Skip to main content
SpringerLink
Log in

Toward a robust and fast real-time point cloud registration with factor analysis and Student’s-t mixture model

  • Special Issue Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Three-dimensional (3D) point cloud registration generally involves in unsatisfied situations like Gaussian white noise, data missing and disorder in affine. This paper proposes a robust and real-time point cloud registration, which combines the Student’s-t mixture model (SMM) with factor analysis. The proposed method extending the point cloud mathematical model to the orthogonal factor model and employs the SMM to fit the point cloud data, because the degree of freedom of Student’s t-distribution makes it more flexible in fitting the probability distribution of data. Since the Expectation Maximization (EM) algorithm has a stable estimation ability for the mixture model, the EM algorithm is used to estimate the factor load matrix. The filed data and experimental results show that the proposed algorithm can achieve accurate registration and fast convergence even in the case of point cloud disorder, data occlusion, incomplete loss and noise.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Aoki, Y., Goforth, H., Rangaprasad, A.S., Lucey, S.: Pointnetlk: robust & efficient point cloud registration using pointnet. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) arXiv:1903.05711 [cs.CV] (2019)

  2. Brea, V., Ginhac, D., Berry, F., Kleihorst, R.: Special issue on advances on smart camera architectures for real-time image processing. J. Real-Time Image Process. (2018). https://doi.org/10.1007/s11554-018-0764-1

    Article  Google Scholar 

  3. Campbell, D., Petersson, L.: Gogma: globally-optimal gaussian mixture alignment. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5685–5694 (2016). https://doi.org/10.1109/CVPR.2016.613

  4. Charles, R., Su, H., Kaichun, M., Guibas, L.: Pointnet: deep learning on point sets for 3D classification and segmentation, In: Computer Vision and Pattern Recognition (CVPR), pp. 77–85 (2017). https://doi.org/10.1109/CVPR.2017.16

  5. Dimitris Panaretos George Tzavelas, M.V., Panagiotakos, D.: Investigating the role of orthogonal and non - orthogonal rotation in multivariate factor analysis, in regard to the repeatability of the extracted factors: a simulation study. Commun. Stat. Simul. Comput. 48(7), 2165–2176 (2019). https://doi.org/10.1080/03610918.2018.1435803

    Article  MathSciNet  Google Scholar 

  6. Ge, X.: Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets. ISPRS J. Photogramm. Remote Sens. 130, 344–357 (2017). https://doi.org/10.1016/j.isprsjprs.2017.06.011

    Article  Google Scholar 

  7. Ge, Y., Wang, B., Nie, J., Sun, B.: A point cloud registration method combining enhanced particle swarm optimization and iterative closest point method. In: Chinese Control and Decision Conference (CCDC), pp. 2810–2815 (2016). https://doi.org/10.1109/CCDC.2016.7531460

  8. Han, L., Xu, L., Bobkov, D., Steinbach, E., Fang, L.: Real-time global registration for globally consistent rgb-d slam. IEEE Trans. Robot. 35(2), 498–508 (2019). https://doi.org/10.1109/TRO.2018.2882730

    Article  Google Scholar 

  9. He, M., Liu, M., Wang, R., Jiang, X., Liu, B., Zhou, H.: Particle swarm optimization with damping factor and cooperative mechanism. Appl. Soft Comput. 76, 45–52 (2018). https://doi.org/10.1016/j.asoc.2018.11.050

    Article  Google Scholar 

  10. He, S.J., Zhao, S.T., Bai, F., Wei, J.: A method for spatial data registration based on pca-icp algorithm. Adv. Mater. Res. 718, 1033–1036 (2013). https://doi.org/10.4028/www.scientific.net/AMR.718-720.1033

    Article  Google Scholar 

  11. Jauer, P., Kuhlemann, I., Bruder, R., Schweikard, A., Ernst, F.: Efficient registration of high-resolution feature enhanced point clouds. IEEE Trans. Pattern Anal. Mach. Intell. 41(5), 1102–1115 (2019). https://doi.org/10.1109/TPAMI.2018.2831670

    Article  Google Scholar 

  12. Jian, B., Vemuri, B.: Robust point set registration using gaussian mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1633–1645 (2011). https://doi.org/10.1109/TPAMI.2010.223

    Article  Google Scholar 

  13. Kleppe, A., Tingelstad, L., Egeland, O.: Coarse alignment for model fitting of point clouds using a curvature-based descriptor. IEEE Trans. Autom. Sci. Eng. pp, 1–14 (2018). https://doi.org/10.1109/TASE.2018.2861618

    Article  Google Scholar 

  14. Li, Q., Xiong, R., Vidal-Calleja, T.: A gmm based uncertainty model for point clouds registration. Robot. Auton. Syst. 91, 349–362 (2017). https://doi.org/10.1016/j.robot.2016.11.021

    Article  Google Scholar 

  15. Myronenko, A., Song, X.: Point set registration: Coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32, 2262–2275 (2010). https://doi.org/10.1109/TPAMI.2010.46

    Article  Google Scholar 

  16. Paul, J.B.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992). https://doi.org/10.1109/34.121791

    Article  MathSciNet  Google Scholar 

  17. Persad, R.A., Armenakis, C.: Automatic co-registration of 3D multi-sensor point clouds. ISPRS J. Photogramm. Remote Sens. 130, 162–186 (2017). https://doi.org/10.1016/j.isprsjprs.2017.05.014

    Article  Google Scholar 

  18. Prakhya, S.M., Liu, B., Lin, W., Jakhetiya, V., Guntuku, S.C.: B-shot: a binary 3D feature descriptor for fast keypoint matching on 3D point clouds. Auton. Rob. 41, 1501–1520 (2017). https://doi.org/10.1007/s10514-016-9612-y

