About this video

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: https://github.com/PacktPublishing/Autonomous-Robots-Kalman-Filter

Publication date:
April 2020
Publisher
Packt
Duration
2 hours 3 minutes
ISBN
9781800565210

About the Author

  • 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.

    Browse publications by this author

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