Ruby | Jupyter Notebook | |
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Kalman-and-Bayesian-Filters-in-Python
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The Kalman Filter
A fantastic interactive introduction to Kalman filters can be found on the following repo:
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...
It explains them from first principles and provides the intuitive rationale for them but doesn't shy away from the math when it feels the student should be ready for it.
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Kalman Filter Explained Simply
No thread on Kalman Filters is complete without a link to this excellent learning resource, a book written as a set of Jupyter notebooks:
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...
That book mentions alpha-beta filters as sort of a younger sibling to full-blown Kalman filters. I recently had need of something like this at work, and started doing a bunch of reading. Eventually I realized that alpha-beta filters (and the whole Kalman family) is very focused on predicting the near future, whereas what I really needed was just a way to smooth historical data.
So I started reading in that direction, came across "double exponential smoothing" which seemed perfect for my use-case, and as I went into it I realized... it's just the alpha-beta filter again, but now with different names for all the variables :(
I can't help feeling like this entire neighborhood of math rests on a few common fundamental theories, but because different disciplines arrived at the same systems via different approaches, they end up sounding a little different and the commonality is obscured. Something about power series, Euler's number, gradient descent, filters, feedback systems, general system theory... it feels to me like there's a relatively small kernel of intuitive understanding at the heart of all that stuff, which could end up making glorious sense of a lot of mathematics if I could only grasp it.
Somebody help me out, here!
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Recommendations for undergrad to learn optimal state estimation
This provides an excellent intro that jumps right into code. https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
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A Non-Mathematical Introduction to Kalman Filters for Programmers
If you know a bit of Python and you find it sometimes tough to grind through a textbook, take a look here:
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...
Interactive examples programmed in Jupyter notebooks.
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Kalman Filter for Beginners
Thank you, very good resource! Timely too, as I am revising this topic.
My work is mostly in python. I found this interactive book using Jupyter that explains Kalman filters from first principles.
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...
- Starting out with Kalman Filter.
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kalman filter & c++
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python And on robotics in general
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Doing SLAM with laptop sensors
Seems like visual inertial SLAM is what you're after, anyway. There will be a bunch of results if you put that into google scholar, but you will likely have to fuse in the encoder data yourself as that's not as common a use case. For that you're likely looking at some form of Kalman filter.
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How a Kalman filter works, in pictures
This article is fantastic.
For those who have some familiarity with Python, I found this to be a great resource for Kalman Filtering: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...
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Resources on Kalman filter predictors?
Here is a detailed introduction/tutorial: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
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