门式起重机作业环境的自适应统计滤波去噪OA

Addressing the issue of noise interference in point cloud images collected during the intelligent and unmanned transformation of gantry cranes,an adaptive statistical filtering and fitted plane filtering point cloud denoising algorithm combining point cloud density and volume is proposed.First,based on Euclidean distance,the noise points in the point cloud are divided into far-field noise points and near-field noise points.For far-field noise points,the optimal parameters of statistical filtering are determined through adaptive search,with point cloud density and volume as the objective functions for filtering.For near-field noise points,they are removed by calculating the distance between the target point cloud and the fitted plane.Experiments on the Stanford public dataset show that the proposed algorithm has better denoising performance than the traditional radius filtering method.The processing results of point cloud data collected from the actual operation of gantry cranes further verify its excellent denoising performance.This algorithm can effectively filter out point cloud noise,improve point cloud quality,and provide a reliable point cloud preprocessing method for the intelligent transformation of gantry cranes.