A Mobile Robot Application for 3D Object Classification via Point Cloud Data in Indoor Environment

References

1. K. Alhamzi, M. Elmogy and S. Barakat, “3D Object Recognition Based on Local and Global Features Using Point Cloud Library”, International Journal of Advancements in Computing Technology, vol. 7, no. 3, pp. 43-54, 2015.
2. X.F. Han, J.X. Jin, J. Xie, M.J. Wang and W. Jiang, “A comprehensive review of 3D point cloud descriptors”, ArXiv, 2018.
3. S. H. Kasaei , A. M. Tomé , L. S. Lopes and M. Oliveira, “A Global Orthographic Object Descriptor for 3D Object Recognition and Manipulation”, Pattern Recognition Letters, vol. 83, no. 3, pp. 312-320, 2016.
4. R. B. Rusu, Z. C. Marton, N. Blodow, and M. Beetz, “Learning Informative Point Classes for the Acquisition of Object Model Maps,” in Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 643-650, 2008.
5. R.B. Rusu, N. Blodow and M.Beetz, “Fast Point Feature Histograms (FPFH) for 3D Registration”, IEEE Int. Conf Robot Automation (ICRA), pp. 3212 - 3217, 2009.
6. R. B. Rusu, G. Bradski, R. Thibauxand and J. Hsu, “Fast 3D Recognition and Pose Using the Viewpoint Feature Histogram”,2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2155-2162, 2010.
7. A. Aldoma, N. Blodow, D. Gossow, S. Gedikli, R. B. Rusu and G. Bradski, “CAD-Model Recognition and 6DOF Pose Estimation Using 3D Cues”, IEEE International Conference on Computer Vision Workshops(ICCV Workshops), pp.585-592, 2011.
8. Z. Fan, Z. Li and W. Li, “Object Detection and Sorting by Using a Global Texture-Shape 3D Feature Descriptor”, eprint arXiv:1802.01116,2018arXiv180201116F, 2018.
9. W. Wohlkingerve and M. Vincze, “Ensemble of shape functions for 3D object classification”,2011 IEEE International Conference on Robotics and Biomimetics, Karon Beach, Phuket, pp. 2987-2992, 2011.
10. R. B. Rusu and S. Cousins, “3D is here: Point Cloud Library (PCL),” in IEEE International Conference on Robotics and Automation (ICRA), pp. 1-4, 2011.
11. L. A. Alexandre, “3D descriptors for object and category recognition: A comparative evaluation”, In Workshop on Color-Depth Camera Fusion in Robotics at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2012.
12. F. Melgani and L. Bruzzone “Classification of Hyperspectral Remote Sensing Images with Support Vector Machines” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol.42, no. 8, 2004.
13. B. Sch¨olkopf, K. Sung, C. J. C. Burges, F. Girosi, P. Niyogi, T. Poggio and V.Vapnik, “Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers” IEEE TRANSACTIONS ON SIGNAL PROCESSING, vol. 45, no. 11, 1997.
14. K. Ramasubramanian, A. Singh, “Machine Learning Using R”, Apress, no:1,2017
15. Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang and J. Xiao “3D ShapeNets: A Deep Representation for Volumetric Shapes”, Proceedings of 28th IEEE Conference on Computer Vision and Pattern Recognition(CVPR2015) Available:http://modelnet.cs.princeton.edu/
16. Google,Internet: https://www.blender.org/

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