The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Condition-invariant Place Recognition Using Deep Convolutional Auto-encoder

Deep Convolutional Auto-encoder를 이용한 환경 변화에 강인한 장소 인식

  • Oh, Junghyun (Electrical and Computer Engineering, Seoul National University) ;
  • Lee, Beomhee (Electrical and Computer Engineering, Seoul National University)
  • Received : 2018.12.07
  • Accepted : 2019.01.04
  • Published : 2019.02.28
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Abstract

Visual place recognition is widely researched area in robotics, as it is one of the elemental requirements for autonomous navigation, simultaneous localization and mapping for mobile robots. However, place recognition in changing environment is a challenging problem since a same place look different according to the time, weather, and seasons. This paper presents a feature extraction method using a deep convolutional auto-encoder to recognize places under severe appearance changes. Given database and query image sequences from different environments, the convolutional auto-encoder is trained to predict the images of the desired environment. The training process is performed by minimizing the loss function between the predicted image and the desired image. After finishing the training process, the encoding part of the structure transforms an input image to a low dimensional latent representation, and it can be used as a condition-invariant feature for recognizing places in changing environment. Experiments were conducted to prove the effective of the proposed method, and the results showed that our method outperformed than existing methods.

Keywords

References

  1. S. Lowry, N. Sunderhauf, P. Newman, J. Leonard, D. Cox, P. Corke, and M. Milford, "Visual place recognition: A survey," IEEE Transactions on Robotics, vol. 32, no. 1, pp. 1-19, Feb., 2016. https://doi.org/10.1109/TRO.2015.2496823
  2. J. Engel, T. Schops, and D. Cremers, "LSD-SLAM: Large-scale direct monocular SLAM," European Conference on Computer Vision (ECCV), pp. 834-849, 2014.
  3. R. Mur-Artal and J. Tardos, "ORB-SLAM2: an open-source SLAM system for monocular, stereo and RGB-D cameras," IEEE Transactions on Robotics, vol. 33, no. 5, pp. 1255-1262, Oct., 2017. https://doi.org/10.1109/TRO.2017.2705103
  4. M. Cummins and P. Newman, "Appearance-only SLAM at large scale with FAB-MAP 2.0," The International Journal of Robotics Research, vol. 30, no. 9, pp. 1100-1123, 2011. https://doi.org/10.1177/0278364910385483
  5. A. Angeli, D. Filliat, S. Doncieux, and J.-A. Meyer, "Fast and incremental method for loop-closure detection using bags of visual words," IEEE Transactions on Robotics, vol. 24, no. 5, pp. 1027-1037, Oct., 2008. https://doi.org/10.1109/TRO.2008.2004514
  6. M. J. Milford and G. F. Wyeth, "SeqSLAM: Visual routebased navigation for sunny summer days and stormy winter nights," IEEE International Conference on Robotics and Automation (ICRA), Saint Paul, MN, USA, pp. 1643-1649, 2012.
  7. S. Lowry and M. J. Milford, "Supervised and unsupervised linear learning techniques for visual place recognition in changing environments," IEEE Transactions on Robotics, vol. 32, no. 3, pp. 600-613, Jun., 2016. https://doi.org/10.1109/TRO.2016.2545711
  8. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, Nov., 2004.. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  9. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, "Speeded-up robust features (SURF)," Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, Jun., 2008. https://doi.org/10.1016/j.cviu.2007.09.014
  10. S. Leutenegger, M. Chli, and R. Y. Siegwart, "BRISK: Binary robust invariant scalable keypoints," 2011 IEEE International Conference on Computer Vision, Barcelona, Spain, pp. 2548-2555, 2011.
  11. H. Badino, D. Huber, and T. Kanade, "Real-time topometric localization," 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA, pp. 1635-1642, 2012..
  12. N. Sunderhauf and P. Protzel, "BRIEF-Gist - closing the loop by simple means," 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA, pp. 1234-1241, 2011.
  13. N. Sunderhauf, S. Shirazi, F. Dayoub, B. Upcroft, and M. Milford, "On the performance of ConvNet features for place recognition," 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, pp. 4297-4304, 2015.
  14. Z. Chen, A. Jacobson, N. Sunderhauf, B. Upcroft, L. Liu, C. Shen, I. Reid, and M. Milford, "Deep learning features at scale for visual place recognition," 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, Singapore, pp. 3223-3230, 2017.
  15. J. Masci, U. Meier, D. Ciresan and J. Schmidhuber, "Stacked convolutional auto-encoders for hierarchical feature extraction," International Conference on Artificial Neural Networks, pp. 52-59, 2011.
  16. D. P. Kingma and M. Welling, "Auto-encoding variational Bayes," arXiv: 1312.6114 [stat. ML], 2014.
  17. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion," Journal of machine learning research, vol. 11, pp. 3371-3408, 2010.

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