  This paper proposes a novel LiDAR-Inertial odometry (LIO), named SR-LIO,
based on an iterated extended Kalman filter (iEKF) framework. We adapt the
sweep reconstruction method, which segments and reconstructs raw input sweeps
from spinning LiDAR to obtain reconstructed sweeps with higher frequency. We
found that such method can effectively reduce the time interval for each
iterated state update, improving the state estimation accuracy and enabling the
usage of iEKF framework for fusing high-frequency IMU and low-frequency LiDAR.
To prevent inaccurate trajectory caused by multiple distortion correction to a
particular point, we further propose to perform distortion correction for each
segment. Experimental results on four public datasets demonstrate that our
SR-LIO outperforms all existing state-of-the-art methods on accuracy, and
reducing the time interval of iterated state update via the proposed sweep
reconstruction can improve the accuracy and frequency of estimated states. The
source code of SR-LIO is publicly available for the development of the
community.
