Self Driving and ROS - Learn by Doing! Odometry & Control

<div>Would you like to build a real Self-Driving Robot using ROS,&nbsp; the Robot Operating System?</div><div><br></div><div>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?</div><div><br></div><div>The philosophy of this course is the Learn by Doing and quoting the American writer and teacher Dale Carnegie</div><div><br></div><div>Learning is an Active Process. We learn by doing, only knowledge that is used sticks in your mind.</div><div><br></div><div>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.</div><div><br></div><div>Each section is composed of three parts:</div><div><ul><li><span style="font-size: 1rem;">Theoretical explanation of the concept and functionality</span></li><li><span style="font-size: 1rem;">Usage of the concept in a simple Practical example</span></li><li><span style="font-size: 1rem;">Application of the functionality in a real Robot</span></li></ul></div><div><br></div><div>There is more!</div><div><br></div><div>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!</div><div><br></div><div><span style="font-size: 1rem;">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.</span></div>

What you'll 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