Vision-based localization for AUVs using weighted template matching in a structured environment

3Citations
Citations of this article
14Readers
Mendeley users who have this article in their library.

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, deadreckoning 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. © ICROS 2013.

Cite

CITATION STYLE

APA

Kim, D., Lee, D., Myung, H., & Choi, H. T. (2013). Vision-based localization for AUVs using weighted template matching in a structured environment. Journal of Institute of Control, Robotics and Systems, 19(8), 667–675. https://doi.org/10.5302/J.ICROS.2013.13.9012

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free