Autonomous Course Navigation by High-Speed 1/10 Scale Racecars Using Lidar and Passive Stereo Camera
School Name
Governor's School for Science & Mathematics
Grade Level
12th Grade
Presentation Topic
Engineering
Presentation Type
Mentored
Abstract
The field of autonomous vehicles is rapidly expanding. Companies like Google and Tesla have been on the forefront of these advances. This project aimed to explore the challenges of autonomous vehicles through scale model cars. Our Rapid Autonomous Complex-Environment Competing Ackermann-Steering Robots (RACECARs) were outfitted with a 270° laser radar (LiDAR) and a color stereo camera and ran Ubuntu for ARM processors using the open-source Robot Operating System (ROS) software library. The project was broken into weeks with building goals culminating in the completion of a racecourse. The first goal was to use LiDAR for autonomous wall-following. The second goal was to identify colored flags and turn based on the color, following the correct wall. The next focus was on space exploration and identifying a greater variety of colored flags. Our work shed light on the pitfalls of autonomous navigation and the path that future work will follow.
Recommended Citation
Bregman, Nikki, "Autonomous Course Navigation by High-Speed 1/10 Scale Racecars Using Lidar and Passive Stereo Camera" (2017). South Carolina Junior Academy of Science. 91.
https://scholarexchange.furman.edu/scjas/2017/all/91
Location
Wall 223
Start Date
3-25-2017 9:15 AM
Presentation Format
Oral and Written
Group Project
No
Autonomous Course Navigation by High-Speed 1/10 Scale Racecars Using Lidar and Passive Stereo Camera
Wall 223
The field of autonomous vehicles is rapidly expanding. Companies like Google and Tesla have been on the forefront of these advances. This project aimed to explore the challenges of autonomous vehicles through scale model cars. Our Rapid Autonomous Complex-Environment Competing Ackermann-Steering Robots (RACECARs) were outfitted with a 270° laser radar (LiDAR) and a color stereo camera and ran Ubuntu for ARM processors using the open-source Robot Operating System (ROS) software library. The project was broken into weeks with building goals culminating in the completion of a racecourse. The first goal was to use LiDAR for autonomous wall-following. The second goal was to identify colored flags and turn based on the color, following the correct wall. The next focus was on space exploration and identifying a greater variety of colored flags. Our work shed light on the pitfalls of autonomous navigation and the path that future work will follow.
Mentor
Mentor: Sertac Karaman, Massachussets Institute of Technology