Vision-Based Shipwreck Mapping: On Evaluating Features Quality and Open Source State Estimation Packages

A. Quattrini Li, A. Coskun, S. M. Doherty, S. Ghasemlou, A. S. Jagtap, M. Modasshir, S. Rahman, A. Singh, M. Xanthidis, J. M. O'Kane, I. Rekleitis
In Proc. MTS/IEEE Oceans Monterey 2016.

Abstract

Historical shipwrecks are important for many reasons, including historical, touristic, and environmental. Currently, limited efforts for constructing accurate models are performed by divers that need to take measurements manually using a grid and measuring tape, or using handheld sensors. A commercial product, Google Street View, contains underwater panoramas from select location around the planet including a few shipwrecks, such as the SS Antilla in Aruba and the Yongala at the Great Barrier Reef. However, these panoramas contain no geometric information and thus there are no 3D representations available of these wrecks. This paper provides, first, an evaluation of visual features quality in datasets that span from indoor to underwater ones. Second, by testing some open-source vision-based state estimation packages on different shipwreck datasets, insights on open challenges for shipwrecks mapping are shown. Some good practices for replicable results are also discussed.

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BibTeX

@inproceedings{QuaCos+16b,
  title        = {Vision-Based Shipwreck Mapping: {O}n Evaluating Features
		 Quality and Open Source State Estimation Packages},
  author       = {A. Quattrini Li and A. Coskun and S. M. Doherty and S.
		 Ghasemlou and A. S. Jagtap and M. Modasshir and S. Rahman
		 and A. Singh and M. Xanthidis and J. M. O'Kane and I.
		 Rekleitis},
  booktitle    = {Proc. MTS/IEEE Oceans Monterey},
  year	       = {2016}
}

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