Autonomous Robots: Kalman Filter [Video]

More Information
  • Become proficient in using Kalman Filters
  • Solve real-world problems faced by self-driving cars or autonomous vehicles
  • Get an overview of the complete robotic software stack

In this course, you will learn not only how Kalman Filters work but also why they are needed. You will grips with writing the code to run the simulations designed to mimic a self-driving car. Don't worry if you don't have any experience in linear algebra or software; all the code in the course is written in Python, which is a very easy language to get up and running with, even if you're new to software programming.

This course provides simplified explanations of Kalman Filters. It also allows you to test your knowledge at the end of the course by working on simulators that the author has designed to cover a set of problems that any self-driving car can encounter. What's more? You will even get a working Kalman Filter code that you can deploy on a real robotic system.

All the code and supporting files for this course are available here:

  • Get started with applying Kalman Filter and toy implementation
  • Implement 1D and 2D depth fields in Kalman Filter
  • Understand the filter's essence, its meanings, and complex applications
Course Length 2 hours 3 minutes
ISBN 9781800565210
Date Of Publication 29 Apr 2020


Daniel Stang

Daniel Stang is a robotics software engineer who holds a master’s degree in mechanical engineering, which he earned for his research in control system design for automotive applications. In his first job out of school, he was responsible for designing motion controllers and stabilization systems for military tank turrets. Daniel has previously written robotic software for a startup based out of Toronto, Canada, and currently writes software for autonomous vehicles in California.