GPU-Based Point Cloud Recognition Using Evolutionary Algorithms

  • Roberto UgolottiEmail author
  • Giorgio Micconi
  • Jacopo Aleotti
  • Stefano Cagnoni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8602)


In this paper, we describe a method for recognizing objects in the form of point clouds acquired with a laser scanner. This method is fully implemented on GPU and uses bio-inspired metaheuristics, namely PSO or DE, to evolve the rigid transformation that best aligns some references extracted from a dataset to the target point cloud. We compare the performance of our method with an established method based on Fast Point Feature Histograms (FPFH). The results prove that FPFH is more reliable under simple and controlled situations, but PSO and DE are more robust with respect to common problems as noise or occlusions.


Particle Swarm Optimization Differential Evolution Pattern Recognition GPGPU Point Clouds 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Das, S., Suganthan, P.: Differential Evolution: A Survey of the State-of-the-Art. IEEE Transactions on Evolutionary Computation 15(1), 4–31 (2011)CrossRefGoogle Scholar
  2. 2.
    de Veronese, L., Krohling, R.: Swarm’s flight: Accelerating the particles using C-CUDA. In: Proc. IEEE Congress on Evolutionary Computation, pp. 3264–3270 (2009)Google Scholar
  3. 3.
    de Veronese, L., Krohling, R.: Differential Evolution algorithm on the GPU with C-CUDA. In: Proc. IEEE Congress on Evolutionary Computation, pp. 1–7 (2010)Google Scholar
  4. 4.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc. IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  6. 6.
    Kromer, P., Platos, J., Snasel, V.: A brief survey of advances in Particle Swarm Optimization on Graphic Processing Units. In: IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 182–188 (2013)Google Scholar
  7. 7.
    Kromer, P., Platos, J., Snasel, V.: A brief survey of Differential Evolution on Graphic Processing Units. In: Symp. on Differential Evolution, pp. 157–164 (2013)Google Scholar
  8. 8.
    Li, H., Shen, T., Huang, X.: Approximately global optimization for robust alignment of generalized shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(6), 1116–1131 (2011)CrossRefGoogle Scholar
  9. 9.
    Makadia, A., Patterson, A., Daniilidis, K.: Fully automatic registration of 3D point clouds. In: Conf. on Computer Vision and Pattern Recognition, pp. 1297–1304 (2006)Google Scholar
  10. 10.
    Nashed, Y.S.G., Ugolotti, R., Mesejo, P., Cagnoni, S.: libCudaOptimize: an open source library of GPU-based metaheuristics. In: Proc. of the Genetic and Evolutionary Computation Conference (GECCO) Companion, pp. 117–124. ACM (2012)Google Scholar
  11. 11.
    nVIDIA Corporation: nVIDIA CUDA Programming Guide v. 5.0. (2012)Google Scholar
  12. 12.
    Oleari, F., Lodi Rizzini, D., Caselli, S.: A low-cost stereo system for 3D object recognition. In: IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 127–132 (2013)Google Scholar
  13. 13.
    Owens, J.D., Luebke, D., Govindaraju, N., Harris, M., Krüger, J., Lefohn, A.E., Purcell, T.J.: A Survey of General-Purpose Computation on Graphics Hardware. Computer Graphics Forum 26, 80–113 (2007)CrossRefGoogle Scholar
  14. 14.
    Poli, R., Kennedy, J., Blackwell, T.: Particle Swarm Optimization. Swarm Intelligence 1(1), 33–57 (2007)CrossRefGoogle Scholar
  15. 15.
    Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3212–3217 (2009)Google Scholar
  16. 16.
    Rusu, R.B., Marton, Z.C., Blodow, N., Beetz, M.: Learning informative point classes for the acquisition of object model maps. In: IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 643–650 (2008)Google Scholar
  17. 17.
    Storn, R., Price, K.: Differential Evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report, International Computer Science Institute (1995)Google Scholar
  18. 18.
    Ugolotti, R., Nashed, Y.S., Mesejo, P., Ivekovič, Š., Mussi, L., Cagnoni, S.: Particle Swarm Optimization and Differential Evolution for model-based object detection. Applied Soft Computing 13(6), 3092–3105 (2013)CrossRefGoogle Scholar
  19. 19.
    Urfalolu, O., Mikulastik, P.A., Stegmann, I.: Scale Invariant Robust Registration of 3D-Point Data and a Triangle Mesh by Global Optimization. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2006. LNCS, vol. 4179, pp. 1059–1070. Springer, Heidelberg (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Roberto Ugolotti
    • 1
    Email author
  • Giorgio Micconi
    • 1
  • Jacopo Aleotti
    • 1
  • Stefano Cagnoni
    • 1
  1. 1.Department of Information EngineeringUniversity of ParmaParma PRItaly

Personalised recommendations