ROS2 Self Driving Car with Deep Learning and Computer Vision

Price: 199.99$
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 SDFSoftware Part: Perception Pipeline setup Lane Detection with Computer Vision Techniques Sign Classification using (custom-built) CNNTraffic 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 3.6Opencv 4.2Tensorflow 2.14Skill 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 partsa) Detection: Gathering information required for that featureb) Control: Proposing appropriate response for the information received Software Requirements Ubuntu 20.4 and ROS2 Foxy Python 3.6Open CV 4.2Tensor Flow 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:) )



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