Denoising method of rail point cloud data based on morphological filtering

Abstract

When using a three-dimensional point cloud scanner to collect data points, due to the influence of various uncertain factors, the data points obtained by the three-dimensional electronic scanning device are always mixed with some noise points. If the noise contained in the point cloud is not filtered through technical methods, these noises will definitely have an impact on the subsequent extraction and recognition of the target set characteristics. Therefore, based on the accurate acquisition of 3D data, we research and design a fast denoising preprocessing algorithm that can be used for high-density 3D data to provide more accurate and effective 3D measurement data for subsequent 3D reconstruction and recognition processing.