DeepSurveyCam—A deep ocean optical mapping system

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Abstract

Underwater photogrammetry and in particular systematic visual surveys of the deep sea are by far less developed than similar techniques on land or in space. The main challenges are the rough conditions with extremely high pressure, the accessibility of target areas (container and ship deployment of robust sensors, then diving for hours to the ocean floor), and the limitations of localization technologies (no GPS). The absence of natural light complicates energy budget considerations for deep diving flash-equipped drones. Refraction effects influence geometric image formation considerations with respect to field of view and focus, while attenuation and scattering degrade the radiometric image quality and limit the effective visibility. As an improvement on the stated issues, we present an AUV-based optical system intended for autonomous visual mapping of large areas of the seafloor (square kilometers) in up to 6000 m water depth. We compare it to existing systems and discuss tradeoffs such as resolution vs. mapped area and show results from a recent deployment with 90,000 mapped square meters of deep ocean floor.

Figures

  • Figure 1. (a) The GEOMAR AUV ABYSS prior to launch, equipped with (b) a high resolution camera behind a dome port and (c) a novel LED flash system.
  • Figure 2. Sketch of image overlap. When a camera with field of view α observes the seafloor from an altitude of h, this creates a footprint of f. After a horizontal movement of d the overlap fraction computes as (f − d)/f, where f can be expressed as 2h × tan (α/2) and d as the product of velocity v and interval t.
  • Table 1. A non-exhaustive selection of ocean floor imaging systems of the last two decades reveals the spectrum of applications and solutions, including the original and new camera configurations of the GEOMAR AUV ABYSS. Abbreviations for methods are (M) mosaicking, (SL) structured light, (P) photogrammetry, either using stereo or structure-from-motion. Measures of efficiency are hard to normalize due to non-uniform information on actual image overlap e.g., during nonlinear vehicle tracks. Therefore, the (FoV) field of view, (V) velocity over ground, (t) survey duration or (A) total area covered are reported.
  • Figure 3. Schematic overview of the components of the newly designed high-altitude camera system for the GEOMAR Remus 6000 AUV.
  • Figure 4. An unprocessed sample image of the system in the test pool of the German Center for Artificial Intelligence (DFKI) in Bremen shows the illumination pattern and achievable resolution in clear water conditions. The inset image shows an enlarged portion of the resolution target in the image center.
  • Figure 5. Sample images used in calibration of the camera. The checkerboard is presented in different positions and orientations (a–d) relative to the camera in order to obtain the 3D ray associated to each pixel in the image.
  • Figure 6. An area of approximately 200 m × 450 m in the DISCOL experimental area of the south-east Pacific ocean offshore Peru. The photo-mosaic consists of 13,000 photos taken from an altitude of 4.7 m on average, captured during 3.5 h by the novel camera system of GEOMAR's AUV Abyss in 4100 m water depth. The tracks are 8 m wide plowmarks (a) from a 1989 experiment to simulate deep sea mining and they are well visible in 2015. The resolution of the images captured (b), undistorted but not color corrected) allows to systematically evaluate megafauna that recolonized the area. Asterisks mark the positions of manual offset measurements.
  • Figure 7. Photogra etric dense point cloud reconstruction delivers geo etry ith approx. 1 resolution from an altitude of 4.7 m. Renderings show (a) artificially perturbated sediment with manganese nodules and holothurian and (b) perturbated sediment and the excavation footprint of a box corer. (c,d) Show the shaded relief of (a,b), respectively, while (e,f) show details of epibenthic organisms (white circles) depicted with their rough geometrical shape. Point density equals about one per 2 mm. Compare Figure 6b for a source image of areas (b,d).

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CITATION STYLE

APA

Kwasnitschka, T., Köser, K., Sticklus, J., Rothenbeck, M., Weiß, T., Wenzlaff, E., … Greinert, J. (2016). DeepSurveyCam—A deep ocean optical mapping system. Sensors (Switzerland), 16(2). https://doi.org/10.3390/s16020164

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