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


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.

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Correspondence to Jae-Bok Song.

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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.

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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).

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