Description
ROS2 Self-Driving Car with Deep Learning and Computer Vision Course. This course covers the ROS2 based self-driving car using an RGB camera built from scratch.
Features of Self Drive:
- – Management Assistant
- Cruise control
- – T-junction navigation
- Crossing intersections
Rose package
- Creating global models
- Editing the Prius OSRF pavilion model
- Start nodes and files
- SDF via Gazebo
- Textures and plugins in SDF
Software area:
- Starting the perception pipeline
- Line detection using computer vision techniques
- Character classification using CNN (custom built).
- Traffic light detection using the Har 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
- opencv4.2
- Tensorflow 2.14
2. Competency-based
- Basic communication of ROS2 nodes
- Basic resume knowledge
- Start the files
- Building a pavilion 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 lecturers. After that, we will connect the Raspberry Pi with motors and camera to start serious programming. Then we will understand the concept of self-driving and how it will change 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 will present our offer to you by talking to you directly in the simulation so you can see the course results for yourself. Our self-driving car essentially consists of four main features.
- Lane assistant
- Cruise control
- T-junction navigation
- cross the intersection
Each feature development consists of two parts
1. Diagnosis: Collecting the information required for this function
2. Control: Proposing an appropriate response to the information received
Required software
- Ubuntu 20.4 and FoxyROS2
- Python 3.6
- OpenCV4.2
- TensorFlow
- Motivated mind for a big programming project
What you will learn in the course “ROS2 Self Driving Car with Deep Learning and Computer Vision”
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Build your own self-driving car in simulation (ROS2).
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Training to develop 4 essential features of self-driving (lane-to-lane assistance, cruise control, T-junction navigation, intersections)
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Computer vision techniques e.g. (detection, localization, tracking)
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Deep insight into customized neural networks (CNN)
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(NEW!!!) Create a satellite navigation system (e.g. GPS) that helps the SDC navigate autonomously to any desired destination.
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Learn how to use the functionality of other repositories for your needs with a practical example.
This course is suitable for people who
- Self-driving car enthusiasts who want to build their own car
- Engineers who want to work in computer vision, artificial intelligence and robotics.
Course Specifications ROS2 Self-Driving Car with Deep Learning and Computer Vision
- Editor: Udemy
- Lecturer: Muhammad Luqman
- Education level: intermediate to advanced
- Training duration: 11 hours and 2 minutes
- Number of courses: 97
Headlines for the course “ROS2 Self Driving Car with Deep Learning and Computer Vision” on 12.2022
Prerequisites for the course “ROS2 Self Driving Car with Deep Learning and Computer Vision”.
- Basic Python programming and modules
- Start ROS2 base node and file processing
- Gazebo models communication with ROS
- Basic Opencv processing
Course pictures
