ROS2 Self Driving Car with Deep Learning and Computer Vision

🚗 ROS2 Self Driving Car with Deep Learning and Computer Vision Course for Beginners 🚀

Headline:

Transform your coding skills into a self-driving future! Learn how to create an autonomous car from scratch using ROS2, TensorFlow, and deep learning. Perfect for beginners eager to dive into the world of self-driving technology, computer vision, and robotics. 📚✨

Course Description:

This comprehensive course is designed to guide you through building a fully functional ROS2-based autonomous car using an RGB camera. You'll start with a blank slate and end up with a vehicle equipped with features like Lane Assist, Cruise Control, T-Junction Navigation, and Crossing Intersections. By leveraging deep learning and computer vision techniques, you'll gain hands-on experience in the exciting field of autonomous vehicles.

Self-Drive Features:

• 🛣️ Lane Assist: Keep your car on course with real-time lane detection and correction.
• 🚀 Cruise Control: Master speed regulation without human intervention.
• ⏱ T-Junction Navigation: Tackle complex intersection scenarios confidently.
• 🛫 Crossing Intersections: Safely navigate through crossroads with traffic.

Ros Package Essentials:

• Develop world models, edit Prius OSRF gazebo models, and work with ROS2 nodes, launch files, SDF textures, and plugins.
  • World Models Creation 🌏
  • Prius OSRF gazebo Model Editing 🚗
  • Nodes, Launch Files 🚀
  • SDF through Gazebo 🏗️
  • Textures and Plugins in SDF 🖼️

Software Part:

• Set up a perception pipeline with computer vision.
• Detect lanes, classify signs using CNNs, identify traffic lights with Haar Cascades, and track objects with optical flow.
• Implement rule-based control algorithms to make autonomous driving decisions.

Pre-Course Requirements:

Software Based:

• Ubuntu 20.04 (LTS)
• ROS2 - Foxy Fitzroy
• Python 3.6
• OpenCV 4.2
• Tensorflow 2.14

Skill Based:

• Basic ROS2 nodes communication skills
• Knowledge of basic computer vision principles
• Experience with launch files and gazebo model creation
• A motivated mind to tackle a challenging programming project 😁

Course Flow (Self-Driving Development Stage):

We'll kickstart our journey by getting the car up and running on Raspberry Pi using provided 3D models and off-the-shelf components. Then, we'll interface Raspberry Pi with motors and cameras for some serious programming work. By understanding the core concepts of autonomous driving, you'll see how this technology can revolutionize transportation and our environment.

We'll make a comparative study between two self-driving giants: Tesla and Waymo. And you'll get an insider look at the outcomes of this course through live simulations.

The autonomous car will be developed with four key features:

1. Lane Detection: Capture and process visual data to keep the car within its lane.
2. T-Junction Navigation: Safely handle T-junctions using computer vision algorithms.
3. Crossing Intersections: Ensure safe crossing through intersections with oncoming traffic.

Each feature will involve two main tasks: a) Detection: Gather the necessary data for each feature. b) Control: Devise an appropriate response based on the information collected.

Software Requirements:

• Ubuntu 20.4 and ROS2 Foxy
• Python 3.6
• OpenCV 4.2
• TensorFlow
• A motivated mind for a huge programming project! 🚀

Don't forget to check out the course Github repository or reach out if you have any questions before making your purchase. You can also learn from the code provided, even without buying the components—it's a fantastic learning opportunity either way! 💻🛠️