A New Variant of the ICP Algorithm for Pairwise 3D Point Cloud Registration

  • Elizeu Martins Oliveira Junior Geodesic Sciences Pos-graduate Program, Paraná Federal University, 100 Francisco H. dos Santos Avenue, Curitiba-PR, 81539-000, Brazil
  • Daniel Rodrigues Santos Cartographic Engineering Section, Military Institute of Engineering, 80 Gen. Tiburcio Square, Rio de Janeiro-RJ, 22290-270, Brazil
  • Giovana Angélica Ross Miola Presidente Prudente Technology Faculty, Informatics Department, 17 Terezinha Street, Presidente Prudente – SP, 19049-230, Brazil
Keywords: Pairwise 3D Point Clouds Registration, Terrestrial Laser Scanner, Planar Surface, ICP

Abstract

Pairwise 3D point cloud registration derived from Terrestrial Laser Scanner (TLS) in static mode is an essential task to produce locally consistent 3D point clouds. In this work, the contributions are twofold. First, a non-iterative scheme by merging the SIFT (Scale Invariant Feature Transform) 3D algorithm and the PFH (Point Feature Histograms) algorithm to find initial approximation of the transformation parameters is proposed. Then, a correspondence model based on a new variant of the ICP (Iterative Closest Point) algorithm to refine the transformation parameters is also proposed. To evaluate the local consistency of the pairwise 3D point cloud registration is used a point-to-distance approach. Experiments were performed using seven pairs of 3D point clouds into an urban area. The results obtained showed that the method achieves point-to-plane RMSE (Root of the Mean Square Error) mean values in the order of 2 centimeters.

References

A. Gressin, C. Mallet, J. Damantké and N. David. “Towards 3D Deal Point Cloud Registration Improvement Using Optimal Neighborhood Knowledge” In ISPRS J. Photogram. Remote Sens, vol. 79, 2013, pp. 240–251.

A. M. Fischler and C. R. Bolles. “Random Sample Consensus: A Paradigm For Model Fitting With Applications To Image Analysis And Automated Cartography ”Communications Of The ACM, vol. 24, pp. 381–395, 1981.

A. Nuchter, K. Lingemann and J. Hertzberg. “Cached KD Tree Search for ICP Algorithms”, In: Proc. Of The IEEE Conference 3-D Digital Imaging and Modeling, 2007, pp. 419-426.

B. K. P. Horn. “Closed-Form Solution Of Absolute Orientation Using Unit Quaternions ”Journal Of The Optical Society Of America, vol. 4, pp. 629–642, 1987.

C. Wei, T. Wu and H. Fu. “Plain-To-Plain Scan Registration Based On Geometric Distributions of Points” in IEEE International Conference on Information and Automation, ICIA 2015, 2015, pp. 1194–1199.

D. Aiger, N. J. Mitra and D. Cohen-Or. “4-Points Congruent Sets for Robust Pairwise Surface Registration ”ACM Transactions on Graphics, vol. 27, pp. 1, 2008.

D. Gibbins. 3D “Target Recognition Using 3-Dimensional Sift Or Curvature Keypoints And Local Spin Descriptors” Defense Applications Of Signal Processing. Kauai, 2009.

D. Lowe. “Distinctive Image Features from Scale-Invariant Keypoints” In: International Journal Of Computer Vision, vol. 60, 2004, pp. 91-110.

D. R. Dos Santos, M. A. Basso, K. Khoshelham, E. de Oliveira, N. L. Pavan and G. Vosselman. "Mapping Indoor Spaces by Adaptive Coarse-to-Fine Registration of RGB-D Data "in IEEE Geoscience and Remote Sensing Letters, vol. 13, 2016, pp. 262-266.

D. W. Eggert, A. Lorusso A. and R. B. Fisher. “Estimating 3-D Rigid Body Transformations: A Comparison Of Four Major Algorithms ”Machine Vision And Applications, Springer Nature, vol. 9, pp. 272-290, 1997

G. Dresch and Dos Santos D. R. “Automatic Assessment of Relative Accuracy of Data Dealing Airborne ”Geodetic Sciences Bulletin [Online], vol. 21, pp. 730–749, 2015.

H. Li, D. Huang, J. M. Morvan, Y. Wang and L. Chen. “Towards 3D Face Recognition In The Real: A Registration-Free Approach Using Fine-Grained Matching Of 3D Keypoint Descriptors ”International Journal Of Computer Vision, vol. 113, pp. 128-142, 2015.

K. H. Bae and D. D. Lichti. “A Method For Automated Registration Of Unorganized Point Clouds ”ISPRS Journal Of Photogrammetry And Remote Sensing, vol. 63, pp. 36–54, 2008.

K. Khoshelham. “Closed-Form Solutions For Estimating A Rigid Motion From Plane Correspondences Extracted From Point Clouds ”ISPRS Journal Of Photogrammetry And Remote Sensing, vol. 114, pp. 78-91, 2016.

K. S. Arun, T. S. Huang and S. D. Blostein. “Least Square Fitting Of Two 3-D Point Sets ”IEEE Trans. Patt. Anal. Intell machine. vol. Pami-9, pp. 698-700, 1987.

N. L. Pavan and D. R. Dos Santos. “An Automatic Method for Registration Of Terrestrial Laser Scanning Data Using Planar Surfaces ”Geodetic Sciences Bulletin, pp. 572–589, 2015.

P. Besl and N. Mckay. “A Method for Registration of 3-D Shapes” in IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 14, 1992, pp. 239–256.

P. Henry, M. Krainin, E. Herbst, X. Ren and D. Fox. “RGB-D Mapping: Using Kinect-Style Depth Cameras For Dense 3D Modeling Of Indoor Environments ”The International Journal Of Robotics Research, vol. 31, pp. 647-663, 2012.

P. W. Theiler, J. D. Wegner and K. Schindler. “Globally Consistent Registration of Terrestrial Laser Scans Via Graph Optimization ”ISPRS Journal Of Photogrammetry And Remote Sensing, vol. 109, pp. 126–138, 2015.

R. B. Rusu, N. Blodow, M. Beetz. “Fast Point Feature Histograms (FPFH) For 3D Registration ”IEEE International Conference on Robotics And Automation, 2009, pp. 3212–3217.

S. Rusinkiewicz and M. Levoy. “Efficient Variants Of The ICP Algorithm” in Proceedings Of Third International Conference On 3D Digital Imaging And Modeling. IEEE Computer Soc., 2001, pp. 145-152.

T. Rabbani, S. Dijkman, V. D. Heuvel and G. Vosselman “An Integrated Approach For Modeling And Global Registration Of Point Clouds ”ISPRS Journal Of Photogrammetry And Remote Sensing. vol. 61, pp. 355-370, 2007.

Y. Chen, and G. Medione. G. “Object Modeling by Registration Of Multiple Range Images. Image and Vision Computing” vol. 10, pp. 145–155, 1992.

Published
2022-01-01
Section
Articles