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Open AccessArticle

De-Skewing LiDAR Scan for Refinement of Local Mapping

State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
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Sensors 2020, 20(7), 1846; https://doi.org/10.3390/s20071846 (registering DOI)
Received: 31 January 2020 / Revised: 24 March 2020 / Accepted: 25 March 2020 / Published: 26 March 2020
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Simultaneous localization and mapping have become a basic requirement for most automatic moving robots. However, the LiDAR scan suffers from skewing caused by high-acceleration motion that reduces the precision in the latter mapping or classification process. In this study, we improve the quality of mapping results through a de-skewing LiDAR scan. By integrating high-sampling frequency IMU (inertial measurement unit) measurements and establishing a motion equation for time, we can get the pose of every point in this scan’s frame. Then, all points in this scan are corrected and transformed into the frame of the first point. We expand the scope of optimization range from the current scan to a local range of point clouds that not only considers the motion of LiDAR but also takes advantage of the neighboring LiDAR scans. Finally, we validate the performance of our algorithm in indoor and outdoor experiments to compare the mapping results before and after de-skewing. Experimental results show that our method smooths the scan skewing on each channel and improves the mapping accuracy. View Full-Text
Keywords: skewing; slam; 3D-LiDAR; point cloud; sensor fusion skewing; slam; 3D-LiDAR; point cloud; sensor fusion
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He, L.; Jin, Z.; Gao, Z. De-Skewing LiDAR Scan for Refinement of Local Mapping. Sensors 2020, 20, 1846.

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