SR-LIO：基于扫描重建的LiDAR-惯性里程计
SR-LIO: LiDAR-Inertial Odometry with Sweep Reconstruction
AI总结：
SR-LIO基于iEKF框架，通过扫描重建方法将旋转LiDAR原始扫描分割重建为高频扫描，减少迭代状态更新时间间隔，提升状态估计精度与频率，并引入分段畸变校正避免轨迹误差，在四个公开数据集上精度优于现有最先进方法。
摘要：
本文提出了一种基于迭代扩展卡尔曼滤波器（iEKF）框架的新型LiDAR-惯性里程计（LIO），命名为SR-LIO。我们采用扫描重建方法，对来自旋转LiDAR的原始输入扫描进行分割和重建，以获得频率更高的重建扫描。我们发现该方法能有效减少每次迭代状态更新的时间间隔，提高状态估计精度，并使iEKF框架能够用于融合高频IMU和低频LiDAR。为防止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分段执行畸变校正。在四个公开数据集上的实验结果表明，SR-LIO在精度上优于所有现有最先进方法，且通过所提扫描重建减少迭代状态更新的时间间隔，可提高估计状态的精度和频率。SR-LIO的源代码已公开，供社区发展使用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
