Parallelized Iterative Closest Point for Autonomous Aerial Refueling

  • Jace Robinson
  • Matt Piekenbrock
  • Lee Burchett
  • Scott Nykl
  • Brian Woolley
  • Andrew Terzuoli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10072)


The Iterative Closest Point algorithm is a widely used approach to aligning the geometry between two 3 dimensional objects. The capability of aligning two geometries in real time on low-cost hardware will enable the creation of new applications in Computer Vision and Graphics. The execution time of many modern approaches are dominated by either the k nearest neighbor search (kNN) or the point alignment phase. This work presents an accelerated alignment variant which utilizes parallelization on a Graphics Processing Unit (GPU) of multiple kNN approaches augmented with a novel Delaunay Traversal to achieve real time estimates.


Point Cloud Graphic Processing Unit Near Neighbor Delaunay Triangulation Iterative Close Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like to thank our sponsor, AFRL/RQ for their support in this research.


  1. 1.
    Owens, J.D., Houston, M., Luebke, D., Green, S., Stone, J.E., Phillips, J.C.: GPU computing. Proc. IEEE 96, 879–899 (2008)CrossRefGoogle Scholar
  2. 2.
    Nvidia, C.: Compute unified device architecture programming guide (2007)Google Scholar
  3. 3.
    Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. In: Robotics-DL Tentative, pp. 586–606. International Society for Optics and Photonics (1992)Google Scholar
  4. 4.
    Mount, D.M.: GTS: GNU Triangulated Surface library (2000–2004)Google Scholar
  5. 5.
    Rusu, R.B., Cousins, S.: 3D is here: point cloud library (PCL). In: ICRA. IEEE (2011)Google Scholar
  6. 6.
    Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18, 509–517 (1975)CrossRefMATHGoogle Scholar
  7. 7.
    Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, pp. 604–613. ACM (1998)Google Scholar
  8. 8.
    Gieseke, F., Heinermann, J., Oancea, C., Igel, C.: Buffer kd trees: processing massive nearest neighbor queries on GPUs. In: Proceedings of The 31st International Conference on Machine Learning, pp. 172–180 (2014)Google Scholar
  9. 9.
    Garcia, V., Debreuve, E., Barlaud, M.: Fast k nearest neighbor search using GPU. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2008, pp. 1–6. IEEE (2008)Google Scholar
  10. 10.
    Li, S., Amenta, N.: Brute-force k-nearest neighbors search on the GPU. In: Amato, G., Connor, R., Falchi, F., Gennaro, C. (eds.) SISAP 2015. LNCS, vol. 9371, pp. 259–270. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-25087-8_25 CrossRefGoogle Scholar
  11. 11.
    Eggert, D., Dalyot, S.: Octree-based SIMD strategy for ICP registration and alignment of 3D point clouds. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 3, 105–110 (2012)CrossRefGoogle Scholar
  12. 12.
    Abe, L.I., Iwao, Y., Gotoh, T., Kagei, S., Takimoto, R.Y., Tsuzuki, M., Iwasawa, T.: High-speed point cloud matching algorithm for medical volume images using 3D voronoi diagram. In: 2014 7th International Conference on Biomedical Engineering and Informatics, pp. 205–210. IEEE (2014)Google Scholar
  13. 13.
    Green, P.J., Sibson, R.: Computing dirichlet tessellations in the plane. Comput. J. 21, 168–173 (1978)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Mcke, E.P., Saias, I., Zhu, B.: Fast randomized point location without preprocessing in two-and three-dimensional delaunay triangulations. Comput. Geom. 12, 63–83 (1999)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Delaunay, B.: Sur la sphere vide. Izv. Akad. Nauk SSSR, Otdelenie Matematicheskii i Estestvennyka Nauk 7, 1–2 (1934)Google Scholar
  16. 16.
    Greenspan, M., Yurick, M.: Approximate KD tree search for efficient ICP. In: Proceedings of the Fourth International Conference on 3-D Digital Imaging and Modeling, 3DIM 2003, pp. 442–448. IEEE (2003)Google Scholar
  17. 17.
    Santos, A., Teixeira, J.M., Farias, T., Teichrieb, V., Kelner, J.: Understanding the efficiency of kD-tree ray-traversal techniques over a GPGPU architecture. Int. J. Parallel Program. 40, 331–352 (2012)CrossRefGoogle Scholar
  18. 18.
    Werner, K.P.: Precision relative positioning for automated aerial refueling from a stereo imaging system. Technical report, DTIC Document (2015)Google Scholar
  19. 19.
    Levoy, M., Gerth, J., Curless, B., Pull, K.: The stanford 3D scanning repository (2005).
  20. 20.
    Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The r*-tree: an efficient and robust access method for points and rectangles. ACM SIGMOD Rec. 19, 322–331 (1990). ACMCrossRefGoogle Scholar
  21. 21.
    Meagher, D.J.: Octree encoding: a new technique for the representation, manipulation and display of arbitrary 3-D objects by computer. Electrical and Systems Engineering Department, Rensseiaer Polytechnic Institute Image Processing Laboratory (1980)Google Scholar
  22. 22.
    Low, K.L.: Linear Least-Squares Optimization for Point-to-Plane ICP Surface Registration, vol. 4. University of North Carolina, Chapel Hill (2004)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jace Robinson
    • 1
  • Matt Piekenbrock
    • 1
  • Lee Burchett
    • 1
  • Scott Nykl
    • 1
  • Brian Woolley
    • 1
  • Andrew Terzuoli
    • 1
  1. 1.Air Force Institute of TechnologyWright-PattersonUSA

Personalised recommendations