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

Install Ubuntu on Virtual Machine

Install Ubuntu on Dual Boot

Install ROS

Configure the Development Environment

Why a Robot Operating System?

What is ROS

Hardware Abstraction

Low-Level Device Control

Messaging between Process

Package Management

Architecture of a ROS Application

Introduction to ROS

<LAB>Create and Activate a Workspace</LAB>

<PY>Simple Publisher</PY>

<C++>Simple Publisher</C++>

<PY>Simple Subscriber</PY>

<C++>Simple Subscriber</C++>

Workspaces, Publishers, Subscribers

Robot Locomotions

Mobile Robots

Friction Effects

Robot Description

URDF

<LAB>Create the URDF Model</LAB>

RViz

Parameter Server

<LAB>Parameter Server</LAB>

<LAB>Visualize the Robot</LAB>

Launch Files

<LAB>Visualize the Robot with Launch Files</LAB>

Add a Can on top of your Robot

Gazebo

<LAB>Simulate the Robot</LAB>

<LAB>Launch the Simulation</LAB>

ROS Control

Control Types

<LAB>ROS Control with Gazebo</LAB>

YAML Configuration File

<LAB>YAML Configuration File</LAB>

<LAB>Launch the Controller</LAB>

Robot Kinematics

Pose of a Mobile Robot

Translation Vector

<LAB>Introduction to Turtlesim</LAB>

<PY>Translation Vector</PY>

<C++>Translation Vector</C++>

Rotation Matrix

<PY>Rotation Matrix</PY>

<C++>Rotation Matrix</C++>

Transformation Matrix

Differential Kinematics

Velocity of a Mobile Robot

Linear Velocity

Angular Velocity

Velocity in World Frame

Differential Forward Kinematics

Simple Speed Controller

<PY>Simple Speed Controller</PY>

<C++>Simple Speed Controller</C++>

<LAB>Teleoperating with Joystick</LAB>

<LAB>Using the diff_drive_controller</LAB>

The TF Library

Operations with Transformations

Static and Dynamic Transformations

<PY>Simple TF Static Broadcaster</PY>

<C++>Simple TF Static Broadcaster</C++>

ROS Timer

<PY>ROS Timer</PY>

<C++>ROS Timer</C++>

<PY>Simple TF Broadcaster</PY>

<C++>Simple TF Broadcaster</C++>

ROS Services

<PY>Service Server</PY>

<C++>Service Server</C++>

<PY>Service Client</PY>

<C++>Service Client</C++>

<PY>Simple TF Listener</PY>

<C++>Simple TF Listener</C++>

Angle Rapresentations

Euler Angles

Quaternion

<PY>Euler to Quaternion</PY>

<C++>Euler to Quaternion</C++>

<LAB>TF Tools</LAB>

Where is the Robot?

The Local Localization Challenge

Wheel Odometry

Differential Inverse Kinematics

<PY>Differential Inverse Kinematic</PY>

<C++>Differential Inverse Kinematic</C++>

Wheel Odometry - Position

Wheel Odometry - Orientation

<PY>Wheel Odometry</PY>

<C++>Wheel Odometry</C++>

<PY>Publish Odometry Message</PY>

<C++>Publish Odometry Message</C++>

Draw the Robot's Trajectory

<PY>Broadcast Odometry Transform</PY>

<C++>Broadcast Odometry Transform</C++>

Motivation

Random Variables

Conditional Probability

Probability Distributions

Gaussian Distributions

Total Probability Theorem

Bayes Rule

Sensor Noise

<PY>Adding Noise to Robot Motion</PY>

<C++>Adding Noise to Robot Motion</C++>

<LAB>Odometry Comparison</LAB>

Advantages of having Multiple Sensors

Gyroscope

Accelerometer and IMU

<LAB>Simulate IMU Sensor</IMU>

Kalman Filter

<PY>Filter Initialization</PY>

<C++>Filter Initialization</C++>

Measurement Update

<PY>Measurement Update</PY>

<C++>Measurement Update<C++>

State Prediction

<PY>State Prediction</PY>

<C++>State Prediction</C++>

<LAB>Localization with Kalman Filter</LAB>

Extended Kalman Filter (EKF)

<PY>IMU Republisher</PY>

<C++>IMU Republisher</C++>

<LAB>Sensor Fusion with robot_localization</LAB>

Mechanics and Electronics

What you are going to need

<HWLAB>Assemble the Robot - Part 1</HWLAB>

<HWLAB>Assemble the Robot - Part 2</HWLAB>

<HWLAB>Assemble the Robot - Part 3</HWLAB>

Recap

What's Next?

[BONUS]: Continue Learning