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Robust registration algorithm based on rational quadratic kernel for point sets with outliers and noise

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Abstract

This paper proposes a new rigid registration algorithm based on the rational quadratic kernel to align point sets with outliers and noise. First of all, the multi-source point sets may contain a lot of outliers and noise and the traditional registration algorithm cannot handle the outliers and noise efficiently, this paper introduces the rational quadratic kernel to the rigid registration problem, which can resist outliers and suppress noise to improve the registration accuracy. Secondly, based on the new registration model, we present an iterative closest point (ICP) algorithm and use Lagrange multiplier and the singular value decomposition (SVD) to compute the rigid transformation. Moreover, the effect of the parameter is discussed detailly and a useful parameter control method is introduced to increase the accuracy and robustness of registration. A series of experiments on simulations and real data demonstrate that the proposed algorithm is more precise and robust than other algorithms.

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References

  1. Ahmed SM, Das NR, Chaudhury KN (2019) Least-squares registration of point sets over SE (d) using closed-form projections. Comput Vis Image Underst 183:20–32

    Article  Google Scholar 

  2. Alliez P, Meyer M, Desbrun M (2002) Interactive geometry remeshing. ACM Trans Graph 21:347–354

    Article  Google Scholar 

  3. Arun KS (1987) Least-squares fitting of two 3-D point sets. IEEE Trans Pattern Anal Mach Intell 9(5):698–700

    Article  Google Scholar 

  4. Balakrishnan G, Zhao A, Sabuncu MR, Guttag J, Dalca AV (2019) VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans Med Imaging 38(8):1788–1800

    Article  Google Scholar 

  5. Besl PJ, McKay ND (1992) Method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):193–200

    Article  Google Scholar 

  6. Choy C, Dong W, Koltun V (2020) Deep global registration. In: IEEE conference on computer vision and pattern recognition (CVPR), 2511–2520

  7. Du S, Liu J, Zhang C, Zhu J, Li K Probability iterative closest point algorithm for m-D point set regsitarion with noise. Neurocomputing, 157: 187–198

  8. Du SY, Xu GL, Zhang SR, Zhang XT, Gao Y, Chen BD (2020) Robust rigid registration algorithm based on pointwise correspondence and correntropy. Pattern Recogn Lett 132:91–98

    Article  Google Scholar 

  9. Dym N, Kovalsky SZ (2019) Linearly converging quasi branch and bound algorithms for global rigid registration. In: IEEE International Conference on Computer Vision (ICCV), 1628–1636

  10. Ferrante E, Paragios N (2017) Slice-to-volume medical image registration: A survey. Med Image Anal 39:101–123

    Article  Google Scholar 

  11. Fiocco M, Bostrom G, Gonçalves JGM, Sequeira V (2005) Multisensor fusion for volumetric reconstruction of large outdoor areas. In: IEEE fifth international conference on 3-D digital imaging and modeling, (3DIM), 47–54

  12. Genton MG (2002) Classes of kernels for machine learning: a statistics perspective. J Mach Learn Res 2:299–312

    MathSciNet  MATH  Google Scholar 

  13. Gold S, Rangarajan A, Lu CP, Pappu S, Mjolsness E (1998) New algorithms for 2D and 3D point matching: pose estimation and correspondence. Pattern Recogn 31(8):1019–1031

    Article  Google Scholar 

  14. Granger S, Pennec X (2002) Multi-scale EM-ICP: a fast and robust approach for surface registration. In: European conference on computer vision (ECCV), 418–432

  15. Jian B, Vemuri BC (2005) A robust algorithm for point set registration using mixture of Gaussians. IEEE International Conference on Computer Vision (ICCV) 2:1246–1251

    Google Scholar 

  16. Jian B, Vemuri BC (2010) Robust point set registration using gaussian mixture models. IEEE Trans Pattern Anal Mach Intell 33(8):1633–1645

    Article  Google Scholar 

  17. Kalman D (1996) A singularly valuable decomposition: the SVD of a matrix. Coll Math J 27(1):2–23

    Article  MathSciNet  Google Scholar 

  18. Kumar M, Diwakar M (2018) A review on CT image noise and its denoising. Biomed Signal Process Control 42(4):73–88

    Google Scholar 

  19. Kumar M, Diwakar M (2018) CT image denoising using NLM and correlation-based wavelet packet thresholding. IET Image Process 12(5):708–715

    Article  Google Scholar 

  20. Latecki LJ, Lakamper R, Eckhardt T (2000) Shape descriptors for non-rigid shapes with a single closed contour. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1:424–429

    Google Scholar 

  21. Li D, Chen F (2019) DeepMapping: unsupervised map estimation from multiple point clouds. In: IEEE conference on computer vision and pattern recognition (CVPR), 8642–8651

  22. Lu WX, Wan GW, Zhou Y, Fu XY, Yuan PF, Song SY (2019) DeepVCP: an end-to-end deep neural network for point cloud registration. In: IEEE international conference on computer vision (ICCV), 12–21

  23. Ma J, Jiang J, Zhou H, Zhao J, Guo XJ (2018) Guided locality preserving feature matching for remote sensing image registration. IEEE Trans Geosci Remote Sens 56(8):4435–4447

    Article  Google Scholar 

  24. Ma W, Wen Z, Wu Y, Jiao LC, Gong MG, Zheng YF, Liu L (2016) Remote sensing image registration with modified SIFT and enhanced feature matching. IEEE Geosci Remote Sens Lett 14(1):3–7

    Article  Google Scholar 

  25. Myronenko A, Song X (2010) Point set registration: coherent point drift. IEEE Trans Pattern Anal Mach Intell 32(12):2262–2275

    Article  Google Scholar 

  26. Nüchter A, Kai L, Hertzberg J (2007) Cached k-d tree search for ICP algorithms. In: sixth international conference on 3-d digital imaging and modeling (3DIM), 21–23

  27. Pais GD, Ramalingam S, Govindu VM, Nascimento JC, Chellappa R, Miraldo P (2020) 3DRegNet: a deep neural network for 3D point registration. In: IEEE conference on computer vision and pattern recognition (CVPR), 7191–7201

  28. Phillips JM, Liu R, Tomasi C (2007) Outlier robust ICP for minimizing fractional RMSD. In: IEEE sixth international conference on 3-D digital imaging and modeling (3DIM), 427–434

  29. Ren MJ, Sun LJ, Liu MY, Chi FC, Yue HY (2017) A reconstruction registration integrated data fusion method for measurement of multiscaled complex surfaces. IEEE Trans Instrum Meas 66(3):414–423

    Article  Google Scholar 

  30. Sain SR (1996) The nature of statistical learning theory. Technometrics 38(4):409–409

    Article  Google Scholar 

  31. Sun J, Sun ZL, Lam KM, Lam KM, Zeng ZG (2020) A robust point set registration approach with multiple effective constraints. IEEE Trans Ind Electron 67(12):10931–10941

    Article  Google Scholar 

  32. Tsin Y, Kanade T (2004) A correlation-based approach to robust point set registration. In: European conference on computer vision (ECCV), 558–569

  33. Vapnik VN (1999) Statistical learning theory. Technometrics 41(4):371–389

    Google Scholar 

  34. Vongkulbhisal J, Irastorza Ugalde B, De la Torre F, Costeira JP (2018) Inverse composition discriminative optimization for point cloud registration. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2993–3001

  35. Wan T, Du SY, Cui WT, Yao RZ, Ge YY, Gao Y, Zheng NN (2021) RGB-D point cloud registration based on salient object detection. IEEE transactions on neural networks and learning systems, 1–12

  36. Yang H, Carlone L (2019) A quaternion-based certifiably optimal solution to the wahba problem with outliers. In: IEEE International Conference on Computer Vision (ICCV), 1665–1674.

  37. Yang Y, Fan DD, Du SY, Chen BD, and Gao Y (2019) Point set registration with similarity and affine transformations based on bidirectional KMPE loss. IEEE transactions on cybernetics, 1–12

  38. Yang J, Li H, Jia Y (2013) Go-ICP: solving 3D registration efficiently and globally optimally. In: IEEE international conference on computer vision (ICCV), 1457–1464

  39. Yang H, Shi JN, Carlone L (2020) TEASER: Fast and certifiable point cloud registration. IEEE Transactions on Robotics. https://arxiv.org/abs/2001.07715v2

  40. Ye Y, Shan J, Bruzzone L, Li S (2017) Robust registration of multimodal remote sensing images based on structural similarity. IEEE Trans Geosci Remote Sens 55(5):2941–2958

    Article  Google Scholar 

  41. Yew XJ, Lee GH (2020) RPM-net: robust point matching using learned features. In: IEEE conference on computer vision and pattern recognition (CVPR), 11821–11830

  42. Zhang X, Li L, Tu D (2013) Point cloud registration with 2D and 3D fusion information on mobile robot integrated vision system. In: IEEE international conference on robotics and Biomimetics (ICRB), 1187–1192

  43. Zhou QY, Park J, Koltun V (2016) Fast global registration. European Conference on Computer Vision (ECCV) 9906:766–782

    Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant Nos. 61971343, 61627811 and 61866022, and the Fundamental Research Funds for the Central Universities under Grant No. xzy022020052.

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Authors and Affiliations

  1. Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi’an Jiaotong University, Xi’an, Shaanxi Province, 710049, People’s Republic of China

    Runzhao Yao, Shaoyi Du, Teng Wan & Wenting Cui

  2. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi Province, 710049, People’s Republic of China

    Yang Yang & Yang Jing

  3. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, Gansu Province, 730050, People’s Republic of China

    Ce Li

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  1. Runzhao Yao
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Correspondence to Shaoyi Du.

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Yao, R., Du, S., Wan, T. et al. Robust registration algorithm based on rational quadratic kernel for point sets with outliers and noise. Multimed Tools Appl 80, 27925–27945 (2021). https://doi.org/10.1007/s11042-021-10851-x

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