Creating 3-dimensional (3D) models of underwater scenes has become a common approach for monitoring coral reef changes and its structural complexity. Also in underwater archeology, 3D models are often created using underwater optical imagery. In this paper, we focus on the aspect of detecting small changes in the coral reef using a multi-temporal photogrammetric modelling approach, which requires a high quality control network. We show that the quality of a good geodetic network limits the direct change detection, i.e., without any further registration process. As the photogrammetric accuracy is expected to exceed the geodetic network accuracy by at least one order of magnitude, we suggest to do a fine registration based on a number of signalized points. This work is part of the Moorea Island Digital Ecosystem Avatar (IDEA) project that has been initiated in 2013 by a group of international researchers (https://mooreaidea.ethz.ch/).
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Coral Identification and Counting with an Autonomous Underwater Vehicle
Monitoring coral reef populations as part of
environmental assessment is essential. Recently, many marine
science researchers are employing low-cost and power efficient
Autonomous Underwater Vehicles (AUV) to survey coral reefs.
While the counting problem, in general, has rich literature, little
work has focused on estimating the density of coral population
using AUVs. This paper proposes a novel approach to identify,
count, and estimate coral populations. A Convolutional Neural
Network (CNN) is utilized to detect and identify the different
corals, and a tracking mechanism provides a total count for
each coral species per transect. Experimental results from
an Aqua2 underwater robot and a stereo hand-held camera
validated the proposed approach for different image qualities.
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- Award ID(s):
- 1659514
- PAR ID:
- 10166774
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)
- Page Range / eLocation ID:
- 524 to 529
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The evolution of underwater photogrammetry allows to realize 3D models of submerged object and structures throughout the use of rapid and efficient procedures either in terms of data acquisition and data processing. These procedures are based on solutions that are applied using natural control points, signalized markers and tie points; the most common algorithms are based on Structure from Motion (SfM) approach. The limit of these applications is sometimes due to the final accuracy, especially when the goal is a centimeter level of accuracy. This accuracy should be necessary when dealing with a survey devoted to deformation control purposes. An example is the underwater photogrammetry for the determination of coral growth; it is effectively a movement or a deformation detection issue where the geometric change is almost at centimeter or few centimeters accuracy level. When dealing with deformation control applications, a geodetic network is essential to realize a stable and unambiguous reference frame through the accurate and permanent installation of Ground Control Points (GCPs). Such a network, indeed, permits a robust reference frame for the georeferencing of images blocks in the different époques of data acquisition. Therefore, the comparison among subsequent photogrammetric restitutions is based on homogeneous 3D models that have been oriented in the same absolute reference system. The photogrammetric survey is based on a methodological approach especially adapted to underwater biometry (like coral growth determination) and to underwater archaeology. The approach is suitable both for modeling objects of relatively reduced dimensions and for structures with a length of ten meters or more, such as coral barriers, wrecks and long walls. The paper describes underwater photogrammetric surveys on sites at different extensions, the geodetic GCPs reference network installation and measurements (distance and elevation difference observations) as well as preliminary results of the network adjustment. A brief description of image acquisition at a different scales and the resulting 3D model of first campaign are also shown.more » « less
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Abstract—The current approach to exploring and monitoring complex underwater ecosystems, such as coral reefs, is to conduct surveys using diver-held or static cameras, or deploying sensor buoys. These approaches often fail to capture the full variation and complexity of interactions between different reef organisms and their habitat. The CUREE platform presented in this paper provides a unique set of capabilities in the form of robot behaviors and perception algorithms to enable scientists to explore different aspects of an ecosystem. Examples of these capabilities include low-altitude visual surveys, soundscape surveys, habitat characterization, and animal following. We demonstrate these capabilities by describing two field deployments on coral reefs in the US Virgin Islands. In the first deployment, we show that CUREE can identify the preferred habitat type of snapping shrimp in a reef through a combination of a visual survey, habitat characterization, and a soundscape survey. In the second deployment, we demonstrate CUREE’s ability to follow arbitrary animals by separately following a barracuda and stingray for several minutes each in midwater and benthic environments, respectively.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ROBIO.2018.8664785
