3D Vehicle Detection on an FPGA from LiDAR Point Clouds

In this paper is presented a deep neural network architec- ture designed to run on a field-programmable gate array (FPGA) for detection vehicle on LIDAR point clouds. This works present a network based on VoxelNet adapted to run on an FPGA and to locate vehicles on point clouds from a 32 and a 64 channel optical sensor. For training the pre- sented network the Kitti and Nuscenes dataset have been used. This work aims to motivate the usage of dedicated FPGA targets for training and validating neural network due to their accelerated computational capability compared to the well known GPUs. This platform also has some constraints that need to be assessed and taken care during development (limited memory e.g.). This research presents an implementation to overcome such limitations and obtain as good results as if a GPU would be used.