Download Udemy – ROS2 Self Driving Car with Deep Learning and Computer Vision 2022-7

Description

ROS2 Self Driving Car with Deep Learning and Computer Vision course. This course covers the ROS2-based self-driving car through an RGB camera created from scratch.

Features of Self Drive:

• – Line assistant
• – Cruise control
• – T-Junction navigation
• – Crossing intersections

Ros package

• Creating global models
• Prius OSRF gazebo model edit
• Launch nodes, files
• SDF via Gazebo
• Textures and plugins in SDF

Software section:

• Launching the perception pipeline
• Line detection with computer vision techniques
• Sign classification using CNN (custom built).
• Detection of traffic lights using Harr waterfall
• Tracking signs and traffic lights using optical flow
• Law-based control algorithms

Pre-course requirements

1. Software-based

• Ubuntu 20.04 (LTS)
• ROS2 – Foxy Fitzroy
• Python 3.6
• Opencv 4.2
• Tensorflow 2.14

2. Skill-based

• Basic communication of ROS2 Nodes
• Basic CV knowledge
• Launch the files
• Construction of Gazebo model
• Motivated mind 🙂

We quickly set up our car on the Raspberry Pi using 3D models (provided in the repository) and car parts purchased from the links provided by the instructors. After that, we will connect Raspberry Pi with Motors and Camera to start serious programming. Then by understanding the concept of self-driving and how it will transform our near future in the field of transportation and environment. Then we will make a comparison between two SD giants (Tesla and Waymo). After that, we make our offer by talking directly to you inside the simulation so you can see the course results for yourself. Primarily, our self-driving car consists of four key features.

• Lane Assis
• Cruise control
• T-Junction Navigation
• Crossing the intersection

Each feature development will consist of two parts

1. Diagnosis: Collect information required for that feature

2. Control: Proposing a suitable answer for the received information

Required software

• Ubuntu 20.4 and Foxy ROS2
• Python 3.6
• OpenCV 4.2
• TensorFlow
• Motivated mind for a big programming project

What you will learn in the ROS2 Self Driving Car with Deep Learning and Computer Vision course

• Build your own self-driving car in simulation (ROS2).

• Training on the development of 4 essential features of self-drive (assistant between lanes, cruise control, T-Junc navigation, cross intersections)

• ComputerVision techniques eg (detection, localization, tracking)

• Deep Dive with Custom Built Neural Networks (CNN)

• (NEW!!!) Create a satellite navigation system (i.e. GPS) that helps the SDC navigate independently to any desired destination.

• Through a practical example, learn how to use the functionality provided by other repositories for your needs.

This course is suitable for people who

• Self-driving car enthusiasts who are looking to build one of their own
• Engineers who want to start working in the fields of computer vision, artificial intelligence and robotics.

Course specifications ROS2 Self Driving Car with Deep Learning and Computer Vision

• Publisher: Yudmi
• teacher: Muhammad Luqman
• Training level: intermediate to advanced
• Training duration: 11 hours and 2 minutes
• Number of courses: 97

Headlines of the ROS2 Self Driving Car with Deep Learning and Computer Vision course on 12/2022

Prerequisites of the ROS2 Self Driving Car with Deep Learning and Computer Vision course

• Python Basic Programming and Modules
• ROS2 Basic Nodes and Launch Files Processing
• Gazebo Models Communication with ROS
• Basic Opencv Processing

Course images

Sample video of the course

Volume

9.8 GB