Self Driving and ROS 2 - Learn by Doing! Perception & Vision

Created by Antonio Brandi
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 160 Lectures ( 29h 33m ) | Size: 23 GB
Physical AI with ROS 2: Self-Driving Robot in Python & C++. Master Computer Vision, OpenCV, Visual Odometry & SLAM

What you'll learn

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Create a Real Robot, powered by ROS 2
Mastering ROS2, the latest version of the Robot Operating System
Robotics Theory
Implement Computer Vision algorithms
Implement Visual Odometry algorithms
Simulate a Self-Driving robot in Gazebo
Create 3D Map of the environment using Point Clouds
Programming Arduino for Robotics Applications
Master RTAB Map
Implement Visual SLAM Algorithms

Requirements

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Programming Arduino for Robotics Applications
Basic knowledge of Linux
No prior knowledge of ROS or ROS 2 required
No prior knowledge of Robotics theory required
No hardware required. All the course can be followed also using only the PC

Description

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Would you like to give yourSelf-Driving Robot the sense of sight? Do you want to build a real robot that sees, understands, and moves through the world usingROS 2, the latest version of the Robot Operating System?
Would you like to step into the world ofPhysical AI and learn the theory and practice of Computer Vision, Stereo Vision, Point Clouds,Visual Odometry, andVisual SLAM from industry experts?
This is the fourth and final course of the Self-Driving and ROS 2 - Learn by Doing! series. Your robot can already move, map, localize, and navigate. Now it is time to teach it to see.
In this course, you will
Understand how a camera forms an image, from the Pinhole Camera model to lenses, distortion, and Camera Calibration
Process images with OpenCV (color spaces, filtering, masks, and contours) and build a Ball Follower robot
Simulate RGB and Stereo Cameras in Gazebo and turn Disparity Images into 3D Point Clouds
Filter Point Clouds with PCL and reconstruct the 3D environment around the robot
Estimate the robot's motion with Visual Odometry, using Feature Detectors (SIFT, FAST, ORB), Epipolar Geometry, and Optical Flow
Run Visual SLAM with RTAB-Map, including Loop Closure with Bag of Words
Master the ROS 2 tools for vision: Image Transport, Quality of Service (QoS), Message Filters, and Components
Build a real robot with a Raspberry Pi and run all the functionalities on hardware
The philosophy of this course is 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.
To help you master these concepts and use them in your projects and in your future job, I will guide you through every functionality of ROS 2, both from the theoretical and the practical point of view.
Each section is composed of three parts
Theoretical explanation of the concept and functionality
Usage of the concept in a simplePracticalexample
Application of the functionality in areal Robot
There is more!
All the programming lessons are developed in bothPython andC++. You can choose the language you are most familiar with, or become an expertRobotics Software Developer in both! The course supports all ROS 2 distributions, and you can follow it entirely insimulation.
By taking this course, you will gain a deep understanding of robot perception, the foundation of Physical AI, and open up opportunities in the exciting field of robotics and autonomous systems.

Who this course is for

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Self-Driving enthusiast
Makers and Hobbists keen on robotics
Software developers who want to learn ROS 2 and Robotics
Students or Engineers that want to learn how to buid a robot from scratch
Developers who already know ROS 2 and want to use it in a real-world application
ROS Developers who want to learn and migrate to ROS 2
Robotics Engineers who want to develop skills in Autonomous Navigation
Beginner Python developers curious about Self-Driving
Beginner C++ developers curious about Self-Driving

Homepage

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