Learn how Kalman filters work and how to apply them to mobile robots using ROS.
One of the most common problems in robot navigation is to know where your robot is localized in the environment (known as robot localization). In this field, Kalman Filters are one of the most important tools that we can use.
With this course, you will understand the importance of Kalman Filters for robotics, and how they work. You will learn the theoretical meaning, but also the Python implementation. Finally, you will also apply the studied filters to mobile robots using ROS.
What You Will Learn
In this course you will learn:
- What is a Kalman Filter and why are required
- Different types of Kalman Filters and when to apply each one.
- Bayesian Filters
- One-dimensional Kalman Filters
- Multivariate Kalman Filters
- Unscendent Kalman Filters
- Extended Kalman Filters
- Particle Filters
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