Filtering of LiDAR Point Cloud Data Based on new TIN Algorithm
Authors
Lichun Sui, Jianfeng Zhu, Haixiong Zhu, Mianqing Zhong
Corresponding Author
Lichun Sui
Available Online April 2017.
- DOI
- 10.2991/icmse-17.2017.14How to use a DOI?
- Keywords
- LiDAR; point cloud; improved TIN; ground points; filtering; morphology
- Abstract
In this paper, the new method is proposed for filtering of airborne LiDAR data based on improved Triangulated Irregular Network(TIN) algorithm and the details of filter principle is described. Firstly, LiDAR point cloud data is organized and designed by regular grid and TIN, the seed points from point cloud data are selected by regional sub-block method or mathematical morphology. Then, an initial sparse TIN is created from the seed points and densified upward gradually and the ground points are extracted through an interactive process. In experiments it is shown that this filtering method can effectively remove different sizes of buildings, low vegetation and other objects, and keep topographical features better.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Lichun Sui AU - Jianfeng Zhu AU - Haixiong Zhu AU - Mianqing Zhong PY - 2017/04 DA - 2017/04 TI - Filtering of LiDAR Point Cloud Data Based on new TIN Algorithm BT - Proceedings of the 2017 7th International Conference on Manufacturing Science and Engineering (ICMSE 2017) PB - Atlantis Press SP - 72 EP - 76 SN - 2352-5401 UR - https://doi.org/10.2991/icmse-17.2017.14 DO - 10.2991/icmse-17.2017.14 ID - Sui2017/04 ER -