Simultaneous localization and mapping have become a basic requirement for most automatic moving robots. However, the LiDAR scan suffers from skewing caused by high-acceleration motion that reduces the precision in the latter mapping or classification process. In this study, we improve the quality of mapping results through a de-skewing LiDAR scan. By integrating high-sampling frequency IMU (inertial measurement unit) measurements and establishing a motion equation for time, we can get the pose of every point in this scan's frame. Then, all points in this scan are corrected and transformed into the frame of the first point. We expand the scope of optimization range from the current scan to a local range of point clouds that not only considers the motion of LiDAR but also takes advantage of the neighboring LiDAR scans. Finally, we validate the performance of our algorithm in indoor and outdoor experiments to compare the mapping results before and after de-skewing. Experimental results show that our method smooths the scan skewing on each channel and improves the mapping accuracy.

中文翻译：

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消除偏斜的LiDAR扫描以优化局部映射。

同时定位和映射已成为大多数自动移动机器人的基本要求。但是，LiDAR扫描会遭受由高加速度运动引起的偏斜，从而降低了后者映射或分类过程中的精度。在这项研究中，我们通过消除偏斜的LiDAR扫描提高了映射结果的质量。通过集成高采样频率的IMU（惯性测量单位）测量结果并建立时间运动方程，我们可以获得该扫描帧中每个点的姿态。然后，将校正此扫描中的所有点并将其转换为第一点的帧。我们将优化范围的范围从当前扫描扩展到点云的局部范围，这不仅考虑了LiDAR的运动，而且还利用了相邻的LiDAR扫描的优势。最后，我们在室内和室外实验中验证了我们算法的性能，以比较去歪斜前后的映射结果。实验结果表明，我们的方法可以平滑每个通道的扫描偏斜并提高映射精度。