SAE-GFR Racetrack Detection Using LiDAR Data

Lidar sends out lasers and develops a point cloud with depth information based on the light received.
There are two projects involving Lidar this year. The first will include retraining the lidar neural network on
an open source data set created and contributed to by Formula Student teams. Additionally, relabelling
and creating a custom dataset will be part of that project.
The second project will be to evaluate the current weaknesses and improve the accuracy of the
classic lidar pipeline. The classic pipeline uses a three step process where the points are first prefiltered,
then clustered, and then the cones are detected.

Objectives


● Fall
○ Background research of previous projects and relevant papers completed.
○ Github and development environment set up.
○ ROS tutorial and assignment completed.
○ Design changes conceptualized.
○ Practice for participation in rules quiz.
○ Documentation for any changes made during Fall.
● Winter
○ Final documentation for code completed - details on wiki as well.
○ Testing and benchmarking completed and documented.
○ UML Diagram detailing system.
○ Final edits to the code by May.

Motivations


GFR's autonomous vehicle utilizes two cameras and a single LiDAR to handle cone detection and support racetrack detection.

Qualifications


Minimum Qualifications:
None Listed

Preferred Qualifications:
None Listed


Details


Project Partner:

Devin Pham

NDA/IPA:

No Agreement Required

Number Groups:

1

Project Status:

Accepting Applicants

Website:
https://www.global-formula-racing.com/en/
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