Self Driving and ROS - Learn by Doing! Odometry & Control
Create a Self-Driving robot and learn about Robot Localization and Sensor Fusion using Kalman Filters
What you will learn
Create a Real Self-Driving Robot
Mastering ROS, the Robot Operating System
Implement Sensor Fusion algorithms
Simulate a Self-Driving robot in Gazebo
Develop a Controller
Odometry and Localization
Kalman Filters and Extended Kalman Filter
Probability Theory
Differential Kinematics
Create a Digital Twin of a Self-Driving Robot
Master the TF library
Why take this course?
Would you like to build a real Self-Driving Robot using ROS, the Robot Operating System?
Would you like to get started with Autonomous Navigation of Robot and dive into the theoretical and practical aspects of Odometry and Localization from industry experts?
The philosophy of this course is the Learn by Doing and quoting the American writer and teacher Dale Carnegie
Learning is an Active Process. We learn by doing, only knowledge that is used sticks in your mind.
In order for you to master the concepts covered in this course and use them in your projects and also in your future job, I will guide you through the learning of all the functionalities of ROS both from the theoretical and practical point of view.
Each section is composed of three parts:
Theoretical explanation of the concept and functionality
Usage of the concept in a simple Practical example
Application of the functionality in a real Robot
There is more!
All the programming lessons are developed both using Python and C++ . This means that you can choose the language you are most familiar with or become an expert Robotics Software Developer in both programming languages!
By taking this course, you will gain a deeper understanding of self-driving robots and ROS, which will open up opportunities for you in the exciting field of robotics.