This Course Contains ROS2 Based self-driving car through an RGB camera, created from scratch SelfDrive Features:- Lane Assist- CruiseControl- T-Junction Navigation- CrossingIntersectionsRos PackageWorld Models CreationPrius OSRF gazebo Model EditingNodes, Launch FilesSDF through GazeboTextures and Plugins in SDFSoftware Part: Perception Pipeline setupLane Detection with Computer Vision TechniquesSign Classification using (custom-built) CNNTraffic Light DetectionUsing Haar CascadesSign and Traffic Light Tracking using Optical FlowRule-Based Control AlgorithmsPre-Course RequirmentsSoftware BasedUbuntu 20.04 (LTS)ROS2 - Foxy FitzroyPython 3.6Opencv 4.2Tensorflow 2.14Skill BasedBasic ROS2 Nodes CommunicationLaunch FilesGazebo Model CreationMotivated 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 IntersectionEach feature development will comprise of two partsa) Detection: Gathering information required for that featureb) Control: Proposing appropriate response for the information receivedSoftware Requirements Ubuntu 20.4 and ROS2 Foxy Python 3.6OpenCV 4.2TensorFlowMotivated 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: ) )
