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

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Antonio Brandi

19:23:12

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1. Introduction

1. Course Motivation.mp4

02:53

2. The Self-Driving Program.mp4

03:29

3. Course Presentation.mp4

06:16

4. Meet your Teacher.mp4

01:45

5. Get the Most out of the Course.mp4

03:49

6. Course Material.html

2. Setup

1. Install Ubuntu on Virtual Machine.html

2. Install Ubuntu on Dual Boot.html

3. Install ROS.mp4

03:53

4. Configure the Development Environment.mp4

06:43

3. ROS Introduction

1. Why a Robot Operating System.mp4

04:50

2. What is ROS.mp4

03:24

3. Hardware Abstraction.mp4

03:11

4. Low-Level Device Control.mp4

01:40

5. Messaging between Process.mp4

07:56

6. Package Management.mp4

01:46

7. Architecture of a ROS Application.mp4

02:48

8. LABCreate and Activate a WorkspaceLAB.mp4

11:07

9. PYSimple PublisherPY.mp4

17:50

10. C++Simple PublisherC++.mp4

17:38

11. PYSimple SubscriberPY.mp4

11:56

12. C++Simple SubscriberC++.mp4

12:21

4. Locomotion

1. Robot Locomotions.mp4

06:39

2. Mobile Robots.mp4

04:53

3.1 L27 Friction Effects.pdf

3. Friction Effects.mp4

09:54

4. Robot Description.mp4

04:03

5. URDF.mp4

05:28

6. LABCreate the URDF ModelLAB.mp4

23:01

7. RViz.mp4

05:55

8. Parameter Server.mp4

04:56

9. LABParameter ServerLAB.mp4

06:16

10. LABVisualize the RobotLAB.mp4

08:02

11. Launch Files.mp4

04:26

12. LABVisualize the Robot with Launch FilesLAB.mp4

08:48

13. Gazebo.mp4

05:03

14. LABSimulate the RobotLAB.mp4

17:13

15. LABLaunch the SimulationLAB.mp4

13:49

5. Control

1. ROS Control.mp4

09:42

2. Control Types.mp4

06:05

3. LABROS Control with GazeboLAB.mp4

08:29

4. YAML Configuration File.mp4

03:47

5. LABYAML Configuration FileLAB.mp4

07:56

6. LABLaunch the ControllerLAB.mp4

13:37

6. Kinematics

1. Robot Kinematics.mp4

03:52

2. Pose of a Mobile Robot.mp4

03:53

3. Translation Vector.mp4

04:46

4. LABIntroduction to TurtlesimLAB.mp4

13:24

5. PYTranslation VectorPY.mp4

21:46

6. C++Translation VectorC++.mp4

27:09

7.1 L52 Rotation Matrix.pdf

7. Rotation Matrix.mp4

08:14

8. PYRotation MatrixPY.mp4

10:10

9. C++Rotation MatrixC++.mp4

10:32

10. Transformation Matrix.mp4

03:39

7. Differential Kinematics

1. Differential Kinematics.mp4

01:36

2. Velocity of a Mobile Robot.mp4

03:17

3.1 L58 Linear Velocity.pdf

3. Linear Velocity.mp4

06:04

4.1 L59 Angular Velocity.pdf

4. Angular Velocity.mp4

05:28

5.1 L60 Velocity in World Frame.pdf

5. Velocity in World Frame.mp4

05:05

6.1 L61 Differential Forward Kinematics.pdf

6. Differential Forward Kinematics.mp4

04:08

7. Simple Speed Controller.mp4

02:03

8. PYSimple Speed ControllerPY.mp4

31:48

9. C++Simple Speed ControllerC++.mp4

35:16

10. LABTeleoperating with JoystickLAB.mp4

11:25

11. LABUsing the diff drive controllerLAB.mp4

22:14

8. TF Library

1. The TF Library.mp4

05:23

2. Operations with Transformations.mp4

07:09

3. Static and Dynamic Transformations.mp4

03:25

4. PYSimple TF Static BroadcasterPY.mp4

16:54

5. C++Simple TF Static BroadcasterC++.mp4

20:15

6. ROS Timer.mp4

05:12

7. PYROS TimerPY.mp4

06:00

8. C++ROS TimerC++.mp4

06:16

9. PYSimple TF BroadcasterPY.mp4

12:38

10. C++Simple TF BroadcasterC++.mp4

14:14

11. ROS Services.mp4

05:48

12. PYService ServerPY.mp4

14:49

13. C++Service ServerC++.mp4

17:11

14. PYService ClientPY.mp4

12:18

15. C++Service ClientC++.mp4

13:56

16. PYSimple TF ListenerPY.mp4

19:22

17. C++Simple TF ListenerC++.mp4

19:57

18. Angle Rapresentations.mp4

02:02

19. Euler Angles.mp4

04:46

20. Quaternion.mp4

04:29

21. PYEuler to QuaternionPY.mp4

11:00

22. C++Euler to QuaternionC++.mp4

10:50

23. LABTF ToolsLAB.mp4

