  Lidar odometry (LO) is a key technology in numerous reliable and accurate
localization and mapping systems of autonomous driving. The state-of-the-art LO
methods generally leverage geometric information to perform point cloud
registration. Furthermore, obtaining point cloud semantic information which can
describe the environment more abundantly will help for the registration. We
present a novel semantic lidar odometry method based on self-designed
parameterized semantic features (PSFs) to achieve low-drift ego-motion
estimation for autonomous vehicle in realtime. We first use a convolutional
neural network-based algorithm to obtain point-wise semantics from the input
laser point cloud, and then use semantic labels to separate the road, building,
traffic sign and pole-like point cloud and fit them separately to obtain
corresponding PSFs. A fast PSF-based matching enable us to refine geometric
features (GeFs) registration, reducing the impact of blurred submap surface on
the accuracy of GeFs matching. Besides, we design an efficient method to
accurately recognize and remove the dynamic objects while retaining static ones
in the semantic point cloud, which are beneficial to further improve the
accuracy of LO. We evaluated our method, namely PSF-LO, on the public dataset
KITTI Odometry Benchmark and ranked #1 among semantic lidar methods with an
average translation error of 0.82% in the test dataset at the time of writing.
