ROS2 Path Planning and Maze Solving with Computer Vision

Mobile Robot Localization , Navigation and Motion Planning with Robot Operating System 2
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)
Requirements
Python basic Programming and Modules
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
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
Who this course is for
Engineers wanting to embark in the fields of Computer Vision, Artificial Intelligence and Robotics
Homepage

https://www.udemy.com/course/ros2-path-planning-and-maze-solving-with-computer-vision/

ROS2 Path Planning and Maze Solving with Computer Vision Fast Download
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