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.

翻译：本文提出了一种名为SR-LIO的新型激光雷达-惯性里程计（LiDAR-Inertial Odometry, LIO），该里程计基于迭代扩展卡尔曼滤波（iEKF）框架。我们采用了扫描重建方法，对旋转式激光雷达的原始输入扫描进行分割与重建，从而获得更高频率的重建扫描。研究发现，该方法能有效缩短每次迭代状态更新的时间间隔，提升状态估计精度，并使得融合高频惯性测量单元（IMU）与低频激光雷达的iEKF框架得以应用。为避免对特定点进行多次畸变校正导致轨迹不准确，我们进一步提出对每个扫描分割片段执行畸变校正。在四个公开数据集上的实验结果表明，我们的SR-LIO在精度上优于所有现有最先进方法，且通过所提出的扫描重建缩短迭代状态更新时间间隔能够提升状态估计的精度与频率。SR-LIO的源代码已公开，以促进社区发展。