一种城区LiDAR点云数据的抽稀算法

doi:10.11873/j.issn.1004-0323.2019.6.1245

遥感技术与应用

2019, Vol. 34

Issue (6): 1245-1251 DOI: 10.11873/j.issn.1004-0323.2019.6.1245

数据与图像处理

一种城区LiDAR点云数据的抽稀算法

陈佩奇(

),赖旭东(

),李咏旭

武汉大学遥感信息工程学院，湖北武汉 430079

A Thinning Algorithm of LiDAR Point Cloud Data in Urban Area

Peiqi Chen(

),Xudong Lai(

),Yongxu Li

School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

全文: PDF(3233 KB) HTML

摘要：

当城区LiDAR点云数据密度较大时，存在大量的数据冗余，造成了计算量大、效率低、显示不便等一系列问题，使得建筑物的三维可视化及三维重建等应用受到较大挑战。针对该问题，结合泊松碟采样在测地空间中的地形自适应特点，提出了适用于城区LiDAR点云数据的抽稀算法。泊松碟采样随机将与已有采样点的测地距离大于某一阈值的点加入采样点集，并不断重复这一过程直至没有新的采样点加入为止。在此基础上，依据LiDAR点云数据的特点，定义了一种新的与所选点与其邻域内其他点间高度差标准差相关的加权测地距离，改进了泊松碟采样算法。该方法能有效调整城区建筑物的采样率，从而尽可能地保持建筑物的原始特征，并保留良好的可视化效果。四组对比实验结果表明了该算法的适用性及高效性。

关键词： 泊松碟; 测地距离; LiDAR; 点云数据; 抽稀; 自适应采样

Abstract:

When the density of LiDAR point cloud data in urban area is high, there is so much data redundancy that a series of problems such as large computation, low efficiency, inconvenient display and so on arise, making the application of 3D visualization and 3D reconstruction of buildings more challenging. To solve this problem, a thinning algorithm suitable for LiDAR point cloud data in urban area is proposed, which combines the terrain adaptive features of Poisson disk sampling in geodesic space. Poisson disk sampling randomly add points whose geodesic distance is larger than a certain threshold to the sampling point set, and repeat this process until there are no new sampling points can be added anymore. On this basis, according to the characteristics of LiDAR point cloud data, a new weighted geodesic distance related to the height standard deviation of the points around the selected point is defined to improve the Poisson disk sampling algorithm. This method can effectively adjust the sampling rate of urban buildings, so as to keep the original features of buildings as much as possible, and keep good visualization effect at the same time. The experimental results of four sets of data demonstrate the applicability and efficiency of the algorithm.

Key words: Poisson disk Geodesic distance LiDAR Point cloud data Thinning Adaptive sampling

收稿日期: 2018-09-01 出版日期: 2020-03-23

ZTFLH:

P237

基金资助: 国家自然科学基金面上项目“密集LiDAR点云三维密度特征的表征及应用研究”(41771368);广东省国土资源技术中心项目“广东省机载LiDAR点云数据获取与数字高程模型更新项目技术研究服务”(0612-1841D0330175)

通讯作者: 赖旭东 E-mail: cpq94@126.com;laixudong @whu.edu.cn

作者简介: 陈佩奇（1994-），女，湖北武汉人，硕士研究生，主要从事机载激光雷达数据处理及应用技术研究。E?mail:cpq94@126.com。

	服务
	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	陈佩奇
	赖旭东
	李咏旭

引用本文:

陈佩奇,赖旭东,李咏旭. 一种城区LiDAR点云数据的抽稀算法[J]. 遥感技术与应用, 2019, 34(6): 1245-1251.

