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'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

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 expertsThe 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 functionalityUsage of the concept in a simple Practical exampleApplication of the functionality in a real RobotThere 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.

Overview

Section 1: Introduction

Lecture 1 Course Motivation

Lecture 2 The Self-Driving Program

Lecture 3 Course Presentation

Lecture 4 Meet your Teacher

Lecture 5 Get the Most out of the Course

Lecture 6 Course Material

Section 2: Setup

Lecture 7 Install Ubuntu on Virtual Machine

Lecture 8 Install Ubuntu on Dual Boot

Lecture 9 Install ROS

Lecture 10 Configure the Development Environment

Section 3: ROS Introduction

Lecture 11 Why a Robot Operating System?

Lecture 12 What is ROS

Lecture 13 Hardware Abstraction

Lecture 14 Low-Level Device Control

Lecture 15 Messaging between Process

Lecture 16 Package Management

Lecture 17 Architecture of a ROS Application

Lecture 18 Create and Activate a Workspace

Lecture 19 Simple Publisher

Lecture 20 Simple Publisher

Lecture 21 Simple Subscriber

Lecture 22 Simple Subscriber

Section 4: Locomotion

Lecture 23 Robot Locomotions

Lecture 24 Mobile Robots

Lecture 25 Friction Effects

Lecture 26 Robot Description

Lecture 27 URDF

Lecture 28 Create the URDF Model

Lecture 29 RViz

Lecture 30 Parameter Server

Lecture 31 Parameter Server

Lecture 32 Visualize the Robot

Lecture 33 Launch Files

Lecture 34 Visualize the Robot with Launch Files

Lecture 35 Gazebo

Lecture 36 Simulate the Robot

Lecture 37 Launch the Simulation

Section 5: Control

Lecture 38 ROS Control

Lecture 39 Control Types

Lecture 40 ROS Control with Gazebo

Lecture 41 YAML Configuration File

Lecture 42 YAML Configuration File

Lecture 43 Launch the Controller

Section 6: Kinematics

Lecture 44 Robot Kinematics

Lecture 45 Pose of a Mobile Robot

Lecture 46 Translation Vector

Lecture 47 Introduction to Turtlesim

Lecture 48 Translation Vector

Lecture 49 Translation Vector

Lecture 50 Rotation Matrix

Lecture 51 Rotation Matrix

Lecture 52 Rotation Matrix

Lecture 53 Transformation Matrix

Section 7: Differential Kinematics

Lecture 54 Differential Kinematics

Lecture 55 Velocity of a Mobile Robot

Lecture 56 Linear Velocity

Lecture 57 Angular Velocity

Lecture 58 Velocity in World Frame

Lecture 59 Differential Forward Kinematics

Lecture 60 Simple Speed Controller

Lecture 61 Simple Speed Controller

Lecture 62 Simple Speed Controller

Lecture 63 Teleoperating with Joystick

Lecture 64 Using the diff_drive_controller

Section 8: TF Library

Lecture 65 The TF Library

Lecture 66 Operations with Transformations

Lecture 67 Static and Dynamic Transformations

Lecture 68 Simple TF Static Broadcaster

Lecture 69 Simple TF Static Broadcaster

Lecture 70 ROS Timer

Lecture 71 ROS Timer

Lecture 72 ROS Timer

Lecture 73 Simple TF Broadcaster

Lecture 74 Simple TF Broadcaster

Lecture 75 ROS Services

Lecture 76 Service Server

Lecture 77 Service ServerService Client

Lecture 79 Service Client

Lecture 80 Simple TF Listener

Lecture 81 Simple TF Listener

Lecture 82 Angle Rapresentations

Lecture 83 Euler Angles

Lecture 84 Quaternion

Lecture 85 Euler to Quaternion

Lecture 86 Euler to Quaternion

Lecture 87 TF Tools

Section 9: Odometry

Lecture 88 Where is the Robot?

Lecture 89 The Local Localization Challenge

Lecture 90 Wheel Odometry

Lecture 91 Differential Inverse Kinematics

Lecture 92 Differential Inverse Kinematic

Lecture 93 Differential Inverse Kinematic

Lecture 94 Wheel Odometry - Position

Lecture 95 Wheel Odometry - Orientation

Lecture 96 Wheel Odometry

Lecture 97 Wheel Odometry

Lecture 98 Publish Odometry Message

Lecture 99 Publish Odometry Message

Lecture 100 Broadcast Odometry Transform

Lecture 101 Broadcast Odometry Transform

Section 10: Probability for Robotics

Lecture 102 Motivation

Lecture 103 Random Variables

Lecture 104 Conditional Probability

Lecture 105 Probability Distributions

Lecture 106 Gaussian Distributions

Lecture 107 Total Probability Theorem

Lecture 108 Bayes Rule

Lecture 109 Sensor Noise

Lecture 110 Adding Noise to Robot Motion

Lecture 111 Adding Noise to Robot Motion

Lecture 112 Odometry Comparison

Section 11: Sensor Fusion

Lecture 113 Advantages of having Multiple Sensors

Lecture 114 Gyroscope

Lecture 115 Accelerometer and IMU

Lecture 116 Simulate IMU Sensor

Lecture 117 Kalman Filter

Lecture 118 Filter Initialization

Lecture 119 Filter Initialization

Lecture 120 Measurement Update

Lecture 121 Measurement Update

Lecture 122 Measurement Update

Lecture 123 State Prediction

Lecture 124 State Prediction

Lecture 125 State Prediction

Lecture 126 Localization with Kalman Filter

Lecture 127 Extended Kalman Filter (EKF)

Lecture 128 IMU Republisher

Lecture 129 IMU Republisher

Lecture 130 Sensor Fusion with robot_localization

Section 12: Conclusions

Lecture 131 Recap

Lecture 132 What's Next?

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