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Reliable pose estimation of underwater dock using single camera: a scene invariant approach

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Abstract

It is well known that docking of Autonomous Underwater Vehicle (AUV) provides scope to perform long duration deep-sea exploration. A large amount of literature is available on vision-based docking which exploit mechanical design, colored markers to estimate the pose of a docking station. In this work, we propose a method to estimate the relative pose of a circular-shaped docking station (arranged with LED lights on periphery) up to five degrees of freedom (5-DOF, neglecting roll effect). Generally, extraction of light markers from underwater images is based on fixed/adaptive choice of threshold, followed by mass moment-based computation of individual markers as well as center of the dock. Novelty of our work is the proposed highly effective scene invariant histogram-based adaptive thresholding scheme (HATS) which reliably extracts positions of light sources seen in active marker images. As the perspective projection of a circle features a family of ellipses, we then fit an appropriate ellipse for the markers and subsequently use the ellipse parameters to estimate the pose of a circular docking station with the help of a well-known method in Safaee-Rad et al. (IEEE Trans Robot Autom 8(5):624–640, 1992). We analyze the effectiveness of HATS as well as proposed approach through simulations and experimentation. We also compare performance of targeted curvature-based pose estimation with a non-iterative efficient perspective-n-point (EPnP) method. The paper ends with a few interesting remarks on vantages with ellipse fitting for markers and utility of proposed method in case of non-detection of all the light markers.

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Acknowledgments

The research was carried out in the “UnWaR” project (Project No. ESC-0113) funded by CSIR, India. The authors wish to express sincere thanks to all Robotics and Automation Group members for their help and support.

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Authors and Affiliations

  1. Academy of Scientific and Innovative Research (AcSIR), CSIR-CMERI, Durgapur, India

    Shatadal Ghosh

  2. Robotics and Automation Division, CSIR-CMERI, Durgapur, India

    Ranjit Ray, Siva Ram Krishna Vadali, Sankar Nath Shome & Sambhunath Nandy

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  1. Shatadal Ghosh
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  2. Ranjit Ray
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  3. Siva Ram Krishna Vadali
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  4. Sankar Nath Shome
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  5. Sambhunath Nandy
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Correspondence to Ranjit Ray.

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Ghosh, S., Ray, R., Vadali, S.R.K. et al. Reliable pose estimation of underwater dock using single camera: a scene invariant approach. Machine Vision and Applications 27, 221–236 (2016). https://doi.org/10.1007/s00138-015-0736-4

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  • DOI: https://doi.org/10.1007/s00138-015-0736-4

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