Tornado method for ground point filtering from LiDAR point clouds

Abstract

LiDAR (light detection and ranging) is one of the most important techniques used in remote sensing and photogrammetry to extract high quality features, objects, and models. In this paper, we present an innovative and simple method for ground point filtering from LiDAR point clouds. This method is inspired by nature, where tornadoes destroy buildings and other manmade objects and uproot trees. This simulates the removing of points of building, trees and other objects from LiDAR point clouds. We have imposed that the tornado is a vertical cone with a down vertex and up base. The vertical cone moves on the ground surface so that a network of points is selected as vertex points for the vertical cone. Points that are located inside the cone are removed as non ground points. In other words, the distances between the points and the cone axis are measured, then these distances are compared with calculated distances relative to the cone equation, finally, the points within the cone are removed. To evaluate the proposed method, the datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) are used. The results show that this method can adapt to complex environments and achieve high filtering accuracy. Furthermore, by comparing our method with other methods tested by the ISPRS, the proposed method achieves an average total error of 6.63%, which is smaller than that of the most methods.