Kalman-and-Bayesian-Filters-in-Python
PythonRobotics
Kalman-and-Bayesian-Filters-in-Python | PythonRobotics | |
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32 | 10 | |
15,859 | 21,815 | |
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0.0 | 8.8 | |
3 months ago | 7 days ago | |
Jupyter Notebook | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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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.
- Looking for a study partner to learn kalman filter
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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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want to learn kalman filter
Try this book
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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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Do you use particle/Kalman filters at work?
- Kalman and Bayesian Filters in Python
PythonRobotics
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Python Alternative to MATLAB's UAV Toolbox?
I usually start here: https://github.com/AtsushiSakai/PythonRobotics , but I'm not sure how much is extended to 3D off the top of my head.
- kalman filter & c++
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Robot path planning
Code is on github. You could try to start with the main method here: https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning/AStar/a_star.py
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slam using rplidar A1 in python only
Also another good source of material https://atsushisakai.github.io/PythonRobotics/
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probabilistic robotics as ros packages
Look into the following source. Its not implemented in ROS but there are examples of implementation of Robotics algorithms in python. From there it should be easy to implement them in ROS after you understand how ROS work. https://atsushisakai.github.io/PythonRobotics/
- A Robotics Roadmap to get you started
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I am creating a fast, header-only, C++ library for control algorithms
This was partly inspired by PythonRobotics (which I love!). However, I don't want this repo to become a huge collection of unorganized algorithms. I want the focus to be on quality over quantity. At the same time, I don't want this repo to be only "educational". While I want to include as many examples as possible for educational (and documentation) purpose, I want this library to be fast so that it can be used in practical applications.
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i dont know what to do next with ros
check out python robotics https://github.com/AtsushiSakai/PythonRobotics
- Comprehensive Simulation Library for Robotics
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LQR controller on a v-rep robot by Matlab
https://github.com/AtsushiSakai/PythonRobotics#linearquadratic-regulator-lqr-speed-and-steering-control
What are some alternatives?
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rospy - ROS communications-related packages, including core client libraries (roscpp, rospy, roslisp) and graph introspection tools (rostopic, rosnode, rosservice, rosparam).
clojure-style-guide - A community coding style guide for the Clojure programming language
roslibpy - Python ROS Bridge library
git-internals-pdf - PDF on Git Internals
mini-cheetah-tmotor-python-can - Python Motor Driver for Mini-Cheetah based Actuators: T-Motor AK80-6/AK80-9 using SocketCAN Interface
kalmanpy - Implementation of Kalman Filter in Python
rtabmap - RTAB-Map library and standalone application
react-bits - ✨ React patterns, techniques, tips and tricks ✨
PYQT-RVIZ - The following GUI is performed in PYQT4 for the teleoperation and control of a simulated differential mobile robot in RVIZ using ROS.
elm-architecture-tutorial - How to create modular Elm code that scales nicely with your app
dwm1001-examples - Simple C examples for Decawave DWM1001 hardware