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

1,2,3 and 1,2,3,*
1
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
2
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
3
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; https://doi.org/10.3390/rs11101179
Received: 10 April 2019 / Revised: 11 May 2019 / Accepted: 16 May 2019 / Published: 17 May 2019
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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. 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
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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).
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Ma, W.; Li, Q. An Improved Ball Pivot Algorithm-Based Ground Filtering Mechanism for LiDAR Data. Remote Sens. 2019, 11, 1179.

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