Peiqi Chen,Xudong Lai,Yongxu Li. A Thinning Algorithm of LiDAR Point Cloud Data in Urban Area. Remote Sensing Technology and Application, 2019, 34(6): 1245-1251.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.6.1245 或 http://www.rsta.ac.cn/CN/Y2019/V34/I6/1245

图1 测地距离与欧氏距离

图2 按高程渲染的点云原始数据

表1 数据详细信息表

图3 按高程渲染的实验结果

表2 实验结果

1	Lai Xudong, Li Yongxu, Chen Peiqi. Present Situation and Prospect of Airborne LiDAR Technology[J]. Geospatial Information, 2017, 15(8):1-4.
1	赖旭东, 李咏旭, 陈佩奇. 机载激光雷达技术现状及展望[J]. 地理空间信息, 2017, 15(8):1-4.
2	Lai Xudong. Fundamentals and Applications of Airborne LiDAR[M]. Beijing: Publishing House of Electronics Industry, 2010.
2	赖旭东. 机载激光雷达基础原理与应用[M]. 北京: 电子工业出版社, 2010.
3	Qian Jinju, Zhang Changsai, Wang Ke, et al. Research Review on Thinning Algorithm of Airborne LiDAR Point Cloud Data[J]. Bulletin of Surveying and Mapping, 2017(Sup.1):33-35.
3	钱金菊, 张昌赛, 王柯,等. 机载LiDAR点云数据抽稀算法研究述评[J]. 测绘通报, 2017(增刊1):33-35.
4	Zhang Xiaohong. Theory and Method of Airborne LiDAR Measurement Technology[M]. Wuhan: Wuhan University Press, 2007.
4	张小红. 机载激光雷达测量技术理论与方法[M]. 武汉: 武汉大学出版社, 2007.
5	Wang Xuejiao, Hong Youtang. The Application of Airborn LiDAR Technique in Rapid Production for High-accuracy DEM[J].Beijing Surveying and Mapping, 2011(4):46-48.
5	王雪娇, 洪友堂. 机载LiDAR技术在快速生产高精度DEM中的应用[J]. 北京测绘, 2011(4):46-48.
6	Wang Pu, Xing Yanqiu, Wang Cheng. Road Extraction Using Airborne LiDAR Data in Mountainous Areas[J]. Remote Sensing Technology and Application, 2017,32(5):851-857.
6	王璞, 王成, 习晓环,等. 机载LiDAR数据提取山区道路方法研究[J]. 遥感技术与应用， 2017, 32(5):851-857.
7	Xu Jingzhong, Wan Youchuan, Zhang Shengwang. On Simplification Method for LiDAR Ground Points Cloud[J]. Journal of Geomatcis, 2008,33(1):32-34.
7	徐景中, 万幼川, 张圣望. LiDAR地面点云的简化方法研究[J]. 测绘地理信息, 2008, 33(1):32-34.
8	Miao Zhixiu, Qi Hua, Wang Guochang, et al. Discussion on Thinning Algorithm for Construction of DEM based on Airborne LiDAR Data[J]. Railway Investigation and Surveying, 2010, 36(4):39-44.
8	缪志修, 齐华, 王国昌,等. 基于机载LiDAR数据构建的DEM抽稀算法研究[J]. 铁道勘察, 2010, 36(4):39-44.
9	Yang Mingjun, Su Chunmei, Kang Bingfeng, et al. Study and Application on Thinning Algorithm of Airborne LiDAR Data in Plain Area[J]. Bulletin of Surveying and Mapping, 2019(1):109-115.
9	杨明军, 苏春梅, 康冰锋,等. 平原地区机载激光雷达数据的抽稀算法分析[J]. 测绘通报, 2019(1):109-115.
10	Hou W, Zhang X, Li X, et al. Poisson Disk Sampling in Geodesic Metric for DEM Simplification[J]. International Journal of Applied Earth Observation & Geoinformation, 2013, 23(1):264-272.
11	Hou Wenguang, Wu Zicui, Ding Mingyue. Poisson Disk Sampling in Geodesic Metric for DEM Simplification[J]. Acta Electronica Sinica, 2012, 40(6):1274-1278.
11	侯文广, 吴梓翠, 丁明跃. 测地空间中泊松碟采样的地形模型约简方法[J]. 电子学报, 2012, 40(6):1274-1278.
12	Cook R. Stochastic Sampling in Computer Graphics[J]. ACM Trans Graph, 1986, 5(1):137-145.
13	Cao Lin, Xu Ziqian, Dai Jinsong, et al. Method Optimization and Accuracy Evaluation of Terrain and Buildings Extraction based on LiDAR and CCD Data[J]. Remote Sensing Technology and Application, 2014,29(1):130-137.
13	曹林, 许子乾, 代劲松,等. 基于LiDAR和CCD数据的地形与建筑提取方法优化及精度评价[J].遥感技术与应用,2014,29(1):130-137.
14	Dong Baogen, Ma Hongchao, Che Sen, et al. Method of Land Cover Refined Classification Supported by LiDAR Point Clouds[J]. Remote Sensing Technology and Application, 2016,31(1):165-169.
14	董保根, 马洪超, 车森,等. LiDAR点云支持下地物精细分类的实现方法[J].遥感技术与应用,2016,31(1):165-169.
15	Wei Guangyu, Gan Shu. Research on Elimination of Non-building Points in Airborne LiDAR Building Point Cloud[J]. Value Engineering, 2017, 36(2):31-33.
15	魏广宇, 甘淑. 机载LiDAR建筑物点云中非建筑物点剔除研究[J]. 价值工程, 2017, 36(2):31-33.
16	Tong Lihua, Cheng Liang, Li Manchun, et al. Extraction of Building Contours and Corners from Terrestrial LiDAR Data[J].Journal of Image and Graphics, 2013, 18(7):876-883.
16	童礼华, 程亮, 李满春,等. 地面LiDAR数据中建筑轮廓和角点提取[J]. 中国图象图形学报, 2013, 18(7):876-883.
17	Hu Cheng. Thinning Algorithm of LiDAR Bare Earth Surface Point Cloud Under the Restriction of Precision[D]. Chengdu: Southwest Jiaotong University, 2015.
17	胡诚. 精度约束下地表LiDAR点云抽稀方法研究[D]. 成都：西南交通大学, 2015.
18	Zhao Chuan, Zhang Baoming, Chen Xiaowei, et al. A Method of Extracting Building based on LiDAR Point Clouds[J]. Bulletin of Surveying and Mapping, 2017(2):35-39.
18	赵传, 张保明, 陈小卫,等. 一种基于LiDAR点云的建筑物提取方法[J]. 测绘通报, 2017(2):35-39.
19	Fang Chengxi, Lichun Sui, Zhu Haixiong. LiDAR Point Cloud Thinning Algorithm for Road Survey and Design[J].Bulletin of Surveying and Mapping, 2017(10):58-61.方程喜, 隋立春, 朱海雄. 用于公路勘测设计的LiDAR点云抽稀算法[J]. 测绘通报, 2017(10):58-61.

