Point cloud merging and compression of complicated goaf using multi-point laser-scan

被引：0

作者：

Xiong, Li-Xin ^{[1
]}

Luo, Zhou-Quan ^{[1
]}

Luo, Zhen-Yan ^{[1
]}

Qi, Fei-Xiang ^{[1
]}

机构：

[1] School of Resource and Safety Engineering, Central South University, Changsha

来源：

Beijing Keji Daxue Xuebao/Journal of University of Science and Technology Beijing | 2014年 / 36卷 / 09期

关键词：

Compression; Goaf; Merging; Multipoint scan; Point cloud data;

D O I：

10.13374/j.issn1001-053x.2014.09.002

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In view of the problems of 'blind spots' in complicated goaf detecting by using laser scanning and point cloud density distribution inhomogeneity, this article introduced multi-point laser scan and point cloud merging and compression. Multi-point scan in complicated goaf avoided 'blind spots' and densified sparse point cloud regions. The merging algorithm of point cloud data was put forward based on a common coordinate system and the least-squares principle to solve the target transformation matrix. After the distribution rule of point cloud concentration areas was analyzed, the scattered point cloud compression algorithm was proposed, in which the point cloud was divided into portions along the y direction firstly, then intralayer data were divided by the extreme values of x and z, and each point was sorted on the x value and screened on step k. Error analysis of an instance of large versed goaf shows that the merging algorithm based on the least-squares principle will achieve high precision with an error range of about 0.1 mm. The compression algorithm can achieve a compression proportion of 15% to 25% and ensure the integrity of 3D boundary information at the same time.

引用

页码：1136 / 1142

页数：6

共 18 条

[1]

Peng Y., Liu S.G., Compression and segmentation technology of point cloud data in reverse engineering, 7, 2, (2013)

[2]

Zhang H.F., Cheng X.J., Liu Y.P., Study on improved point cloud compression algorithm with features reserved, 63, 2, (2011)

[3]

Smith M., Posner I., Newman P., Adaptive compression for 3D laser data, 30, 7, (2011)

[4]

Friedman S., Stamos I., Online detection of repeated structures in point clouds of urban scenes for compression and registration, 102, 1, (2013)

[5]

Shen H.P., Da F.P., Lei J.Y., Research of point-clouds registration based on least-square method, 10, 9, (2005)

[6]

Sheng Y.H., Zhang K., Zhang K., Et al., Seamless multi-station merging of terrestrial laser scanned 3D point clouds, 29, 2, (2010)

[7]

Zhang S.S., Zheng C.Z., Xiao S.B., Et al., Realization of point cloud data combination based on openGL function in MFC, 2, (2006)

[8]

Wang R., Zhou M.Q., Xing Y.H., Reduction algorithm for 3D scattered points cloud data based on clustering plane feature, 37, 10, (2011)

[9]

Zhou Y., Lei Y., Du F.R., Et al., Algorithm of scattered point cloud data reduction based on non-uniform subdivision, 40, 9, (2009)

[10]

Xie Z.X., Xu S., Li X.Y., A fine registration method for 3D point clouds in reverse engineering, 20, 13, (2009)

← 1 2 →