Authors:
Ahmad Kamal Aijazi
and
Paul Checchin
Affiliation:
Institut Pascal, UMR 6602 CNRS Université Clermont Auvergne, F-63000 Clermont-Ferrand, France
Keyword(s):
LiDAR, Non-Repetitive Scanning, 3D Point Cloud and Scan Registration.
Abstract:
The task of 3D point cloud registration is fundamentally about aligning multiple scans or point clouds obtained from one or more LiDAR sensors to create a unified and accurate representation of the scanned scene. This process serves as the cornerstone for applications such as map building, autonomous navigation, land surveying and many others. While 3D registration techniques have made significant advancements, several persistent challenges continue to warrant research attention and innovation. Recently, non-repetitive scanning LiDAR sensors are emerging as a promising alternative for 3D data acquisition. In this paper, a novel 3D point cloud registration method is presented that exploits the unique scanning pattern of the sensor to register successive 3D scans. The sensor is first characterized and then, using the characteristic equation of the unique scanning pattern, a perfect scan is reconstructed at the target distance. The real scan is then compared with this reconstructed scan
to extract objects in the scene. The displacements of these extracted objects in successive scans, with respect to the center of the unique scanning pattern, are compared in successive scans to determine the transformations that are then used to register the successive scans. The proposed method is evaluated on two real and different datasets and compared with other state-of-the-art registration methods. The results show that the method is comparable with other methods in terms of accuracy but surpasses them in performance in terms of processing time.
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