Skip to main content

Robust Place Recognition Using Illumination-compensated Image-based Deep Convolutional Autoencoder Features

Abstract

Place recognition is a method for determining whether a robot has previously visited the place it currently observes, thus helping the robot correct its accumulated position error. Ultimately, the robot will travel long distances more accurately. Conventional image-based place recognition uses features extracted from a bag-of-visual-words (BoVW) scheme or pre-trained deep neural network. However, the BoVW scheme does not cope well with environmental changes, and the pre-trained deep neural network is disadvantageous in that its computation time is high. Therefore, this paper proposes a novel place recognition scheme using an illumination-compensated image-based deep convolutional autoencoder (ICCAE) feature. Instead of reconstructing the raw image, the autoencoder designed to extract ICCAE features is trained to reconstruct the image, whose illumination component is compensated in the logarithm frequency domain. As a result, we can extract the ICCAE features based on a convolution layer that is robust to illumination and environmental changes. Additionally, ICCAE features can perform faster feature matching than the features extracted from existing deep networks. To evaluate the performance of ICCAE feature-based place recognition, experiments were conducted using a public dataset that includes various conditions.

This is a preview of subscription content, access via your institution.

References

  1. [1]

    F. Endres, J. Hess, J. Sturm, D. Cremers, and W. Burgard, “3-D mapping with an RGB-D camera,” IEEE Trans. on Robotics, vol. 30, no. 1, pp. 177–187, 2013.

    Article  Google Scholar 

  2. [2]

    M. Cummins and P. Newman, “FAB-MAP: probabilistic localization and mapping in the space of appearance,” Int. Journal of Robotics Research, vol. 27, no. 6, pp. 647–665, 2008.

    Article  Google Scholar 

  3. [3]

    M. Labbe and F. Michaud, “Appearance-based loop closure detection for online large-scale and long-term operation,” IEEE Trans. on Robotics, vol. 29, no. 3, pp. 734–745, 2013.

    Article  Google Scholar 

  4. [4]

    S. J. Lee and S. S. Hwang, “Bag of sampled words: a sampling-based strategy for fast and accurate visual place recognition in changing environments,” Int. Journal of Control, Automation and Systems, vol. 17, no. 10, pp. 2597–2609, 2019.

    Article  Google Scholar 

  5. [5]

    R. Mur-Artal and J. D. Tardos, “ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras,” IEEE Trans. on Robotics, vol. 33, no. 5, pp. 1255–1262, 2017.

    Article  Google Scholar 

  6. [6]

    D. G’alvez-L’opez and J. D. Tardos, “Bags of binary words for fast place recognition in image sequences,” IEEE Trans. on Robotics, vol. 28, no. 5, pp. 1188–1197, 2012.

    Article  Google Scholar 

  7. [7]

    M. J. Milford and G. F. Wyeth, “SeqSLAM: visual route-based navigation for sunny summer days and stormy winter nights,” Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 1643–1649, 2012.

  8. [8]

    P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “Overfeat: integrated recognition, localization, and detection using convolutional networks,” arXiv preprint arXiv:1312.6229, 2013.

  9. [9]

    A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in Neural Information Processing Systems, vol. 60, no. 6, pp. 1097–1105, 2012.

    Google Scholar 

  10. [10]

    C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826, 2016.

  11. [11]

    K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.

  12. [12]

    A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “CNN features off-the-shelf: an astounding baseline for recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 806–813, 2014.

  13. [13]

    S. Lowry, G. Wyeth, and M. Milford, “Unsupervised online learning of condition-invariant images for place recognition,” Proc. Australasian Conf. on Robot. and Automation, 2014.

  14. [14]

    N. Merrill and G. Huang, “Lightweight unsupervised deep loop closure,” arXiv preprint arXiv:1805.07703, 2018.

  15. [15]

    K. Zuiderveld, “Contrast limited adaptive histogram equalization,” Graphics Gems IV, Academic Press Professional, Inc., pp. 474–485, 1994.

  16. [16]

    W. Chen, M. J. Er, and S. Wu, “Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 36, no. 2, pp. 458–466, 2006.

    Article  Google Scholar 

  17. [17]

    Z. Sufyanu, F. Mohamad, and A. S. Ben-Musa, “Choice of illumination normalization algorithm for preprocessing efficiency of discrete cosine transform,” International Journal of Applied Engineering, vol. 10, no. 3, pp. 6341–6351, 2015.

    Google Scholar 

  18. [18]

    E. H. Land and J. J. McCann, “Lightness and retinex theory,” Journal of the Optical Society of America, vol. 61, no. 1, pp. 1–11, 1971.

    Article  Google Scholar 

  19. [19]

    B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba, “Places: A 10 million image database for scene recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 6, pp. 1452–1464, 2017.

    Article  Google Scholar 

  20. [20]

    J. Luo, A. Pronobis, B. Caputo, and P. Jensfelt, “Incremental learning for place recognition in dynamic environments,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 721–728, 2007.

  21. [21]

    D. Olid, J. M. Fácil, and J. Civera, “Single-view place recognition under seasonal changes,” arXiv preprint arXiv:1808.06516, 2018.

  22. [22]

    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,” Proceedings of Robotics: Science and Systems XII, 2015.

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jae-Bok Song.

Additional information

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Dong-Joong Kang under the direction of Editor Euntai Kim.

This research was supported by the MOTIE under the Industrial Foundation Technology Development Program supervised by the KEIT (No. 20005032).

Chansoo Park received his B.S. degree in Computer and Information Science from Korea University in 2012. He is now an M.S. and Ph.D. candidate in the School of Mechatronics at Korea University. His research interests include robot navigation, computer vision, and software engineering.

Hee-Won Chae received his B.S. degree in Mechanical Engineering from Korea University in 2013. He is now an M.S. and Ph.D. candidate in the School of Mechanical Engineering at Korea University. His research interests include robot navigation, computer vision, and visual SLAM.

Jae-Bok Song received his B.S. and M.S. degrees in Mechanical Engineering from Seoul National Univ., Seoul, Korea, in 1983 and 1985, respectively, and his Ph.D. degree in Mechanical Engineering from MIT, Cambridge, MA, in 1992. He joined the faculty of the Department of Mechanical Engineering, Korea University, Seoul, Korea in 1993. His current research interests are the design and control of robot arms and robot navigation systems.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Park, C., Chae, HW. & Song, JB. Robust Place Recognition Using Illumination-compensated Image-based Deep Convolutional Autoencoder Features. Int. J. Control Autom. Syst. 18, 2699–2707 (2020). https://doi.org/10.1007/s12555-019-0891-x

Download citation

Keywords

  • Convolutional autoencoder
  • frequency image
  • illumination compensation
  • place recognition