We did a tutorial about how to train Computer Vision and LiDAR perception networks during a workshop hosted by Formula Student Germany and Waymo.
Perception team presented our summer projects to all our sponsors, families and potential future members! Find more detail in the video and slides.
Normal LiDAR in the market runs at 10hz, which is sufficient for state-of-the-art autonomous road vehicle, but not enough for autonomous racing vehicle that runs at 180mph. In order to make a "faster LiDAR", inspired by the fact that camera(30+hz) and LiDAR(10hz) holds different operating frequency, we propose a method that uses both camera and LiDAR history information to predict future LiDAR frames.
We deployed the state-of-the-art LiDAR perception model ["Point-Voxel CNN"](http://papers.nips.cc/paper/8382-point-voxel-cnn-for-efficient-3d-deep-learning.pdf) on MIT Driverless's full scale autonomous racing vehicle. The deployment includes converting model task from segmentation to classification, ROS integration and full scale vehicle testing. Find full story in the video provided here.