Intelligent Service Robotics

, Volume 7, Issue 3, pp 175–184 | Cite as

Artificial landmark-based underwater localization for AUVs using weighted template matching

  • Donghoon Kim
  • Donghwa Lee
  • Hyun Myung
  • Hyun-Taek Choi
Original Research Paper


This paper deals with vision-based localization techniques in structured underwater environments. For underwater robots, accurate localization is necessary to perform complex missions successfully, but few sensors are available for accurate localization in the underwater environment. Among the available sensors, cameras are very useful for performing short-range tasks despite harsh underwater conditions including low visibility, noise, and large areas of featureless scene. To mitigate these problems, we design artificial landmarks to be utilized with a camera for localization, and propose a novel vision-based object detection technique and apply it to the Monte Carlo localization (MCL) algorithm, a map-based localization technique. In the image processing step, a novel correlation coefficient using a weighted sum, multiple-template-based object selection, and color-based image segmentation methods are proposed to improve the conventional approach. In the localization step, to apply the landmark detection results to MCL, 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 and the results are discussed.


Vision processing Object detection Segmentation  Localization Autonomous underwater vehicle 



This research was supported by Korea Institute of Ocean Science and Technology (KIOST) (Project title: “Development of technologies for an underwater robot based on artificial intelligence for highly sophisticated missions”) and by grant No. 10043928 from the Industrial Source Technology Development Programs of the MOTIE (Ministry Of Trade, Industry and Energy), Korea. The students are supported by Korea Ministry of Land, Transport and Maritime Affairs (MLTM) as U-City Master and Doctor Course Grant Program.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Donghoon Kim
    • 1
  • Donghwa Lee
    • 1
  • Hyun Myung
    • 1
  • Hyun-Taek Choi
    • 2
  1. 1.Urban Robotics Lab.Korea Advanced Institute of Science and Technology (KAIST)DaejeonRepublic of Korea
  2. 2.Ocean System Engineering Research DivisionKorea Research Institute of Ships and Ocean Engineering (KRISO)DaejeonRepublic of Korea

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