The idea of autonomous robots has been around for decades. Where did it start? Science fiction books? Star Wars? Now one knows for sure by now. However, it’s absolutely fascinating to think a person can create a machine that seems to have a mind of its own.
To a child, it resembles a miracle. An adult, however, knows that every tech secret can be explained – and in this Kalman filter tutorial, we will be doing just that.
The official definition of the Kalman filter states that its an algorithm that takes specific data collected through observation and then estimates unknown variables based on the results. Like a lot of prominent concepts, it’s named after one of its creators – a Hungarian-American engineer Rudolf Kalman.
Kalman filter is usually used in technologies related to navigation, guidance, and vehicle control. This also includes the field of robotics. Using the Kalman filter (with Python as you will learn in this Kalman filter course), you can plan and optimize the trajectory, as well as the motion of an autonomous robot. Interested? In that case, my Kalman filter tutorial is just what you need.
This short but informative Kalman filter with Python course consists of sixteen lessons, divided into three sections. At the end of the Kalman filter tutorial, you will be able to write your own code for a self-driving car simulation. We will be coding in Python, so if you have some basics in the language, you are already one step ahead!
We will start by reviewing the basics of filtering. You will learn why and how the Kalman filter is used typically, what types of limits do they have, and what kinds of problems you might encounter during the process. I will make sure to illustrate the concepts with practical examples to make sure you understand the way the Kalman filter works and how you can use it.
In the further sections, our Kalman filter tutorial will change its course a little bit: we will move to real-life examples and practice the implementation of filters. You will have multiple assignments to make sure you understand both theory and actual use of the Kalman filter with Python.
Who better to teach Kalman filter for beginners than a person who was that beginner himself and grew to be a working professional?
For over a year now, I have been working with Mercedes-Benz as an autonomous driving software engineer in San Francisco. Before that, I was a lead robotics engineer and a motion control software engineer. This is why my Python & Kalman filter tutorial is not just a bunch of dry facts – instead, it’s full of practical assignments and real-life situations.
A lot of people will tell you it’s extremely hard to learn Kalman filter for beginners. This false belief comes from some overcomplicated material you can find online and in various textbooks. However, there are different approaches. I have taken up a challenge to make Kalman filtering understandable and straightforward – come and see for yourself in this Kalman filter course!
Course consist of total 2h of content, in total.
I have a masters degree in Mechanical Engineering which I earned for my research in control system design for automotive applications. At a previous job, I was responsible for designing motion controllers and stabilization systems for military tank turrets.
I also previously wrote robotic software for a startup based out of Toronto Canada.
I currently write software for autonomous vehicles in California.