A New Method of Hybrid Index for Mobile LiDAR Point Cloud Data

ZHANG Rui; LI Guangyun; WANG Li; LI Minglei; ZHOU Yanglin

doi:10.13203/j.whugis20160441

Volume 43 Issue 7

Jul. 2018

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2018 > 43(7): 993-999. > DOI: 10.13203/j.whugis20160441

ZHANG Rui, LI Guangyun, WANG Li, LI Minglei, ZHOU Yanglin. A New Method of Hybrid Index for Mobile LiDAR Point Cloud Data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(7): 993-999. DOI: 10.13203/j.whugis20160441

Citation:

PDF (4993 KB)

A New Method of Hybrid Index for Mobile LiDAR Point Cloud Data

1.
School of Navigation and Aerospace Engineering, Information Engineering University, Zhengzhou 450001, China
2.
North China University of Water Resources and Electric Power, Zhengzhou 450045, China

Funds:

The National Natural Science Foundation of China 41501491

the Key Science Research Program of Higher Education of Henan Province 16A520062

More Information

Author Bio:
ZHANG Rui, associate professor, specializes in management of massive point clouds and 3D targets recognition.E-mail:zhangrui@ncwu.edu.cn
Received Date: January 31, 2017
Published Date: July 04, 2018

Graphical Abstract

Abstract

Abstract

For mobile LiDAR point cloud data, a new hybrid index structure combining global KD-tree and local Octree is proposed to improve the efficiency of data organization and management, which is named as KD-OcTree index. Firstly, global KD-tree reconstructs the spatial neighborhood relations by defining the segmenting dimension and segmenting planes, for the purpose of ensuring the balance of the whole index. Then, local Octree is constructed in the leafs of KD-tree, which can avoid some shortcomings such as the unbalance of point cloud distribution, deeper Octree, large amount of non-point space, and so on. Lastly, we take three real scenes' point clouds as test data to process. The experimental results and comparative analysis show that the KD-OcTree index can not only improve the speed of constructing index and neighborhood searching, but also improve the effect of data-processing and influence the reliability of classification.
- mobile LiDAR,
- point clouds,
- global KD-tree,
- local Octree,
- hybrid spatial index

FullText(HTML)

References (17)

