关键词: C均值, 三维点云, 去噪, 模糊聚类

Abstract: The point cloud data is uneasy to distinguish and difficult to denoise by outlier 3-D laser scanning. To solve the problems, this paper presents an improved C-means algorithm for solving the 3-D laser scanning point cloud data noise and outliers. The improved C-means algorithm introduces the fuzzy weighting factor that can effectively expand the characteristics of outliers in the dataset and make easier to identify outlier data. The noise is divided into large and small scales in two categories. The C-means clustering algorithm can remove the large scale data smoothing noise and some small noise data using point cloud bilateral filtering method. Compared with the density clustering algorithm, orthogonal total least squares plane fitting and filtering point clouds denoising and feature selection algorithm based on bilateral, the accuracy of denoising is promoted 7.3%, 6.5% and 6%. The experimental results show that the algorithm can remove the noise of large scale, better retention of valid data, improve the effect of denoising.

Key words: C-means, 3D point cloud data, denoising, fuzzy clustering