Denoising Method Based on Improved DBSCAN for Lidar Point Cloud

Lidar 3D point cloud data often contains significant noise, which affects the accuracy and reliability of subsequent data analysis. This study aims to propose an improved adaptive DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to more effectively remove noise from LiD...

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Veröffentlicht in:	IEEE access 2024, Vol.12, p.137656-137666
Hauptverfasser:	Zhao, Zhe, Zhou, Weilong, Liang, Denghui, Liu, Juntao, Lee, Xiaobao
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy adaptive Algorithms Cloud point curves Clustering Clustering algorithms Curve fitting Data analysis Data processing DBSCAN Density Filtering Indexes Laser radar Lidar Noise Noise levels Noise reduction Parameter identification Parameter robustness Point cloud compression Spatial data Three dimensional models Three-dimensional point cloud
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