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Scene Recognition for Indoor Localization of Mobile Robots Using Deep CNN

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Computer Vision and Graphics (ICCVG 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11114))

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Abstract

In this paper we propose a deep neural network based algorithm for indoor place recognition. It uses transfer learning to retrain VGG-F, a pretrained convolutional neural network to classify places on images acquired by a humanoid robot. The network has been trained as well as evaluated on a dataset consisting of 8000 images, which were recorded in sixteen rooms. The dataset is freely accessed from our website. We demonstrated experimentally that the proposed algorithm considerably outperforms BoW algorithms, which are frequently used in loop-closure. It also outperforms an algorithm in which features extracted by FC-6 layer of the VGG-F are classified by a linear SVM.

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References

  1. Arroyo, R., Alcantarilla, P., Bergasa, L., Romera, E.: OpenABLE: an open-source toolbox for application in life-long visual localization of autonomous vehicles. In: IEEE International Conference on Intelligent Transportation Systems, pp. 965–970 (2016)

    Google Scholar 

  2. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. Eur. Conf. Comput. Vis. 3951, 404–417 (2006)

    Google Scholar 

  3. Cadena, C., et al.: Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Robot. 32(6), 1309–1332 (2016)

    Article  Google Scholar 

  4. Chatfield, K., Lempitsky, V.S., Vedaldi, A., Zisserman, A.: The devil is in the details: an evaluation of recent feature encoding methods. In: British Machine Vision Conference (BMVC) (2011)

    Google Scholar 

  5. Chen, Z., Lam, O., Jacobson, A., Milford, M.: Convolutional neural network-based place recognition. In: Australasian Conference on Robotics and Automation (2014). https://eprints.qut.edu.au/79662/

  6. Cummins, M., Newman, P.: FAB-MAP: probabilistic localization and mapping in the space of appearance. Int. J. Rob. Res. 27(6), 647–665 (2008)

    Article  Google Scholar 

  7. Galvez-Lopez, D., Tardos, T.: Bags of binary words for fast place recognition in image sequences. IEEE Trans. Robot. 28, 1188–1197 (2012)

    Article  Google Scholar 

  8. Garcia-Fidalgo, E., Ortiz, A.: Vision-based topological mapping and localization by means of local invariant features and map refinement. Robotica 33, 1446–1470 (2014)

    Article  Google Scholar 

  9. Harris, C., Stephens, M.: A combined corner and edge detector. Alvey Vis. Conf. 15, 10–5244 (1988)

    Google Scholar 

  10. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  11. Kuindersma, S., et al.: Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot. Adv. Neural Proc. Syst. 40, 429–455 (2016)

    Google Scholar 

  12. Leutenegger, S., Chli, M., Siegwart, R.: BRISK: binary robust invariant scalable keypoints. In: International Conference on Computer Vision (ICCV) (2011)

    Google Scholar 

  13. Levitt, T., Lawton, D.: Qualitative navigation for mobile robots. Artif. Intell. 44(3), 305–360 (1990)

    Article  Google Scholar 

  14. Li, Q., Li, K., You, X., Bu, S., Liu, Z.: Place recognition based on deep feature and adaptive weighting of similarity matrix. Neurocomputing 199, 114–127 (2016)

    Article  Google Scholar 

  15. Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  MathSciNet  Google Scholar 

  16. Lowry, S., et al.: Visual place recognition: a survey. IEEE Trans. Robot. 32, 1–19 (2016)

    Article  Google Scholar 

  17. Newman, P., Ho, K.: SLAM-loop closing with visually salient features. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 635–642 (2005)

    Google Scholar 

  18. Oliva, A., Torralba, A.: Building the gist of a scene: the role of global image features in recognition. In: Visual Perception, Progress in Brain Research, vol. 155, pp. 23–36. Elsevier (2006)

    Google Scholar 

  19. Oriolo, G., Paolillo, A., Rosa, L., Vendittelli, M.: Humanoid odometric localization integrating kinematic, inertial and visual information. Auton. Robots 40, 867–879 (2016)

