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ROS2 Self Driving Car with Deep Learning and Computer Vision

Autonomous Car using TensorFlow and Neural Networks for Beginners

     
  • 4.2
  •  |
  • Reviews ( 100 )
₹599

This Course Includes

  • iconudemy
  • icon4.2 (100 reviews )
  • icon9h 37m
  • iconenglish
  • iconOnline - Self Paced
  • iconprofessional certificate
  • iconUdemy

About ROS2 Self Driving Car with Deep Learning and Computer Vision

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 :) )

What You Will 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..