An adaptive iterative reweighted filtering methodology for urban MLS dataset

Abstract

ABSTRACT This study presents a novel filtering methodology for Mobile Laser Scanning (MLS) data using robust iterative reweighting. Initially, 3D point clouds are projected onto a 2D grid to create surfaces from the lowest points. Weights are assigned based on the Height Above Ground (HAG) of these points. Ground points are distinguished by applying a surface function to the dataset via iterative reweighting. Among the tested four robust weight functions, the Denmark and Beaton-Tukey functions outperformed others, achieving total error values of 2.30 and 2.32 across three test areas, respectively. This method efficiently filters MLS data, irrespective of ground point proportions.