Area Estimation of Deep-Sea Surfaces from Oblique Still Images
Author(s) -
Frederico Carvalho Dias,
José Nuno GomesPereira,
Inês Tojeira,
Miguel Souto,
Andreia Afonso,
António Calado,
Pedro Madureira,
Aldino Santos de Campos
Publication year - 2015
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0133290
Subject(s) - seabed , remotely operated vehicle , remotely operated underwater vehicle , seafloor spreading , geology , oblique case , tilt (camera) , deep sea , remote sensing , geodesy , marine engineering , computer science , artificial intelligence , computer vision , acoustics , geometry , oceanography , mathematics , engineering , physics , linguistics , philosophy , robot , mobile robot
Estimating the area of seabed surfaces from pictures or videos is an important problem in seafloor surveys. This task is complex to achieve with moving platforms such as submersibles, towed or remotely operated vehicles (ROV), where the recording camera is typically not static and provides an oblique view of the seafloor. A new method for obtaining seabed surface area estimates is presented here, using the classical set up of two laser devices fixed to the ROV frame projecting two parallel lines over the seabed. By combining lengths measured directly from the image containing the laser lines, the area of seabed surfaces is estimated, as well as the camera’s distance to the seabed, pan and tilt angles. The only parameters required are the distance between the parallel laser lines and the camera’s horizontal and vertical angles of view. The method was validated with a controlled in situ experiment using a deep-sea ROV, yielding an area estimate error of 1.5%. Further applications and generalizations of the method are discussed, with emphasis on deep-sea applications.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom