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Title	A High-Accuracy Multi-Temporal Laser Point Cloud Registration Method For Providing A Detailed Expression Of The Urban Environment
ID_Doc	2075
Authors	Xu M.; Zhong X.; Ma H.; Huang J.; Zhong R.
Year	2024
Published	Sustainable Cities and Society, 101
DOI	http://dx.doi.org/10.1016/j.scs.2024.105186
Abstract	In consideration of the significant changes in urban environmental scenes and the impact of factors such as signal obstruction on positioning, the spatial positions of multi-temporal Mobile Laser Scanning (MLS) point clouds collected in the same area are inconsistent, which makes it challenging to provide a complete expression of the urban road environment. To address this issue, a precise registration algorithm based on pole-like targets is proposed. The core steps include registration primitive extraction: a cylindrical spatial neighborhood is established based on the distinctive key points, and precise extraction of pole-like geospatial point cloud data is performed using the Euclidean clustering method with the normal curvature constraint. Achieving optimal spatial transformation: a circle center fitting method with additional Mean Absolute Error (MAE) loss function constraints was developed based on the stable elemental unit which was utilized to accurately construct homonymous feature point pairs. Validation results from multiple real urban point cloud datasets collected from different experimental areas and platforms demonstrate that our method achieves superior registration performance, with mean residuals below 5 cm in both the planar and elevation directions. The proposed method exhibits strong universality and registration robustness for multi-temporal point cloud data of the homologous or cross-sources. © 2024 Elsevier Ltd
Author Keywords	Cross-source MLS point cloud; Feature circle fitting; Mobile laser scanning; Point cloud registration; Pole-like target object; Smart city environment

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