1 research outputs found
Low-complexity Point Cloud Filtering 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 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