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

Enhancing LiDAR performance: Robust De-skewing Exclusively Relying on Range Measurements