  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.
