Autoware.Auto Course Notes
Course Overview
This repository will contain the notes I took during my course on Autoware.auto, a ROS-based software stack for autonomous vehicle development, the biggest free software standard for this purpose.
This course uses the open source robotics framework ROS 2 and the Autoware.Auto algorithms, which are covered through the 14 lectures of it, which show state-of-the-art techniques to combine hardware, software, algorithms, methodologies, tools and data to build useful applications in the autonomous system context. It is oriented to students with previous experience in related fields and knowledges of C++, testing, robotics frameworks and system integration.
The learning platform are the student's personal computers with ade-cli. Each lecture will be provided by YouTube and have an associated .md file here that will be followed by the lecturer to record the videos. They will also use ade-cli. A new lecture will be uploaded weekly starting from the Monday 11th of May 2020. Everything will be linked and divided by lectures in the Apex.AI website. The YouTube playlist with all the videos can be accessed from the following link:
Each folder in this repository contains the notes (in .md files) of one of the lectures (listed in the Collaborators section of this README) and its name will start by the correspondent lecture number. There is one additional folder, 0_additional_information, which contains relevant related information I gathered both before and during the course.
Lectures and Collaborators
This course is held by Apex.AI and TheConstruct organized the classes and was in charge of the logistics. Moreover, the following entities were in charge of the course parts listed below:
- Lecture 01: Development Environment: Apex.AI and StreetScooter
- Lecture 02: ROS 2 101: Open Source Robotics Foundation
- Lecture 03: ROS 2 Tooling: Open Source Robotics Foundation
- Lecture 04: Platform (ECU, RTOS and DDS): Kalray and ADLink
- Lecture 05: Autonomous Driving Stacks and Autoware Use Cases: Virtual Vehicle Research Center (TU Graz)
- Lecture 06: Autoware 101: Autoware Foundation
- Lecture 07: Object Perception LiDAR: Apex.AI
- Lecture 08: Object Perception Camera: FH AAchen University
- Lecture 09: Object Perception Radar: FH AAchen University
- Lecture 10: State Estimation for Localization (Sensor Fusion): Autoware Foundation
- Lecture 11: LGSVL Simulation: LGSVL
- Lecture 12: HD maps: Parkopedia
- Lecture 13: Motion planning and Control: Embotech
- Lecture 14: MARV for Data Storage and Analytics: Ternaris
Other collaborators are: AutonomouStuff, Samsung, and Tier IV.
Each lecture will contain a theoretical background, programatic examples and systematic examples, so that the students will be able to experiment with the labs.