Most commercially available Light Detection and Ranging (LiDAR)s measure the distances along a 2D section of the environment by sequentially sampling the free range along directions centered at the sensor's origin. When the sensor moves during the acquisition, the measured ranges are affected by a phenomenon known as "skewing", which appears as a distortion in the acquired scan. Skewing potentially affects all systems that rely on LiDAR data, however, it could be compensated if the position of the sensor were known each time a single range is measured. Most methods to de-skew a LiDAR are based on external sensors such as IMU or wheel odometry, to estimate these intermediate LiDAR positions. In this paper, we present a method that relies exclusively on range measurements to effectively estimate the robot velocities which are then used for de-skewing. Our approach is suitable for low-frequency LiDAR where the skewing is more evident. It can be seamlessly integrated into existing pipelines, enhancing their performance at a negligible computational cost.

翻译：大多数商用激光雷达（LiDAR）通过沿传感器原点为中心的方向顺序采样自由空间距离，来测量环境二维截面的距离。当传感器在采集过程中移动时，测量距离会受到一种称为“畸变”现象的影响，表现为采集扫描中的扭曲变形。畸变可能影响所有依赖LiDAR数据的系统，但若在每次测量单个距离时已知传感器位置，则可进行补偿。大多数LiDAR去畸变方法依赖外部传感器（如IMU或轮式里程计）来估计这些中间LiDAR位置。本文提出一种仅依赖距离测量来有效估计机器人速度的方法，并用于去畸变。我们的方法适用于畸变更明显的低频LiDAR，可无缝集成到现有处理流程中，以可忽略的计算成本提升其性能。