References

[1]	Samet H. The Quadtree and Related Hierarchical Data Structures[J].ACM Computing Surveys, 1984, 16(2):187-260 doi: 10.1145/356924.356930
[2]	Meagher D. Geometric Modeling Using Octree Encoding[J].Computer Graphics and Image Proces-sing, 1982, 19(2):129-147 doi: 10.1016/0146-664X(82)90104-6
[3]	Karlsson J S. HQT^*:A Scalable Distributed Data Structure for High-Performance Spatial Accesses[M]. Norwell:Kluwer Academic Publishers, 2000
[4]	龚俊, 谢潇.基于R树索引的三维可视化查询方法[J].武汉大学学报·信息科学版, 2011, 36(10):1140-1143 http://ch.whu.edu.cn/CN/abstract/abstract687.shtml Gong Jun, Xie Xiao. Three-Dimension Visualization Query Method Based on R-Tree[J]. Geomatics and Information Science of Wuhan University, 2011, 36(10):1140-1143 http://ch.whu.edu.cn/CN/abstract/abstract687.shtml
[5]	Bechmann N, Kriegel H P, Schneider R, et al. The R^*-Tree: An Efficient and Robust Access Method for Points and Rectangles[C]. The ACM SIGMOD International Conference on Management of Data, Atlantic City, New Jersey, USA, 1990
[6]	Zhu Q, Yao X, Huang D, et al. An Efficient Data Management Approach for Large Cybercity GIS[C]. Symposium on Geospatial Theory, Processing and Applications, Ottawa, Canada, 2002
[7]	龚俊, 朱庆, 张叶廷, 等.顾及多细节层次的三维R树索引扩展方法[J].测绘学报, 2011, 40(2):249-255 http://www.cqvip.com/QK/90069X/201102/37425567.html Gong Jun, Zhu Qing, Zhang Yeting, et al. An Efficient 3D R-tree Extension Method Concerned with Levels of Details[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(2):249-255 http://www.cqvip.com/QK/90069X/201102/37425567.html
[8]	陈驰, 王珂, 徐文学.海量车载激光扫描点云数据的快速可视化方法[J].武汉大学学报·信息科学版, 2015, 40(9):1163-1168 http://ch.whu.edu.cn/CN/abstract/abstract3315.shtml Chen Chi, Wang Ke, Xu Wenxue. Real-Time Visualizing of Massive Vehicle-Borne Laser Scanning Point Clouds[J]. Geomatics and Information Science of Wuhan University, 2015, 40(9):1163-1168 http://ch.whu.edu.cn/CN/abstract/abstract3315.shtml
[9]	郭明. 海量精细空间数据管理技术研究[D]. 武汉: 武汉大学, 2011 Guo Ming. Research on Huge Fine Spatial Data Management[D]. Wuhan: Wuhan University, 2011
[10]	谢洪. 基于地面三维激光扫描技术的海量定义模型重建关键算法研究[D]. 武汉: 武汉大学, 2013 Xie Hong. Model Reconstruction Algorithms for Massive Point Cloud from Terrestrial Laser Scanner[D]. Wuhan: Wuhan University, 2013
[11]	王晏民, 郭明.大规模点云数据的二维与三维混合索引方法[J].测绘学报, 2012, 41(4):605-612 http://www.cqvip.com/QK/90069X/201204/42958752.html Wang Yanmin, Guo Ming. A Combined 2D and 3D Spatial Indexing of Very Large Point-Cloud Data Sets[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(4):605-612 http://www.cqvip.com/QK/90069X/201204/42958752.html
[12]	Yang Jiansi, Huang Xianfeng. A Hybrid Spatial Index for Massive Point Cloud Data Management and Visualization[J]. Transaction in GIS, 2014, 18(S1):97-108 doi: 10.1007%2F978-3-319-48671-0_19
[13]	Richter R, Behrens M, Döllner J. Object Class Segmentation of Massive 3D Point Clouds of Urban Areas Using Point Cloud Topology[J]. International Journal of Remote Sensing, 2013, 34(23):8408-8424 doi: 10.1080/01431161.2013.838710
[14]	齐晓隆. 基于八叉树与R⁺树的点云混合索引研究[D]. 北京: 北京建筑大学, 2013 Qi Xiaolong. Research on Mixed Indexing of Octree and R⁺ Tree for Points Cloud[D]. Beijing: Beijing University of Civil Engineering and Architecture, 2013
[15]	杨建思.一种四叉树与KD树结合的海量机载LiDAR数据组织管理方法[J].武汉大学学报·信息科学版, 2014, 39(8):918-922 http://d.old.wanfangdata.com.cn/Periodical/whchkjdxxb201408007 Yang Jiansi. A Method of Combing the Model of the Global Quadtree Index with Local KD-Tree for Massive Airborne LiDAR Point Cloud Data Organization[J]. Geomatics and Information Science of Wuhan University, 2014, 39(8):918-922 http://d.old.wanfangdata.com.cn/Periodical/whchkjdxxb201408007
[16]	李明磊. 地面激光扫描点云预处理技术研究[D]. 郑州: 信息工程大学, 2014 Li Minglei. Technology of Preprocessing on 3D Laser Scanned Point Clouds[D]. Zhengzhou: Information Engineering University, 2014
[17]	于永涛. 大场景车载激光点云三维目标检测算法研究[D]. 厦门: 厦门大学, 2015 Yu Yongtao. Research on Three-Dimensional Object Detection from Large-Scale Mobile Laser Scanning Point Clouds[D]. Xiamen: Xiamen University, 2015

Cited By

Cited by

Periodical cited type(6)

1.	杨丽娟，崔钰琳，杨紫骞，翟光杰，王超. 基于点云空间分布特征的多级索引结构. 激光与红外. 2023(01): 137-145 .
2.	米晓新，杨必胜，董震. 车载激光点云道路场景可视域快速计算与应用. 武汉大学学报(信息科学版). 2020(02): 258-264 .
3.	蔡来良，宋德云，魏峰远，薛渊，舒前进. 矿区地表彩色点云的自动分类. 测绘通报. 2020(05): 55-58 .
4.	张小陆. 基于B+树的电力大数据混合索引设计分析. 电子设计工程. 2020(22): 7-10+15 .
5.	邱波，张丰，杜震洪，刘仁义，张书瑜，范心仪. 一种面向移动终端地理场景点云在线可视化的集成型索引. 浙江大学学报(理学版). 2019(01): 101-110+120 .
6.	于安斌，梅文胜. 一种R树与格网结合的海量地铁隧道点云管理方法. 武汉大学学报(信息科学版). 2019(10): 1553-1559 .

Other cited types(12)

Get Citation

PDF

XML

Article views (1517) PDF downloads (318) Cited by(18)

A New Method of Hybrid Index for Mobile LiDAR Point Cloud Data

Abstract

References

Cited by

Periodical cited type(6)

Other cited types(12)

Catalog

Related

Export File

Citation

Format

Content