Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

2.10

CiteScore

Wenbin Xie This email address is being protected from spambots. You need JavaScript enabled to view it.1, Zhen Zhang1, Yuefei Wang1, Yuanyuan Zhang1, Liucun Zhu1

1Advanced Science and Technology Research Institute, Beibu Gulf University, Qinzhou  535011, China


 

Received: July 17, 2019
Accepted: January 5, 2020
Publication Date: June 1, 2020

Download Citation: ||https://doi.org/10.6180/jase.202006_23(2).0006  

ABSTRACT


Using Fast Point Feature Histograms feature from point cloud for 3D object recognition or registration, Fast Point Feature Histograms feature descriptor is arbitrarily and inefficiently calculated by subjectively adjusting the neighborhood radius, and the whole process can’t be completed automatically. An adaptive neighborhood-selection Fast Point Feature Histograms point cloud feature extraction algorithm has proposed to solve this problem. Firstly, we estimate the point cloud density of many pairs of point clouds. Secondly, compute the neighborhood radius to extract the Fast Point Feature Histograms features for Sample Consensus Initial Alignment registration, and count the radius and the density when the registration performance is optimal, and then the cubic spline interpolation fitting is used to obtain the function expression of the radius and the density. Finally, the Fast Point Feature Histograms feature extraction algorithm has combined with the function to form adaptive neighborhood-selection Fast Point Feature Histograms feature extraction algorithm. The experimental results have shown that the proposed algorithm can adaptively choose the appropriate neighborhood radius according to the density of point cloud, and improve the Fast Point Feature Histograms feature matching performance. At the same time, it is improved the computing speed to a better value, which is of guiding significance.

 


Keywords: Fast Point Feature Histograms; Sample Consensus Initial Alignment; Point Cloud Density; Neighborhood Radius; Adaptive Selection


REFERENCES


 

