Registration method for grid point cloud based on improved FPFH

ZHANG Ruxiao; FANG Yu; YANG Hao; HANG Guanrong; TAO Hanzhong; NING Yeyan

doi:10.12299/jsues.21-0086

Volume 37 Issue 4

Dec. 2023

Turn off MathJax

Article Contents

Article Navigation > Journal of Shanghai University of Engineering Science > 2023 > 37(4): 414-419

ZHANG Ruxiao, FANG Yu, YANG Hao, HANG Guanrong, TAO Hanzhong, NING Yeyan. Registration method for grid point cloud based on improved FPFH[J]. Journal of Shanghai University of Engineering Science, 2023, 37(4): 414-419. doi: 10.12299/jsues.21-0086

Citation:

ZHANG Ruxiao, FANG Yu, YANG Hao, HANG Guanrong, TAO Hanzhong, NING Yeyan. Registration method for grid point cloud based on improved FPFH[J]. Journal of Shanghai University of Engineering Science, 2023, 37(4): 414-419. doi: 10.12299/jsues.21-0086

Citation:

PDF( 1310 KB)

Registration method for grid point cloud based on improved FPFH

doi: 10.12299/jsues.21-0086

1.
School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2.
Shanghai Institute of Space Propulsion, Shanghai 201112, China

Received Date: 2022-05-19
Publish Date: 2023-12-30

Abstract

Abstract

An improved fast point feature histogram (FPFH) was proposed for the initial registration of grid point cloud. The neighborhood density was added as a point feature description on the basis of FPFH. When searching the corresponding points with k-dimensional tree (k-d tree), the Bhattacharyya distance was used as the index. This improved method was compared with FPFH in the simulation. Then, the test device was built to collect the grid’s surface point cloud data. The data were registered with improved FPFH method and iterative closest point (ICP). Test results show that the precision level of the matched point cloud is 1 μm and the error range of grid hole stays in ±(20~40) μm, which theoretically satisfy the precision requirement of the grid.
- grid,
- automatic measurement,
- point cloud registration,
- fast point feature histogram (FPFH),
- neighborhood density,
- Bhattacharyya distance

FullText(HTML)

References(10)

References

[1]	BESL P J, MCKAY H D. A method for registration of 3-D shapes[J] . IEEE Transactions on Pattern Analysis & Machine Intelligence,1992,14(2):239 − 256.
[2]	REN Z L, WANG L G, BI L. Robust GICP-Based 3D LiDAR SLAM for underground mining environment[J] . Sensors,2019,19(13):2915. doi: 10.3390/s19132915
[3]	王立玲, 苏华强, 马东. 基于ICP匹配和贝叶斯逆传感器模型的地图构建[J] . 激光杂志,2020,41(12):50 − 56.
[4]	吴义山, 徐增波. 基于相移结构光重建的三维数字化服装建模[J] . 上海工程技术大学学报,2015,29(4):353 − 359.
[5]	罗锦鸿, 陈新度, 吴磊. 基于点云配准的曲面三维差异检测[J] . 组合机床与自动化加工技术,2021(1):96 − 100. doi: 10.13462/j.cnki.mmtamt.2021.01.023
[6]	姚敏, 赵增浩, 赵敏, 等. 复杂结构件内部缺陷检测[J] . 仪器仪表学报,2020,41(10):213 − 220.
[7]	陶谦, 熊风光, 刘涛, 等. 多幅点云数据与纹理序列间的自动配准方法[J] . 计算机工程,2020,46(10):259 − 265, 274.
[8]	宫文. 光栅投影旋转测量中重叠点云消冗与纹理映射研究[D]. 绵阳: 西南科技大学, 2020.
[9]	RUSU R B, BLODOW N, BEETZ M. Fast point feature histograms (FPFH) for 3D registration[C]//Proceeding of IEEE International Conference on Robotics and Automation. Kobe: IEEE, 2009: 3212−3217.
[10]	朱正伟, 祝磊, 饶鹏. 基于BS-HMM和巴式距离的手势识别技术研究[J] . 计算机应用与软件,2019,36(6):163 − 166, 253.