RANSAC Based Data Association for Underwater Visual SLAM

  • Antoni Burguera
  • Yolanda González
  • Gabriel Oliver
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 252)


This paper presents an approach to perform data association in a monocular visual SLAM context. The proposed approach is designed to avoid the detection of false associations by means of RANSAC, and is well suited to help in localizing a robot in underwater environments. Experimental results embed the data association in a trajectory-based SLAM in order to evaluate its benefits when localizing an underwater robot. Qualitative and quantitative results are shown evaluating the effects of dead reckoning noise and the frequency of the SLAM updates.


Underwater robotics Visual SLAM 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Antoni Burguera
    • 1
  • Yolanda González
    • 1
  • Gabriel Oliver
    • 1
  1. 1.Dept. de Matemàtiques i InformàticaUniversitat de les Illes BalearsPalma de MallorcaSpain

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