Next Article in Journal
Methodological Ambiguity and Inconsistency Constrain Unmanned Aerial Vehicles as A Silver Bullet for Monitoring Ecological Restoration
Previous Article in Journal
The Need for a Standardized Methodology for Quantitative Assessment of Natural and Anthropogenic Land Subsidence: The Agosta (Italy) Gas Field Case
Article Menu

Export Article

Open AccessArticle

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

1,2,3 and 1,2,3,*
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
Key Laboratory for Geo-Environment Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1179;
Received: 10 April 2019 / Revised: 11 May 2019 / Accepted: 16 May 2019 / Published: 17 May 2019
PDF [4854 KB, uploaded 17 May 2019]
  |     |  


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. View Full-Text
Keywords: LiDAR; ball pivoting algorithm; ground filtering; 3D point clouds; DME generation LiDAR; ball pivoting algorithm; ground filtering; 3D point clouds; DME generation

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Ma, W.; Li, Q. An Improved Ball Pivot Algorithm-Based Ground Filtering Mechanism for LiDAR Data. Remote Sens. 2019, 11, 1179.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top