Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Das, S., Suganthan, P.: Differential Evolution: A Survey of the State-of-the-Art. IEEE Transactions on Evolutionary Computation 15(1), 4–31 (2011)
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)
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)
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)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc. IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
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)
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)
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)
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)
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)
nVIDIA Corporation: nVIDIA CUDA Programming Guide v. 5.0. (2012)
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)
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)
Poli, R., Kennedy, J., Blackwell, T.: Particle Swarm Optimization. Swarm Intelligence 1(1), 33–57 (2007)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ugolotti, R., Micconi, G., Aleotti, J., Cagnoni, S. (2014). GPU-Based Point Cloud Recognition Using Evolutionary Algorithms. In: Esparcia-Alcázar, A., Mora, A. (eds) Applications of Evolutionary Computation. EvoApplications 2014. Lecture Notes in Computer Science(), vol 8602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45523-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-662-45523-4_40
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45522-7
Online ISBN: 978-3-662-45523-4
eBook Packages: Computer ScienceComputer Science (R0)