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Keep shoppingROS2 Self Driving Car with Deep Learning and Computer Vision
1,310 students
ROS2 Self Driving Car with Deep Learning and Computer Vision
Autonomous Car using TensorFlow and Neural Networks for Beginners
Created byMuhammad Luqman, Haider Najeeb
Last updated 7/2024
English
What you'll learn
- Build your own Self Driving Car in Simulation (ROS2)
- Learn to develop 4 Essential Self Drive features (Lane Assist, Cruise Control, Nav. T-Junc, Cross Intersections)
- Master ComputerVision techniques e.g. (Detection, Localization, Tracking)
- Deep Dive with Custom-built Neural Networks (CNN's)
- ( NEW!!! ) Develop a Satellite Navigation System (i.e GPS ) that helps the SDC navigate to any desired destination autonomously.
- Learn how to utilize functionality provided by other repos for your needs through a Practical example.
Explore related topics
Course content
10 sections • 92 lectures • 9h 37m total length
Requirements
- Python basic Programming and Modules
- ROS2 Basic Nodes and Launch Files Processing
- Gazebo Models Communication with ROS
- Basic Opencv Processing
Description
This Course Contains ROS2 Based self-driving car through an RGB camera, created from scratch
Self Drive Features:
- Lane Assist
- Cruise Control
- T-Junction Navigation
- Crossing Intersections
Ros Package
World Models Creation
Prius OSRF gazebo Model Editing
Nodes, Launch Files
SDF through Gazebo
Textures and Plugins in SDF
Software Part :
Perception Pipeline setup
Lane Detection with Computer Vision Techniques
Sign Classification using (custom-built) CNN
Traffic Light Detection Using Haar Cascades
Sign and Traffic Light Tracking using Optical Flow
Rule-Based Control Algorithms
Pre-Course Requirments
Software Based
Ubuntu 20.04 (LTS)
ROS2 - Foxy Fitzroy
Python 3.6
Opencv 4.2
Tensorflow 2.14
Skill Based
Basic ROS2 Nodes Communication
Basic CV knowledge
Launch Files
Gazebo Model Creation
Motivated mind :)
Course Flow (Self-Driving [Development Stage])
We will quickly get our car running on Raspberry Pi by utilizing 3D models ( provided in the repository) and car parts bought from links provided by instructors. After that, we will interface raspberry Pi with Motors and the camera to get started with Serious programming.
Then by understanding the concept of self-drive and how it will transform our near future in the field of transportation and the environment. Then we will perform a comparison between two SD Giants (Tesla & Waymo) ;). After that, we will put forward our proposal by directly talking you inside the simulation so that you can witness course outcomes yourself.
Primarily our Self Driving car will be composed of four key features.
1) Lane Assist 2) Cruise Control
3) Navigating T-Junction 4) Crossing Intersection
Each feature development will comprise of two parts
a) Detection: Gathering information required for that feature
b) Control: Proposing appropriate response for the information received
Software Requirements
Ubuntu 20.4 and ROS2 Foxy
Python 3.6
OpenCV 4.2
TensorFlow
Motivated mind for a huge programming Project
- Before buying take a look into this course Github repository or message( if you do not want to buy get the code at least and learn from it :) )
Who this course is for:
- Self Driving Cars Enthusiasts looking to build one of their own
- Engineers wanting to embark in the fields of Computer Vision, Artificial Intelligence and Robotics