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Low-complexity Point Cloud Filtering for LiDAR by PCA-based Dimension Reduction

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Document pages: 7 pages

Abstract: Signals emitted by LiDAR sensors would often be negatively influenced duringtransmission by rain, fog, dust, atmospheric particles, scattering of light andother influencing factors, causing noises in point cloud images. To addressthis problem, this paper develops a new noise reduction method to filter LiDARpoint clouds, i.e. an adaptive clustering method based on principal componentanalysis (PCA). Different from the traditional filtering methods that directlyprocess three-dimension (3D) point cloud data, the proposed method usesdimension reduction to generate two-dimension (2D) data by extracting the firstprincipal component and the second principal component of the original datawith little information attrition. In the 2D space spanned by two principalcomponents, the generated 2D data are clustered for noise reduction beforebeing restored into 3D. Through dimension reduction and the clustering of thegenerated 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 higherprecision and recall. Experimental results show a F-score as high as 0.92 withcomplexity reduced by 50 compared with traditional density-based clusteringmethod.

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