5 ms·

Kalman and Bayesian Filters in Python (2018)

234 points by r0f1 8y ago

kacamak 8y ago
Much easier to use them in Simulink, but it's good to see alternatives.
msadowski 8y ago
I had this book in my backlog for years. The fact that the book is interactive would immensely help me understand the concept.
- Jabbermonkey 8y ago
  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-robotics--cs373 https://www.udacity.com/course/artificial-intelligence-for-r...
rlayton2 8y ago
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?
- WanderPanda 8y ago
  I think I've used the OpenCV Kalman filter implementation at some point. Maybe that suits your needs
  - gyre007 8y ago
    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
  - deleted 8y ago
    [deleted]
- msadowski 8y ago
  The one quite often used in Robotics + ROS combination is robot_localization (https://github.com/cra-ros-pkg/robot_localization https://github.com/cra-ros-pkg/robot_localization). The library provides implementation for ekf and ukf and in my experience it's quite robust.
- zmk_ 8y ago
  SsfPack [1] is a great implementation if you are willing to suffer through Ox. [1] http://www.ssfpack.com/ http://www.ssfpack.com/
  - em500 8y ago
    Most of SsfPack is now implemented in python statsmodels: https://www.statsmodels.org/stable/statespace.html https://www.statsmodels.org/stable/statespace.html
    - zmk_ 7y ago
      Thanks for that! I was not aware of this. SsfPack will still be faster though and be less memory hungry than Python. On the brief look I had, it also seems that the nonlinear/non-Gaussian simulation methods are not implemented.
- boltzmannbrain 8y ago
  GTSAM [1] packs a lot of functionality for robotics projects, including Kalman filters [2]. A product of GTech (Frank Dellaert), a pro is there are many cool projects built on top of it [3], in C++ and Python. A con is GTSAM is not the cleanest, well-architected codebase (although far better than most products of academic labs). [1] https://bitbucket.org/gtborg/gtsam https://bitbucket.org/gtborg/gtsam [2] https://github.com/borglab/gtsam/tree/develop/examples#kalman-filter-exampleshttps://github.com/borglab/gtsam/tree/develop/examples#kalman-filter-examples https://github.com/borglab/gtsam/tree/develop/examples#kalma... [3] e.g., https://github.com/gtrll/piper https://github.com/gtrll/piper and https://github.com/gtrll/gpmp2 https://github.com/gtrll/gpmp2
michelpp 8y ago
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

234 points by r0f1 8y ago

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

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

Jabbermonkey 8y ago
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-robotics--cs373 https://www.udacity.com/course/artificial-intelligence-for-r...

rlayton2 8y ago

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?

WanderPanda 8y ago
I think I've used the OpenCV Kalman filter implementation at some point. Maybe that suits your needs
- gyre007 8y ago
  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
- deleted 8y ago
  [deleted]
msadowski 8y ago
The one quite often used in Robotics + ROS combination is robot_localization (https://github.com/cra-ros-pkg/robot_localization https://github.com/cra-ros-pkg/robot_localization). The library provides implementation for ekf and ukf and in my experience it's quite robust.
zmk_ 8y ago
SsfPack [1] is a great implementation if you are willing to suffer through Ox. [1] http://www.ssfpack.com/ http://www.ssfpack.com/
- em500 8y ago
  Most of SsfPack is now implemented in python statsmodels: https://www.statsmodels.org/stable/statespace.html https://www.statsmodels.org/stable/statespace.html
  - zmk_ 7y ago
    Thanks for that! I was not aware of this. SsfPack will still be faster though and be less memory hungry than Python. On the brief look I had, it also seems that the nonlinear/non-Gaussian simulation methods are not implemented.
boltzmannbrain 8y ago
GTSAM [1] packs a lot of functionality for robotics projects, including Kalman filters [2]. A product of GTech (Frank Dellaert), a pro is there are many cool projects built on top of it [3], in C++ and Python. A con is GTSAM is not the cleanest, well-architected codebase (although far better than most products of academic labs). [1] https://bitbucket.org/gtborg/gtsam https://bitbucket.org/gtborg/gtsam [2] https://github.com/borglab/gtsam/tree/develop/examples#kalman-filter-exampleshttps://github.com/borglab/gtsam/tree/develop/examples#kalman-filter-examples https://github.com/borglab/gtsam/tree/develop/examples#kalma... [3] e.g., https://github.com/gtrll/piper https://github.com/gtrll/piper and https://github.com/gtrll/gpmp2 https://github.com/gtrll/gpmp2

michelpp 8y ago

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