Hadabot is a low-cost robot kit for students, software engineers, makers to learn ROS 2 and robotics in a hands-on manner. Our robot kits are (somewhat) easy to build, extensible, and customizable. The Hadabot software stack consists of an open source web browser-based coding environment to make the hacking experience frustration-free.

Come interactively learn basic ROS 2 concepts for FREE, no Hadabot kit purchase needed!!

Bayes Filter, Kalman Filter

I continue to implement the labs for Wolfram Burgard's MOOC on Probabilistic Robotics.

Most of the concepts in Probablistic Robotics involve the various ways to implement and use a Bayes Filter - which is a fancy term to describe a general algorithm to compute how certain a robot state is after 2 steps - a movement (ie robot opens the door) and a sensor reading (ie is the door open?).

One implementation of the Bayes Filter is a Particle Filter - here's the exercise we have for implementing a Particle Filter - which, for most, is easier to understand.

The Kalman Filter is a different story for most others (myself included).

I wrote up a 1-page summary and scaffolded a simple exercise to explain how KF implementation maps to the Bayes Filter steps.

I welcome you to check out the Hadabot Kalman Filter exercise. The exercises for Extended Kalman Filter will follow shortly. The exercise is implemented in Jupyter Notebooks (sorry, no ROS 2 for this exercise). A Hadabot robot kit is NOT required.

Screenshot of Kalman Filter exercise

As mentioned in the past, if you are a professor interested in adding ROS 2 to your curriculum (I create the content for you!), or a student actively learning robotics, please don't hesitate to reach out with questions, guidance, and/or suggestions.

Thanks and happy building / learning!
Jack "the Hadabot Maker"