Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Vision-based Localization for AUVs using Weighted Template Matching in a Structured Environment

구조화된 환경에서의 가중치 템플릿 매칭을 이용한 자율 수중 로봇의 비전 기반 위치 인식

  • Kim, Donghoon (Dept. of Civil and Environmental Engineering, Robotics Program, Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Lee, Donghwa (Dept. of Civil and Environmental Engineering, Robotics Program, Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Myung, Hyun (Dept. of Civil and Environmental Engineering, Robotics Program, Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Choi, Hyun-Taek (Ocean System Eng. Research Division, Korea Institute of Ocean Science and Technology (KIOST))
  • Received : 2013.05.15
  • Accepted : 2013.06.30
  • Published : 2013.08.01
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Abstract

This paper presents vision-based techniques for underwater landmark detection, map-based localization, and SLAM (Simultaneous Localization and Mapping) in structured underwater environments. A variety of underwater tasks require an underwater robot to be able to successfully perform autonomous navigation, but the available sensors for accurate localization are limited. A vision sensor among the available sensors is very useful for performing short range tasks, in spite of harsh underwater conditions including low visibility, noise, and large areas of featureless topography. To overcome these problems and to a utilize vision sensor for underwater localization, we propose a novel vision-based object detection technique to be applied to MCL (Monte Carlo Localization) and EKF (Extended Kalman Filter)-based SLAM algorithms. In the image processing step, a weighted correlation coefficient-based template matching and color-based image segmentation method are proposed to improve the conventional approach. In the localization step, in order to apply the landmark detection results to MCL and EKF-SLAM, dead-reckoning information and landmark detection results are used for prediction and update phases, respectively. The performance of the proposed technique is evaluated by experiments with an underwater robot platform in an indoor water tank and the results are discussed.

Keywords

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