Hello ROS community!
We are very excited to announce that we just open-sourced a complete self-driving software platform!
It is something we have been working on for quite some time and we are now in a position to say that we have open sourced a stable, fully documented, end-to-end self-driving software stack with an integrated Gazebo simulation and a user-friendly GUI to facilitate software launching. It builds in under 30 mins (or less if you use the docker), just follow the instructions.
Project Aslan is targeting to facilitate the research and development in autonomous driving projects for low-speed applications in urban environments. We are aiming for maximum community engagement, promoting the research in robotics and enabling education in ROS and self-driving cars.
We would like everyone to be a part of this project!!
There are multiple awesome platforms out there and we engage and support all of them.
What we have done differently is that we developed and tested an easy to install, fully documented and stable self-driving software for specific low-speed urban autonomous applications (20mph control speed urban environments). While we couldn’t test during the lockdown, we took the time to improve our simulation and test on the digital twin of the actual DBW vehicle used during testing. To facilitate the users even more, we have released a full set of tutorials at Project Aslan Youtube Channel.
So with just a few clicks on a GUI, you can:
- Launch a complete self-driving software: Fully documented and tested based on the sense-plan-act robotics top-down approach
- Record rosbags, build point cloud maps, extract waypoints or just use the ones we have provided: rosbag, point cloud map and waypoints csv
- Watch the output of the software stack going live in Gazebo sim and RVIZ
- Develop your own features on top and test them in Gazebo sim or an actual vehicle
- Be part of something awesome!
Features on this Release:
- Graphics User Interface (GUI) with integrated ROS tools
- Docker and Source Code installation
- Vehicle Model basic urdf for RVIZ
- Multiple Sensors Drivers
- Normal Distribution Transform (NDT)* for localization and mapping
- Voxel Grid and Ground Removal point cloud filtering
- Path Planning* based on waypoints
- Object Detection using LiDAR and Radar
- Route Planning* Algorithm based on A* Planner
- High-level supervisor/monitor node
- Vehicle Interface using SocketCAN
- Comprehensive simulation model and worlds with 3 ways of controlling the robot: keyboard, joystick and the vehicle interface
- Complete documentation of the source code in the repo and inside the GUI
*Originally suggested at Autoware 1.10.0. These packages have been configured and modified significantly to fit the requirements of Project ASLAN.