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

arXiv: https://arxiv.org/abs/2303.07312 · HTML: https://mlsanity.com/paper/2303.07312/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2023.

## Built on by (most cited first, 1 of 1)

- [MAD-ICP: It Is All About Matching Data -- Robust and Informed LiDAR Odometry](https://mlsanity.com/paper/2405.05828/) (2024)

## Abstract (arXiv)

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.