Metode Pengenalan Tempat Secara Visual Berbasis Fitur CNN untuk Navigasi Robot di Dalam Gedung

Visual Place Recognition Method Based-on CNN Features for Indoor Robot Navigation

*Hadha Afrisal -  Department of Electrical Engineering, Faculty of Engineering, Universitas Diponegoro, Indonesia
Received: 21 Feb 2019; Revised: 18 Apr 2019; Accepted: 29 Apr 2019; Published: 30 Apr 2019; Available online: 16 Jul 2019.
Open Access Copyright (c) 2019 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Article Info
Section: Articles
Language: ID
In Volume 7, Issue 2, Year 2019 (April 2019)
Statistics: 161 180
Abstract
Place recognition algorithm based-on visual sensor is crucial to be developed especially for an application of indoor robot navigation in which a Ground Positioning System (GPS) is not reliable to be utilized. This research compares the approach of place recognition of using learned-features from a model of Convolutional Neural Network (CNN) against conventional methods, such as Bag of Words (BoW) with SIFT features and Histogram of Oriented Uniform Patterns (HOUP) with its Local Binary Patterns (LBP). This research finding shows that the performance of our approach of using learned-features with transfer learning method from pre-trained CNN AlexNet is better than the conventional methods based-on handcrafted-features such as BoW and HOUP.
Keywords
place recognition; convolutional neural network; visual navigation; mobile robot

Article Metrics:

  1. J. Fuentes-Pacheco, J. Ruiz-Ascencio, and J. M. Rendón-Mancha, "Visual Simultaneous Localization and Mapping: A Survey," Artificial Intelligence Review, vol. 43, no. 1, pp. 55-81, 2015.
  2. S. Lowry, N. Sünderhauf, P. Newman, J. J. Leonard, D. Cox, P. Corke, and M. J. Milford, "Visual Place Recognition: A Survey," IEEE Transactions on Robotics, vol. 32, no. 1, pp. 1-19, 2016.
  3. D. G. Lowe, "Object Recognition from Local Scale-invariant Features," in Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, Sept. 1999, pp. 1150-1157.
  4. H. Bay, T. Tuytelaars, and L. Van Gool, "Surf: Speeded up Robust Features," in European Conference on Computer Vision, Berlin, Heidelberg, 2006, pp. 404-417.
  5. S. Leutenegger, M. Chli, and R. Y. Siegwart, ”BRISK: Binary Robust Invariant Scalable Keypoints,“ in 2011 International Conference on Computer Vision, Barcelona, Spain, Nov. 2011, pp. 2548-2555.
  6. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "ORB: An Efficient Alternative to SIFT or SURF," in 2011 International Conference on Computer Vision, Barcelona, Spain, Nov. 2011, pp. 2564-2571.
  7. Z. Wang, F. Wu, and Z. Hu, "MSLD: A Robust Descriptor for Line Matching," Pattern Recognition, vol. 42, no. 5, pp. 941-953, 2009.
  8. C. G. Harris and M. Stephens, "A Combined Corner and Edge Detector.," in Proc. of Fourth Alvey Vision Conference, 1988, pp. 147-151.
  9. K. Chatfield, V. S. Lempitsky, A. Vedaldi, and A. Zisserman, "The Devil is in the Details: an Evaluation of Recent Feature Encoding Methods," in The 22nd British Machine Vision Conference, England, Sept. 2011, pp. 76.1-76.12.
  10. D. Gálvez-López and J. D. Tardos, "Bags of Binary Words for Fast Place Recognition in Image Sequences," IEEE Transactions on Robotics, vol. 28, no. 5, pp. 1188-1197, 2012.
  11. A. Torii, J. Sivic, T. Pajdla, and M. Okutomi, "Visual Place Recognition with Repetitive Structures," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 11, pp. 2346-2359, 2013.
  12. N. Sünderhauf, S. Shirazi, A. Jacobson, F. Dayoub, E. Pepperell, B. Upcroft, and M. Milford, "Place Recognition with Convnet Landmarks: Viewpoint-robust, Condition-robust, Training-free," in Proceedings of Robotics: Science and Systems XI, Rome, Italy, July 2015.
  13. J. L. Schonberger, H. Hardmeier, T. Sattler, and M. Pollefeys, "Comparative Evaluation of Hand-crafted and Learned Local Features," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.
  14. Q. Li, K. Li, X. You, S. Bu, and Z. Liu, "Place Recognition Based on Deep Feature and Adaptive Weighting of Similarity Matrix," Neurocomputing, vol. 199, pp. 114-127, 2016.
  15. L. Tai and M. Liu, "Deep-Learning in Mobile Robotics-From Perception to Control Systems: A Survey on Why and Why Not," arXiv preprint arXiv:1612.07139 [cs], Dec. 2016.
  16. A. Krizhevsky, I. Sutskever and G. E. Hinton, "Imagenet Classification with Deep Convolutional Neural Networks," Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.
  17. R. Sahdev and J. K. Tsotsos, "Indoor Place Recognition System for Localization of Mobile Robots," in 13th Conference on Computer and Robot Vision (CRV), Victoria, Canada, Jun. 2016, pp. 53-60.
  18. A. Quattoni and A. Torralba, "Recognizing Indoor Scenes," in 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, Jun. 2009, pp. 413-420.
  19. S. Krig, “Interest Point Detector and Feature Descriptor Survey,” in Computer Vision Metrics, Berkeley: Apress, 2014, pp. 187-246.
  20. A. Vedaldi and B. Fulkerson, "VLFeat: An Open and Portable Library of Computer Vision Algorithms," in Proc. of the 18th ACM International Conference on Multimedia, New York, USA, Oct. 2010, pp. 1469-1472.
  21. E. Fazl-Ersi and J. K. Tsotsos, "Histogram Of Oriented Uniform Patterns for Robust Place Recognition and Categorization," International Journal of Robotics Research, vol. 31, no. 2, pp. 468-483, 2012.
  22. O. Russakovsky et al., "Imagenet Large Scale Visual Recognition Challenge," International Journal of Computer Vision, vol. 115, no. 1, pp. 211-252, 2015.
  23. A. Vedaldi and K. Lenc, "Matconvnet: Convolutional Neural Networks for Matlab," in Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, Australia, Oct. 2015, pp. 689-692.

Copyright (c) 2019 Jurnal Teknologi dan Sistem Komputer Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to JTSiskom journal and Department of Computer Engineering, Universitas Diponegoro as the publisher of the journal. Copyright encompasses rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations. 

JTSiskom journal and Department of Computer Engineering, Universitas Diponegoro and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in JTSiskom journal are the sole and exclusive responsibility of their respective authors and advertisers.

The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form JTSiskom]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :  

Eko Didik Widianto (Editor-in-Chief)
Editorial Office of Jurnal Teknologi dan Sistem Komputer
Department of Computer Engineering, Universitas Diponegoro
Jl. Prof. Soedarto, Kampus Undip Tembalang, Semarang, Central Java, Indonesia 50275
Telp./Fax: +62-24-76480609
Email: jtsiskom@ce.undip.ac.id