论文标题
SR-LIO：带有扫描重建的激光射击速度
SR-LIO: LiDAR-Inertial Odometry with Sweep Reconstruction
论文作者
论文摘要
本文提出了一种基于迭代的扩展卡尔曼滤波器（IEKF）框架的新型LIDAR惯性探针（LIO），名为SR-Lio。我们调整了扫描重建方法，该方法将段和重建从旋转激光雷达的原始输入扫描以获得较高频率的重建扫描。我们发现，这种方法可以有效地减少每个迭代状态更新的时间间隔，从而提高状态估计的准确性，并使IEKF框架使用融合高频IMU和低频LIDAR。为了防止由多个失真校正到特定点引起的不准确的轨迹，我们进一步建议对每个段进行失真校正。四个公共数据集的实验结果表明，我们的SR-LIO在准确性上优于所有现有的最新方法，并通过拟议的扫描重建来减少迭代状态更新的时间间隔，可以提高估计状态的准确性和频率。 SR-LIO的源代码可公开用于社区的发展。
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.