    Article  Google Scholar 

  20. Radford, N., et al.: Valkryrie: NASA’s first bipedal humanoid robot. J. Field Robot. 32, 397–419 (2015)

    Article  Google Scholar 

  21. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: International Conference on Computer Vision (ICCV), vol. 32 (2011)

    Google Scholar 

  22. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  23. Sahdev, R., Tsotsos, J.: Indoor place recognition system for localization of mobile robots. In: IEEE Conference on Computer and Robot Vision, pp. 53–60 (2016)

    Google Scholar 

  24. Schönberger, J., Hardmeier, H., Sattler, T., Pollefeys, M.: Comparative evaluation of hand-crafted and learned local features. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6959–6968 (2017)

    Google Scholar 

  25. Simard, P., Steinkraus, D., Platt, J.: Best practices for convolutional neural networks applied to visual document analysis. In: International Conference on Document Analysis and Recognition, pp. 958–963 (2003)

    Google Scholar 

  26. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)

    Google Scholar 

  27. Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, W.: Discovering objects and their location in images. In: IEEE International Conference on Computer Vision, vol. 1, pp. 370–377 (2005)

    Google Scholar 

  28. Sünderhauf, N., Protzel, P.: BRIEF-Gist - closing the loop by simple means. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1234–1241 (2011)

    Google Scholar 

  29. Sünderhauf, N., et al.: Place recognition with convNet landmarks: viewpoint-robust, condition-robust, training-free. In: Proceedings of Robotics: Science and Systems XII (2015)

    Google Scholar 

  30. Tai, L., Liu, M.: Deep-learning in mobile robotics - from perception to control systems: a survey on why and why not. arXiv (2016)

    Google Scholar 

  31. Torii, A., Sivic, J., Pajdla, T., Okutomi, M.: Visual place recognition with repetitive structures. In: Proceedings of the IEEE Conference on Computer, Vision and Pattern Recognition (2013)

    Google Scholar 

  32. Wang, Z., Wu, F., Hu, Z.: MSLD: a robust descriptor for line matching. Pattern Recogn. 42, 941–953 (2009)

    Article  Google Scholar 

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Acknowledgment

This work was supported by Polish National Science Center (NCN) under a research grant 2014/15/B/ST6/02808.

Author information

Authors and Affiliations

  1. AGH University of Science and Technology, 30 Mickiewicza, 30-059, Kraków, Poland

    Bogdan Kwolek

  2. Universitas Gadjah Mada, Bulaksumur, Yogyakarta, 55281, Indonesia

    Hadha Afrisal

  3. Monterrey Institute of Technology and Higher Education, Av. E. G. Sada 2501 Sur, Tecnológico, 64849, Monterrey, Mexico

    Rigel Galindo Esparza

  4. Rzeszów University of Technology, Al. Powstańców Warszawy 12, 35-959, Rzeszów, Poland

    Piotr Wozniak

Authors
  1. Piotr Wozniak
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  2. Hadha Afrisal
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  3. Rigel Galindo Esparza
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  4. Bogdan Kwolek
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Corresponding author

Correspondence to Bogdan Kwolek .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Leszek J. Chmielewski

  2. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Ryszard Kozera

  3. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Arkadiusz Orłowski

  4. Institute of Computer Science, Silesian University of Technology, Gliwice, Poland

    Konrad Wojciechowski

  5. Technion, Israel Institute of Technology, Haifa, Israel

    Alfred M. Bruckstein

  6. University of Groningen, Groningen, The Netherlands

    Nicolai Petkov

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Wozniak, P., Afrisal, H., Esparza, R.G., Kwolek, B. (2018). Scene Recognition for Indoor Localization of Mobile Robots Using Deep CNN. In: Chmielewski, L., Kozera, R., Orłowski, A., Wojciechowski, K., Bruckstein, A., Petkov, N. (eds) Computer Vision and Graphics. ICCVG 2018. Lecture Notes in Computer Science(), vol 11114. Springer, Cham. https://doi.org/10.1007/978-3-030-00692-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-00692-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00691-4

  • Online ISBN: 978-3-030-00692-1

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