  Existing 3D point-based dynamic point detection and removal methods have a
significant time overhead, making them difficult to adapt to LiDAR-inertial
odometry systems. This paper proposes a label consistency based dynamic point
detection and removal method for handling moving vehicles and pedestrians in
autonomous driving scenarios, and embeds the proposed dynamic point detection
and removal method into a self-designed LiDAR-inertial odometry system.
Experimental results on three public datasets demonstrate that our method can
accomplish the dynamic point detection and removal with extremely low
computational overhead (i.e., 1$\sim$9ms) in LIO systems, meanwhile achieve
comparable preservation rate and rejection rate to state-of-the-art methods and
significantly enhance the accuracy of pose estimation. We have released the
source code of this work for the development of the community.
