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.

翻译：激光雷达里程计是自动驾驶与自主移动机器人领域的关键技术。然而，当前多数研究聚焦于非线性优化方法，而使用传统迭代扩展卡尔曼滤波框架解决该问题仍面临诸多挑战：IEKF仅对观测方程进行迭代，依赖初始状态的粗略估计，不足以完全消除输入点云中的运动畸变；在复杂运动的状态估计过程中，系统过程噪声难以确定；以及不同传感器载体间运动模型存在差异。为解决这些问题，我们提出了双迭代扩展卡尔曼滤波及其基于此的激光雷达里程计。该方法不仅对观测方程进行迭代，还利用状态更新迭代式地减轻激光雷达点云中的运动畸变。此外，它在状态估计过程中根据先验预测的置信度动态调整过程噪声，并为不同传感器载体建立运动模型，以实现精确高效的状态估计。综合实验表明，I2EKF-LO在激光雷达里程计领域达到了卓越的精度与计算效率水平。同时，为促进社区发展，我们的代码已开源。https://github.com/YWL0720/I2EKF-LO。