论文标题
基于PCA的尺寸降低的低复杂点云过滤
Low-complexity Point Cloud Filtering for LiDAR by PCA-based Dimension Reduction
论文作者
论文摘要
LIDAR传感器发出的信号通常会受到雨水，雾，灰尘，大气颗粒，光和其他影响因子的散射的传播期间的负面影响，从而在点云图像中引起噪音。为了解决这个问题，本文开发了一种新的降噪方法来过滤LiDar点云，即基于主成分分析（PCA）的自适应聚类方法。与直接处理三维（3D）点云数据的传统过滤方法不同，该方法使用尺寸缩小来生成二维（2D）数据，通过提取原始数据的第一个主要组件和第二个主组件，而没有信息损耗。在由两个主要组件跨越的2D空间中，生成的2D数据聚类以减少噪声，然后再恢复为3D。通过降低尺寸和生成的2D数据的聚类，该方法得出了低计算复杂性，有效地消除了噪音，同时保留了环境特征的细节。与传统的过滤算法相比，所提出的方法具有更高的精度和回忆。实验结果表明，与传统的基于密度的聚类方法相比，F-评分高达0.92，复杂性降低了50％。
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.