[1]	梁茜茜，张汉德，孙根云，王鹏. 基于机载激光雷达数据的海岸带水域提取方法[J]. 遥感技术与应用, 2018, 33(1): 136-142.
[2]	吴迪，史文中，高利鹏，张华，何鹏飞. 一种改进的基于上下文信息的多源数据融合目标提取方法[J]. 遥感技术与应用, 2018, 33(1): 128-135.
[3]	卜帆，石玉立. 机载LiDAR高差和高分影像的城市树冠提取比较[J]. 遥感技术与应用, 2017, 32(5): 875-882.
[4]	董保根,马洪超,车森,解龙根,何乔. LiDAR点云支持下地物精细分类的实现方法[J]. 遥感技术与应用, 2016, 31(1): 165-169.
[5]	汪骏,夏少波,王和平,王成,谭弘武. 基于直升机激光点云的分裂导线重建研究[J]. 遥感技术与应用, 2015, 30(6): 1189-1194.
[6]	郑义,汪承义. 基于航空LiDAR数据的桥梁边界线提取方法[J]. 遥感技术与应用, 2014, 29(3): 469-475.
[7]	高利鹏,史文中,吕志勇,张华. 基于机载LiDAR和高分辨率遥感影像的城市道路网提取[J]. 遥感技术与应用, 2013, 28(4): 562-568.
[8]	任秀云,刘立宝,杨君国,王静,田兆硕. 机载脉冲激光雷达剖面测量技术的进展及应用[J]. 遥感技术与应用, 2011, 26(3): 392-398.
[9]	陈磊,赵书河. 一种改进的基于平面拟合的机载LiDAR点云滤波方法[J]. 遥感技术与应用, 2011, 26(1): 117-122.
[10]	王金亮,陈联君. 激光雷达点云数据的滤波算法述评[J]. 遥感技术与应用, 2010, 25(5): 632-638.

Viewed

Full text

Abstract

Cited

Shared

Discussed