08:13

9. Odometry

1. Where is the Robot.mp4

03:08

2. The Local Localization Challenge.mp4

06:37

3. Wheel Odometry.mp4

07:49

4.1 L93 Differential Inverse Kinematics.pdf

4. Differential Inverse Kinematics.mp4

05:33

5. PYDifferential Inverse KinematicPY.mp4

16:08

6. C++Differential Inverse KinematicC++.mp4

18:01

7.1 L96 Wheel Odometry Position.pdf

7. Wheel Odometry - Position.mp4

03:17

8.1 L97 Wheel Odometry Orientation.pdf

8. Wheel Odometry - Orientation.mp4

03:38

9. PYWheel OdometryPY.mp4

10:04

10. C++Wheel OdometryC++.mp4

09:14

11. PYPublish Odometry MessagePY.mp4

14:22

12. C++Publish Odometry MessageC++.mp4

14:51

13. PYBroadcast Odometry TransformPY.mp4

12:09

14. C++Broadcast Odometry TransformC++.mp4

12:44

10. Probability for Robotics

1. Motivation.mp4

07:09

2.1 L105 Random Variables.pdf

2. Random Variables.mp4

08:55

3.1 L106 Conditional Probability.pdf

3. Conditional Probability.mp4

07:18

4.1 L107 Probability Distributions.pdf

4. Probability Distributions.mp4

08:40

5. Gaussian Distributions.mp4

04:52

6.1 L109 Total Probability.pdf

6. Total Probability Theorem.mp4

05:44

7.1 L110 Bayes Rule.pdf

7. Bayes Rule.mp4

05:11

8. Sensor Noise.mp4

02:37

9. PYAdding Noise to Robot MotionPY.mp4

14:48

10. C++Adding Noise to Robot MotionC++.mp4

18:44

11. LABOdometry ComparisonLAB.mp4

08:05

11. Sensor Fusion

1. Advantages of having Multiple Sensors.mp4

06:28

2. Gyroscope.mp4

03:38

3. Accelerometer and IMU.mp4

03:30

4. LABSimulate IMU SensorIMU.mp4

12:23

5. Kalman Filter.mp4

06:28

6. PYFilter InitializationPY.mp4

14:18

7. C++Filter InitializationC++.mp4

19:30

8. Measurement Update.mp4

02:23

9. PYMeasurement UpdatePY.mp4

04:58

10. C++Measurement UpdateC++.mp4

06:02

11. State Prediction.mp4

02:33

12. PYState PredictionPY.mp4

12:55

13. C++State PredictionC++.mp4

11:48

14. LABLocalization with Kalman FilterLAB.mp4

06:51

15. Extended Kalman Filter (EKF).mp4

04:24

16. PYIMU RepublisherPY.mp4

04:47

17. C++IMU RepublisherC++.mp4

06:16

18. LABSensor Fusion with robot localizationLAB.mp4

18:44

12. Conclusions

1. Recap.mp4

02:34

2. Whats Next.mp4

01:51

Description

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

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

Who is this 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

More details

Description
Would you like to build a real Self-Driving Robot using ROS,Â 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 throught the learning of all the functionalities of ROS both from the theoretical and practical point of view.

Each section is composed od 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 progremming 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

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Focused display

Automotive Engineering

Antonio Brandi

Instructor's Courses

Hey, I'm Antonio Brandi and I'm glad you are here!I am a Robotics Engineer specialized in Autonomous Navigation for Robot applications with several years of experience working with ROS and mobile robots both for industrial and commercial applications.Actually I'm working with the brightest minds in the field of ROS and Robotics at Pal Robotics.Despite having an Engineering background, I'm a ROS self learner and I know how tough and demotivating it can be rushing through all the concepts and documentations. Furthermore, I genuinely think that the best way to learn something is to scratch your head and build something real that you can interact and play with.That's why my courses will handle both the required theoretical background and its implementation in the real world!Remember to have fun and experimenting while learning

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