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

Name: Self Driving and ROS - Learn by Doing! Odometry & Control
Rating: 4.7 (239 reviews)

Create a Self-Driving robot and learn about Robot Localization and Sensor Fusion using Kalman Filters

Highest Rated

Created byAntonio Brandi

Last updated 1/2026

English

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

Course content

13 sections • 139 lectures • 20h 20m total length

Course Motivation2:53
The Self-Driving Program3:29
Course Presentation6:16
Meet your Teacher1:45
[BONUS]: Boost your Robotics Software Developer Career2:10
Get the Most out of the Course3:49
Course Material1:02

Why a Robot Operating System?4:50
What is ROS3:24
Hardware Abstraction3:11
Low-Level Device Control1:40
Messaging between Process7:56
Package Management1:46
Architecture of a ROS Application2:48
Introduction to ROS
<LAB>Create and Activate a Workspace</LAB>11:07
<PY>Simple Publisher</PY>17:50
<C++>Simple Publisher</C++>17:38
<PY>Simple Subscriber</PY>11:56
<C++>Simple Subscriber</C++>12:21
Workspaces, Publishers, Subscribers

Robot Locomotions6:39
Mobile Robots4:53
Friction Effects9:54
Robot Description4:03
URDF5:28
<LAB>Create the URDF Model</LAB>23:01
RViz5:55
Parameter Server4:56
<LAB>Parameter Server</LAB>6:16
<LAB>Visualize the Robot</LAB>8:02
Launch Files4:26
<LAB>Visualize the Robot with Launch Files</LAB>8:48
Add a Can on top of your Robot
Gazebo5:03
show some world that is possible to create with gazebo AWS robomaker
<LAB>Simulate the Robot</LAB>17:13
<LAB>Launch the Simulation</LAB>13:49

The TF Library5:23
Operations with Transformations7:09
Static and Dynamic Transformations3:25
<PY>Simple TF Static Broadcaster</PY>16:54
<C++>Simple TF Static Broadcaster</C++>20:15
ROS Timer5:12
<PY>ROS Timer</PY>6:00
<C++>ROS Timer</C++>6:16
<PY>Simple TF Broadcaster</PY>12:38
<C++>Simple TF Broadcaster</C++>14:14
ROS Services5:48
<PY>Service Server</PY>14:49
<C++>Service Server</C++>17:11
<PY>Service Client</PY>12:18
<C++>Service Client</C++>13:56
<PY>Simple TF Listener</PY>19:22
<C++>Simple TF Listener</C++>19:57
Angle Rapresentations2:02
Euler Angles4:46
Quaternion4:29
<PY>Euler to Quaternion</PY>11:00
<C++>Euler to Quaternion</C++>10:50
<LAB>TF Tools</LAB>8:13

Where is the Robot?3:08
The Local Localization Challenge6:37
Wheel Odometry7:49
Differential Inverse Kinematics5:33
<PY>Differential Inverse Kinematic</PY>16:08
<C++>Differential Inverse Kinematic</C++>18:01
Wheel Odometry - Position3:17
Wheel Odometry - Orientation3:38
<PY>Wheel Odometry</PY>10:04
<C++>Wheel Odometry</C++>9:14
<PY>Publish Odometry Message</PY>14:22
<C++>Publish Odometry Message</C++>14:51
Draw the Robot's Trajectory
<PY>Broadcast Odometry Transform</PY>12:09
<C++>Broadcast Odometry Transform</C++>12:44

Requirements

Basic knowledge of Python or C++
Basic knowledge of Linux
No prior knowledge of ROS required
No prior knowledge of Robotics theory required
No hardware required. All the course can be followed also using only the PC

Description

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.

Who this course is for:

Self-Driving enthusiast
Makers and Hobbists keen on robotics
Software developers taht wants to learn ROS and Robotics
Students or Engineers that wants to learn how to buid a robot from scratch
Developers that already knows ROS and that want to use it in a real world application
Robotics Engineers that wants to develop skills in Autonomous Navigation
Beginner Python developers curious about Self-Driving
Beginner C++ developers curious about Self-Driving

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

What you'll learn

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Course content

Introduction7 lectures • 21min

Setup4 lectures • 16min

ROS Introduction12 lectures • 1hr 36min

Locomotion15 lectures • 2hr 8min

Control6 lectures • 50min

Kinematics10 lectures • 1hr 47min

Differential Kinematics11 lectures • 2hr 8min

TF Library23 lectures • 4hr 2min

Odometry14 lectures • 2hr 18min

Probability for Robotics11 lectures • 1hr 32min

Requirements

Description

Who this course is for: