Overlapping region extraction method for laser point clouds registration

被引：0
|
作者：
Wang S. [1 ]
Sun H. [2 ]
Guo H. [2 ]
机构：
[1] Department of Postgraduate, The Academy of Equipment, Beijing
[2] Department of Optical and Electrical Equipment, The Academy of Equipment, Beijing
来源：
| 1600年 / Chinese Society of Astronautics卷 / 46期
关键词：
ESF; Laser 3D imaging; Point cloud registration; Spectral clustering;
D O I：
10.3788/IRLA201746.S126002
中图分类号：
学科分类号：
摘要：
Multi-view laser point cloud registration is the basis of three dimension reconstruction, and the extraction of overlapping regions in multi-viewpoint laser point clouds is of great values to improve the efficiency of laser point cloud registration. A method of overlapping regions extraction based on region segmentation was presented, the spectral clustering was used to segment the point clouds of each viewpoint according to the geometric structure, and then a multi-dimensional shape descriptor was created for each region. The Euclidean distances were calculated for each extracted descriptor, the area with nearest Euclidean distance between descriptors was the overlapping area between point clouds. Experiments show that the algorithm is stable to the laser point clouds noise and the initial position, and the algorithm could still complete the extraction of overlapping regions in the case of large differences between point clouds. With the simulated multi-viewpoint point clouds, the overlap ratio increased by an average of 14.3%. And with the actual multi-viewpoint point clouds, the overlap ratio increased by an average of 13.3%. © 2017, Editorial Board of Journal of Infrared and Laser Engineering. All right reserved.
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  • [1] Fan Y., Zhao H., Sun H., Et al., Application of cross-correlation algorithm in moving target range-gated three-dimensional laser active imaging, Infrared and Laser Engineering, 45, 6, (2016)
  • [2] Liu Y., Chen F., Wang Y., Et al., Measurement of relative position and pose between two spacecrafts based on laser lidar, Infrared and Laser Engineering, 45, 8, (2016)
  • [3] Rusu R.B., Blodow N., Beetz M., Fast Point Feature Histograms (FPFH) for 3D registration, IEEE International Conference on Robotics and Automation, pp. 1848-1853, (2009)
  • [4] Wei H., Zhang L., Liu S., Et al., An algorithm on registration of multi-view range images based on SIFT feature matching, Journal of Computer-Aided Design & Computer Graphics, 22, 4, pp. 654-661, (2010)
  • [5] Chen M., Lu W., Wan Y., Et al., Automatic registration of terrestrial point clouds without additional information, Chinese Journal of Lasers, 43, 4, (2016)
  • [6] Dai Y., Zhang A., Research on fast registration of 3D laser scanning data, Bulletin of Surveying and Mapping, 6, pp. 8-11, (2010)
  • [7] Sun N., Research on the key techniques of quad-nocular stereo measurement, (2012)
  • [8] Zhang X., Li Z., Wang X., Et al., Registration of point clouds data based on genetic algorithm, Computer Engineering, 38, 21, pp. 214-217, (2012)
  • [9] Shen Y., Li Z., Yang C., Et al., Point cloud registration with normal feature histogram, Optics and Precision Engineering, 23, 10 z, pp. 591-598, (2015)
  • [10] Liu B., Guo J., Deng X., A point cloud registration method based on Octree and ICP, Science of Surveying & Mapping, 41, 2, pp. 130-132, (2016)
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