Robust Perception for Formula Student Driverless Racing
Building an autonomous system for race cars requires robust and highly accurate perception running in real time. This thesis proposes a novel ground removal strategy for 3D LiDAR perception, modelling the ground as several planes, and a novel clustering method for LiDARs that sweep the scene in a predefined pattern resulting in a 20-fold performance increase over clustering methods commonly used f
