Advanced Kalman Filtering and Sensor Fusion

Advanced Kalman Filtering and Sensor Fusion. You need to learn know Sensor Fusion and Kalman Filtering! Learn how to use these concepts and implement them with a focus on autonomous vehicles in this course.

The Kalman filter is one of the greatest discoveries in the history of estimation and data fusion theory, and perhaps one of the greatest engineering discoveries in the twentieth century. It has enabled mankind to do and build many things which could not be possible otherwise. It has immediate application in control of complex dynamic systems such as cars, aircraft, ships and spacecraft.

These concepts are used extensively in engineering and manufacturing but they are also used in many other areas such as chemistry, biology, finance, economics, and so on.

Why focus on Sensor Fusion and Kalman Filtering

  • Data Fusion is an amazing tool that is used pretty much in every modern piece of technology that involves any kind of sensing, measurement or automation.
  • The Kalman Filter is one of the most widely used methods for data fusion. By understanding this process you will more easily understand more complicated methods.
  • Sensor fusion is one of the key uses of Kalman Filtering and is extensively used in unmanned vehicles and self-driving cars.
  • Evaluating and tuning the Kalman Filter for best performance can be a bit of a ‘black art’, we will give you tips and a structure so you know how to do this yourself.
  • So you don’t waste time trying to solve or debug problems that would be easily avoided with this knowledge! Become a Subject Matter Expert!

What you will learn:

You will learn the theory from ground up, so you can completely understand how it works and the implications things have on the end result. You will also learn practical implementation of the techniques, so you know how to put the theory into practice. In this course you will work with a C++ simulation that leads you through the implementation of various Kalman filtering methods for autonomous vehicles.

At the end of the course, the Capstone project is to implement the Unscented Kalman Filter and run it as it would be used in a real self-driving car or autonomous vehicle!

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