The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Image Enhancement for Visual SLAM in Low Illumination

저조도 환경에서 Visual SLAM을 위한 이미지 개선 방법

  • Donggil You (Department of Robotics, Kwangwoon University) ;
  • Jihoon Jung (Department of Robotics, Kwangwoon University) ;
  • Hyeongjun Jeon (Department of Robotics, Kwangwoon University) ;
  • Changwan Han (Department of Robotics, Kwangwoon University) ;
  • Ilwoo Park (Department of Robotics, Kwangwoon University) ;
  • Junghyun Oh (Department of Robotics, Kwangwoon University)
  • Received : 2022.10.31
  • Accepted : 2022.12.03
  • Published : 2023.02.28
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Abstract

As cameras have become primary sensors for mobile robots, vision based Simultaneous Localization and Mapping (SLAM) has achieved impressive results with the recent development of computer vision and deep learning. However, vision information has a disadvantage in that a lot of information disappears in a low-light environment. To overcome the problem, we propose an image enhancement method to perform visual SLAM in a low-light environment. Using the deep generative adversarial models and modified gamma correction, the quality of low-light images were improved. The proposed method is less sharp than the existing method, but it can be applied to ORB-SLAM in real time by dramatically reducing the amount of computation. The experimental results were able to prove the validity of the proposed method by applying to public Dataset TUM and VIVID++.

Keywords

Acknowledgement

This paper was supported by Korea Institute for Advancement of Technology(KIAT) grant funded by the Korea Government(MOTIE) (P0017124, HRD Program for Industrial Innovation)

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