    Article  Google Scholar 

  19. Qi, L., Dou, W., Wang, W., Li, G., Yu, H., Wan, S.: Dynamic mobile crowdsourcing selection for electricity load forecasting. IEEE Access 6, 46926–46937 (2018). https://doi.org/10.1109/Access.2018.2866641

    Article  Google Scholar 

  20. Qi, L., Meng, S., Zhang, X., Wang, R., Xu, X., Zhou, Z., Dou, W.: An exception handling approach for privacy-preserving service recommendation failure in a cloud environment. Sensors 18, 2037 (2018). https://doi.org/10.3390/s18072037

    Article  Google Scholar 

  21. Qi, L., Zhang, X., Dou, W., Hu, C., Yang, C., Chen, J.: A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment. Future Gener. Comput. Syst. Int. J. eSci. 88, 636–643 (2018). https://doi.org/10.1016/j.future.2018.02.050

    Article  Google Scholar 

  22. Quan, S., Ma, J., Hu, F., Fang, B., Ma, T.: Local voxelized structure for 3D binary feature representation and robust registration of point clouds from low-cost sensors. Inf. Sci. 444, 153–171 (2018). https://doi.org/10.1016/j.ins.2018.02.070

    Article  Google Scholar 

  23. Sarode, V., Li, X., Goforth, H., Aoki, Y., Rangaprasad, A.S., Lucey, S., Choset, H.: Pcrnet: point cloud registration network using pointnet encoding. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). arXiv:1908.07906 [cs.CV] (2019)

  24. Schaffert, R., Wang, J., Fischer, P., Borsdorf, A., Maier, A.: Metric-driven learning of correspondence weighting for 2-d/3-d image registration. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). arXiv:1806.07812 [cs.CV] (2018)

  25. Sharp, G.C., Lee, S.W., Wehe, D.K.: Icp registration using invariant features. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 90–102 (2002). https://doi.org/10.1109/34.982886

    Article  Google Scholar 

  26. Sui, Y., Qin, Z., Tong, X., Li, H., Lai, G.: A cumulative projection-based automatic registration method for mobile laser scanning data. Remote Sens. Lett. 10(1), 86–94 (2019). https://doi.org/10.1080/2150704X.2018.1523582

    Article  Google Scholar 

  27. Turk, G., Levoy, M.: Zippered polygon meshes from range data[J]. Comput. Graph. (1994). https://doi.org/10.1145/192161.192241 (2005)

    Article  Google Scholar 

  28. Villa, C., Rubio, F.J.: Objective priors for the number of degrees of freedom of a multivariate t distribution and the t-copula. Comput. Stat. Data Anal. 124, 197–219 (2018). https://doi.org/10.1016/j.csda.2018.03.010

    Article  MathSciNet  MATH  Google Scholar 

  29. Wang, R., Ji, W., Liu, M., Wang, X., Weng, J., Deng, S., Gao, S., Yuan, C.: Review on mining data from multiple data sources. Pattern Recogn. Lett. 109(120–128), 109 (2018). https://doi.org/10.1016/j.patrec.2018.01.013

    Article  Google Scholar 

  30. Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: A deep representation for volumetric shapes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1912–1920 (2015). https://doi.org/10.1109/CVPR.2015.7298801

  31. Yang, J., Li, H., Campbell, D., Jia, Y.: Go-ICP: a globally optimal solution to 3D ICP point-set registration. IEEE Trans. Pattern Anal. Mach. Intell. 38(11), 2241–2254 (2016). https://doi.org/10.1109/TPAMI.2015.2513405

    Article  Google Scholar 

  32. Yang, J., Li, H., Jia, Y.: Go-ICP: Solving 3D registration efficiently and globally optimally. In: IEEE International Conference on Computer Vision, pp. 1457–1464 (2013). https://doi.org/10.1109/ICCV.2013.184

  33. Ying, S., Peng, J., Du, S., Qiao, H.: A scale stretch method based on ICP for 3D data registration. IEEE Trans. Autom. Sci. Eng. 6(3), 559–565 (2009). https://doi.org/10.1109/TASE.2009.2021337

    Article  Google Scholar 

  34. Yu, C., Ju, D.Y.: A maximum feasible subsystem for globally optimal 3D point cloud registration. Sensors 18, 544 (2018). https://doi.org/10.3390/s18020544

    Article  Google Scholar 

  35. Zaganidis, A., Sun, K., Duckett, T., Cielniak, G.: Integrating deep semantic segmentation into 3-D point cloud registration. IEEE Robot. Autom. Lett. (2018). https://doi.org/10.1109/LRA.2018.2848308

    Article  Google Scholar 

  36. Zhu, J., Jin, C., Jiang, Z., Xu, S., Xu, M., Pang, S.: Robust point cloud registration based on both hard and soft assignments. Opt. Laser Technol. 110(SI), 202–208 (2019). https://doi.org/10.1016/j.optlastec.2018.07.072

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (U19A2086), and SKLGP2019Z014.

Author information

Authors and Affiliations

  1. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China

    Zhirong Tang, Mingzhe Liu & Feixiang Zhao

  2. College of Earth Sciences, Chengdu University of Technology, Chengdu, China

    Shaoda Li

  3. Institute of Natural and Computational Sciences, Massey University, Auckland, New Zealand

    Ming Zong

Authors
  1. Zhirong Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingzhe Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Feixiang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaoda Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ming Zong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhirong Tang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, Z., Liu, M., Zhao, F. et al. Toward a robust and fast real-time point cloud registration with factor analysis and Student’s-t mixture model. J Real-Time Image Proc 17, 2005–2014 (2020). https://doi.org/10.1007/s11554-020-00964-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-020-00964-1

Keywords

Search

Navigation