Home » Publication » 27687

Dettaglio pubblicazione

2023, International Conference of the Italian Association for Artificial Intelligence 2023, Pages 310-320

Enhancing LiDAR Performance: Robust De-Skewing Exclusively Relying on Range Measurements (02a Capitolo o Articolo)

Salem O. A. A. K., Giacomini E., Brizi L., Di Giammarino L., Grisetti G.

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.
ISBN: 978-3-031-47545-0; 978-3-031-47546-7
© Università degli Studi di Roma "La Sapienza" - Piazzale Aldo Moro 5, 00185 Roma