Low-complexity point cloud denoising for LiDAR by PCA-based dimension reduction

Signals emitted by LiDAR sensors would often be negatively influenced during transmission by rain, fog, dust, atmospheric particles, scattering of light and other influencing factors, causing noises in point cloud images. To address this problem, this paper develops a new noise reduction method to f...

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Bibliographic Details
Published in:Optics communications 2021-03, Vol.482, p.126567, Article 126567
Main Authors: Duan, Yao, Yang, Chuanchuan, Chen, Hao, Yan, Weizhen, Li, Hongbin
Format: Article
Language:English
Subjects:
Clustering
LiDAR
Noise reduction
Point cloud
Principal component analysis
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Summary:Signals emitted by LiDAR sensors would often be negatively influenced during transmission by rain, fog, dust, atmospheric particles, scattering of light and other influencing factors, causing noises in point cloud images. To address this problem, this paper develops a new noise reduction method to filter LiDAR point clouds, i.e. an adaptive clustering method based on principal component analysis (PCA). Different from the traditional filtering methods that directly process three-dimension (3D) point cloud data, the proposed method uses dimension reduction to generate two-dimension (2D) data by extracting the first principal component and the second principal component of the original data with little information attrition. In the 2D space spanned by two principal components, the generated 2D data are clustered for noise reduction before being restored into 3D. Through dimension reduction and the clustering of the generated 2D data, this method derives low computational complexity, effectively removing noises while retaining details of environmental features. Compared with traditional filtering algorithms, the proposed method has higher precision and recall. Experimental results show a F-score as high as 0.92 with complexity reduced by 50% compared with traditional density-based clustering method. •An adaptive clustering method based on principal component analysis (PCA) to filter LiDAR point cloud is proposed.•An improved region segmentation approach is put forward which can help yield better noise-reduction effects.•This method derives low computational complexity, effectively removing noises while retaining details of environmental features.
ISSN:0030-4018
1873-0310
DOI:10.1016/j.optcom.2020.126567