  LiDARs are usually more accurate than cameras in distance measuring. Hence,
there is strong interest to apply LiDARs in autonomous driving. Different
existing approaches process the rich 3D point clouds for object detection,
tracking and recognition. These methods generally require two initial steps:
(1) filter points on the ground plane and (2) cluster non-ground points into
objects. This paper proposes a field-tested fast 3D point cloud segmentation
method for these two steps. Our specially designed algorithms allow instantly
process raw LiDAR data packets, which significantly reduce the processing
delay. In our tests on Velodyne UltraPuck, a 32 layers spinning LiDAR, the
processing delay of clustering all the $360^\circ$ LiDAR measures is less than
1ms. Meanwhile, a coarse-to-fine scheme is applied to ensure the clustering
quality. Our field experiments in public roads have shown that the proposed
method significantly improves the speed of 3D point cloud clustering whilst
maintains good accuracy.
