A method of point cloud data block registration with considering distance from point to surface

• Autorzy: Lu Yinju , Duan Mingyi , Dai Shuguang

Identyfikatory

DOI

10.24425/bpasts.2022.140259

• Języki publikacji: EN

Abstrakty

EN

The number of scanner stations used to acquire point cloud data is limited, resulting in poor data registration. As a result, a cloud point block registration approach was proposed that took into account the distance between the point and the surface. When registering point cloud data, the invariant angle, length, and area of the two groups of point cloud data were affine transformed, and then the block registration parameters of point cloud data were determined. A finite hybrid model of point cloud data was created based on the coplane four-point nonuniqueness during the affine translation. On this basis, the point cloud data block registration algorithm was designed. Experimental results prove that the proposed method has great advantages in texture alignment, registration accuracy and registration time, so it is able to effectively improve the registration effect of point cloud data. The point cloud data block registration algorithm was built on this foundation. Experiments show that the suggested method has significant improvements in texture alignment, registration accuracy, and registration time, indicating that it can significantly improve point cloud data registration.

Słowa kluczowe

EN

point cloud registration; data block registration; distance from point to surface; parameter calculation

PL

rejestracja chmury punktów; rejestracja bloku danych; odległość od punktu do powierzchni; obliczanie parametrów

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2022
• Tom: Vol. 70, nr 2
• Strony: art. no. e140259
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Lu Yinju

• School of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
• School of Information Engineering, Zhengzhou Institute of Technology, Zhengzhou 450044, China

autor

Duan Mingyi

• School of Information Engineering, Zhengzhou Institute of Technology, Zhengzhou 450044, China

autor

Dai Shuguang

• School of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Bibliografia

• [1] K. Zampogiannis, C. Fermuller, and Y. Aloimonos, “Topologyaware non-rigid point cloud registration”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 99, pp. 1–12, 2019.
• [2] E. Renaudin, A. Habib, and A.P. Kersting, “Featured-based registration of terrestrial laser scans with minimum overlap using photogrammetric data”, ETRI J., vol. 33, no. 4, pp. 527, 2011.
• [3] S. Chen, L. Nan, R. Xia, and Z.B. Zhao, “PLADE: A planebased descriptor for point cloud registration with small overlap”, IEEE Trans. Geosci. Remote Sens., vol. 58, no. 4, pp. 2530–2540, 2020.
• [4] L.M. Zhou, S.Y. Zheng, and R.Y. Huang, “A registration algorithm for point clouds obtained by scanning objects on turntable”, Acta Geod. Cartograph. Sinica, vol. 42, no. 1, pp. 73–79, 2013.
• [5] X.Y. Liu, “Point-cloud data segmentation based on fuzzy maximum likelihood estimate clustering”, Comput. Eng., vol. 36, no. 6, pp. 86–88, 2010.
• [6] X. Wang, Y.D. Zhao, and J. Wang, “A registration method of laser point cloud with low overlap”, Sci. Surv. Mapp., vol. 43, no. 12, pp. 130–136, 2018.
• [7] J.H. Xiao, B. Adler, J.W. Zhang, and H.X. Zhang, “Planar segment based three-dimensional point cloud registration in outdoor environments”, J. Field Robot., vol. 30, no. 4, pp. 552–582, 2013.
• [8] W.M. Zhang, and Y.X. Zhang, “The reactive power and voltage control management strategy based on virtual reactance cloud control”, Arch. Electr. Eng., vol. 69, no. 4, pp. 921–936, 2020.
• [9] Y.R. Hou, “Optimal error estimates of a decoupled scheme based on two-grid finite element for mixed stokes-darcy model”, Appl. Math. Lett., vol. 57, no. 12, pp. 90–96, 2016.
• [10] J. Wang, S. Huo, Y. Liu, R. Li, and Z. Liu, “Research of fast point cloudregistration method in construction erroranalysis of hull blocks”, Int. J. Nav. Archit. Ocean Eng., vol. 12, pp. 605–616, 2020.
• [11] Y. Wang, J. Xiao, L. Liu, Y. Wang, “Efficient rock mass point cloud registration based on local invariants”, Remote Sens., vol. 13, pp. 1–19, 2021. doi: 10.3390/rs13081540.
• [12] M. Zaborowski, “Data processing in self-controlling enterprise processes”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 1, pp. 1–18, 2019.
• [13] F. Wang, J. Xiao, and Y. Wang, “Efficient rock-mass point cloud registration using n-point complete graphs”, IEEE Trans. Geosci. Remote Sens., vol. 18, no. 99, pp. 1–12, 2019, doi: 10.1109/TGRS.2019.2926201.
• [14] J. Li, H. Liu, and L. Rondi, “Regularized transformation-optics cloaking for the Helmholtz equation: from partial cloak to full cloak”, Commun. Math. Phys., vol. 335, no. 2, pp. 671–712, 2015.
• [15] N. Vaysfeld and Z. Zhuravlova, “On one new approach to the solving of an elasticity mixed plane problem for the semistrip”, Acta Mech., vol. 226, no. 12, pp. 4159–4172, 2015, doi: 10.1007/s00707-015-1452-x.
• [16] H. Hong and B.H. Lee, “Key-layered normal distributions transform for point cloud registration”, Electr. Lett., vol. 51, no. 24, pp. 1986–1988, 2015.
• [17] L. Han, L. Xu, and D. Bobkov, “Real-time global registration for globally consistent RGB-D SLAM”, IEEE Trans. Robot., vol. 16, no. 2, pp. 1–11, 2019.
• [18] X. Ge and B.Wu, “Contextual global registration of point clouds in urban scenes”, Photogramm. Eng. Remote Sens., vol. 85, no. 8, pp. 559–571, 2019.
• [19] F. Pomerleau, M. Liu, and F. Colas, “Challenging data sets for point cloud registration algorithms”, Int. J. Robot. Res., vol. 31, no. 14, pp. 1705–1711, 2012.
• [20] X. Huang, J. Zhang, and L. Fan, “A systematic approach for cross-source point cloud registration by preserving macro and micro structures”, IEEE Trans. Image Process., vol. 25, no. 7, pp. 1–15, 2017.
• [21] T.R. Yugo et al., “3D reconstruction and multiple point cloud registration using a low precision RGB-D sensor”, Mechatronics, vol. 35, no. 1, pp. 11–22, 2016, doi: 10.1016/j.mechatronics.2015.10.014.
• [22] G.K. Saini et al., “Recognition of human sentiment from image using machine learning”, Ann. Rom. Soc. Cell Biol., vol. 25, no. 5, pp. 1802–1808, 2021.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).