  1. [1]Rusu, R.B., Z.C. Marton, N. Blodow N, et al. (2008) “Persistent Point Feature Histograms for 3D Point Clouds,” In Proceedings of the 10th International Conference on Autonomous Systems, Baden Baden, Germany, 119-128.
  2. [2]He, W., Z.J. Li, C.L.P. Chen, (2017) “A Survey of human-Centered Intelligent Robots: Issues and Challenges,” IEEE/CAA Journal of Automatica Sinica, 4(4), 602-609.
  3. [3]Rusu, R.B., N. Blodow, Z.C. Marton, et al., (2008) “Aligning Point Cloud Views using Persistent Feature Histograms,” In Proceedings of International Conference on Intelligent Robots and Systems, IEEE, Nice, France, 3384-3391.
  4. [4]Rusu, R.B., N. Blodow, M. Beetz, (2009) “Fast Point Feature Histograms (FPFH) for 3D Registration,” In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, 3212-3217.
  5. [5]Zhang, L., L.T. Jiang, (2017) “Research on 3D Object Recognition Based on RealSense,” Information Technology, 10, 78-83.
  6. [6]Huang, J., S. You, (2013) “Detecting Objects in Scene Point Cloud: A Combinational Approach,” In Proceedings of International Conference on 3dtv-Conference. IEEE, 175-182.
  7. [7]Nasab, S.E., S. Kasaei, E. Sanaei, et al., (2014) “Multiview 3D Reconstruction and Human Point Cloud Classification,” In Proceedings of 22nd Iranian Conference on Electrical Engineering, Tehran, Iran, 1119-1124.
  8. [8]Ai, D., M. Wang, G.B. Ni, (2016) “Research and Realization of 3D Restruction Based on FPFH,” Computer Measurement and Control, 24(7), 232-236.
  9. [9]Ye, A.F., S.R. Gong, Z.H. Wang, et al., (2009) “Point Cloud Density Extraction Based on Stochastic Distribution Estimation,” Computer Engineering, 35(4), 183-186.
  10. [10]Xu, X.Y, T.Y. Zhong, (2006) “Construction and Realization of Cubic Spline Interpolation Function,” I. Automation, 25(11),76-78.
  11. [11]Chen, Y.T., J. Xiong, W.H. Xu, et al., (2019) “A Novel Online Incremental and Decremental Learning Algorithm Based on Variable Support Vector Machine,” Cluster Computing, 22(8), S7435-7445.
  12. [12]Yeh, A.R., C. Chen, T.Y. Lim, et al., (2017) “Semantic Image Inpainting with Deep Generative Models,” In Proceedings of 2017 IEEE Conference Computer Vision and Pattern Recognition, IEEE, 6882-6890.
  13. [13]Gao, G.W., D. Zhu, M. Yang, et al., (2018) “Face Image Super-Resolution with Pose Via Nuclear Norm Regularized Structural Orthogonal Procrustes Regression”, Neural Computing and Applications. https://doi.org/10.1007/s00521-018-3826-1.
  14. [14]Chen, Y.T., J. Wang, R.L. Xia, et al., (2019) “The Visual Object Tracking Algorithm Research Based on Adaptive Combination Kernel,” Journal of Ambient Intelligence and Humanized Computing, 10(12), 4855-4867.
  15. [15]Tu, Y., Y. Lin, J. Wang, et al., (2018) “Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification,” Computers Materials & Continua, 55, 243-254.
  16. [16]Chen, Y.T., R.L. Xia, Z. Wang, et al., (2019) “The Visual Saliency Detection Algorithm Research Based on Hierarchical Principle Component Analysis Method,” Multimedia Tools and Applications. https://doi.org/10.1007/s11042-019-07756-1.
  17. [17]Chen, Y.T., J. Wang, X. Chen, et al., (2019) “Single-Image Super-Resolution Algorithm Based on Structural Self-Similarity and Deformation Block Features,” IEEE Access, 7, 58791-58801.
  18. [18]Chen, Y.T., J. Wang, X. Chen, et al., (2019) “Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Network,” Applied Sciences, 9, 2316.
  19. [19]Bengio, Y.S., A. Courville, P. Vincent, (2013) “Representation Learning: A Review and New Perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.
  20. [20]Tang, .F, Y. Ying, J. Wang, et al., (2004) “A Novel Texture Synthesis Based Algorithm for Object Removal in Photographs,” Springer, Berlin Heidelberg.
  21. [21]Chen, Y.T., W.H. Xu, J.W. Zuo, et al., (2019) “The Fire Recognition Algorithm using Dynamic Feature Fusion and IV-SVM Classifier,” Cluster Computing, 22(10), S7665-7675.
  22. [22]Gao, G.W., J. Yang, S.S. Wu, et al., (2015) “Bayesian Sample Steered Discriminative Regression for Biometric Image Classification,” Applied Soft Computing, 37, 48-59.
  23. [23]Gao, G.W., J. Yang, X.Y. Jing, et al., (2017) “Learning Robust and Discriminative Low-rank Representations for Face Recognition with Occlusion,” Pattern Recognition, 66, 129-143.
  24. [24]Gao, G.W., Y. Yu, M. Yang, et al., (2020) “Cross-Resolution Face Recognition with Pose Variations Via Multilayer Locality-Constrained Structural Orthogonal Procrustes Regression,” Information Sciences, 506, 19-36.
  25. [25]Yu, Y., S.H. Tang, K. Aizawa, et al., (2019) “Category-Based Deep CCA for Fine-Grained Venue Discovery From Multimodal Data,” IEEE Transactions on Neural Network Learning System, 30(4), 1250-1258.
  26. [26]Chen, Y.T., J. Wang, S.J. Liu, et al., (2019) “The Multi-Scale Fast Correlation Filtering Tracking Algorithm Based on a Features Fusion Model,” Concurrency and Computation: Practice and Experience, 2019, DOI:10.1002/cpe.5533.