An Improved Ball Pivot Algorithm-Based Ground Filtering Mechanism for LiDAR Data

Wei Ma,Qingquan Li

doi:10.3390/rs11101179

Abstract

Automatic ground filtering is an essential step for Digital Elevation Model (DEM) generation, which has significant application value. However, extraction and classification of ground points from the Light Detection and Ranging (LiDAR) data, especially in multitudinous terrain situations, is a challenging task because it is difficult to determine the set of optimal parameters for removing various non-ground features. In this paper, a new ground filtering technique based on an improved Ball Pivot Algorithm (BPA) is proposed. At the beginning, the LiDAR point cloud dataset was divided into different subsets based on the 2D regular grid. The lowest point in each grid was selected as the seed point to build a single-layer surface. After that, the improved BPA was executed to remove points on the higher location. Then, the rest of the points were calculated and selected as a new seed point according to the spatial relationship with the initial surface. Finally, non-ground points were filtered by means of improved BPA traversing all the grids. Our experimental results on the Benchmark dataset provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) Working Group III/3 showed high accuracy (with a mean kappa coefficient over 80%) in terms of completeness, correctness, and quality for DEM generation. The experimental results demonstrated the proposed method is robust to various terrain situations, as it is more effective and feasible for ground filtering.

Highlights

Digital Elevation Model (DEM) generation is an important research problem in the remote sensing area, with good application prospects in the fields of smart city, 3D mapping, and virtual reality
The methods used to solve this problem vary from machine-learning-based filters and Triangulated Irregular Networks (TIN) ones to filters that rely on morphological properties
Our goal in this paper is to develop a ground filtering model based on 3D alpha shapes (α-shape) with fixed and robust parameters to apply to different environments and conditions, which will significantly reduce labor cost and increase practicability

Summary

Introduction

Digital Elevation Model (DEM) generation is an important research problem in the remote sensing area, with good application prospects in the fields of smart city, 3D mapping, and virtual reality. DEM generation requires a filtering step, which separates the points that belong to the ground from the ones that do not. Increasingly more researchers have focused on ground filtering issues for DEM generation [1,2,3]. Most of the existing works either require a large number of samples for model training or need to set inexplicit parameters that vary with different terrain type and point density. The ground filtering methods are commonly applied to large areas, which include densely forested areas, urban areas, sharp ridges, discontinuous terrains, and so forth. It is still a challenging task to set limited parameters for the purpose of removing a variety of non-ground features in all terrain situations [5]

Methods

Results

Discussion

Conclusion