  LiDAR odometry is a pivotal technology in the fields of autonomous driving
and autonomous mobile robotics. However, most of the current works focus on
nonlinear optimization methods, and still existing many challenges in using the
traditional Iterative Extended Kalman Filter (IEKF) framework to tackle the
problem: IEKF only iterates over the observation equation, relying on a rough
estimate of the initial state, which is insufficient to fully eliminate motion
distortion in the input point cloud; the system process noise is difficult to
be determined during state estimation of the complex motions; and the varying
motion models across different sensor carriers. To address these issues, we
propose the Dual-Iteration Extended Kalman Filter (I2EKF) and the LiDAR
odometry based on I2EKF (I2EKF-LO). This approach not only iterates over the
observation equation but also leverages state updates to iteratively mitigate
motion distortion in LiDAR point clouds. Moreover, it dynamically adjusts
process noise based on the confidence level of prior predictions during state
estimation and establishes motion models for different sensor carriers to
achieve accurate and efficient state estimation. Comprehensive experiments
demonstrate that I2EKF-LO achieves outstanding levels of accuracy and
computational efficiency in the realm of LiDAR odometry. Additionally, to
foster community development, our code is
open-sourced.https://github.com/YWL0720/I2EKF-LO.
