Eustice Ryan M.

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Eustice
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Ryan M.
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  • Preprint
    Toward extraplanetary under-ice exploration : robotic steps in the Arctic
    ( 2009-01-12) Kunz, Clayton G. ; Murphy, Christopher A. ; Singh, Hanumant ; Pontbriand, Claire W. ; Sohn, Robert A. ; Singh, Sandipa ; Sato, Taichi ; Roman, Christopher N. ; Nakamura, Ko-ichi ; Jakuba, Michael V. ; Eustice, Ryan M. ; Camilli, Richard ; Bailey, John
    This paper describes the design and use of two new autonomous underwater vehicles, Jaguar and Puma, which were deployed in the summer of 2007 at sites at 85°N latitude in the ice-covered Arctic Ocean to search for hydrothermal vents. These robots are the first to be deployed and recovered through ice to the deep ocean (> 3500m) for scientific research. We examine the mechanical design, software architecture, navigation considerations, sensor suite and issues with deployment and recovery in the ice based on the missions they carried out. Successful recoveries of vehicles deployed under the ice requires two-way acoustic communication, flexible navigation strategies, redundant localization hardware, and software that can cope with several different kinds of failure. The ability to direct an AUV via the low bandwidth and intermittently functional acoustic channel, is of particular importance. Based on our experiences, we also discuss the applicability of the technology and operational approaches of this expedition to the exploration of Jupiter's ice-covered moon Europa.
  • Preprint
    Characterizing the deep insular shelf coral reef habitat of the Hind Bank marine conservation district (US Virgin Islands) using the Seabed autonomous underwater vehicle
    ( 2005-10-26) Armstrong, Roy A. ; Singh, Hanumant ; Torres, Juan ; Nemeth, Richard S. ; Can, Ali ; Roman, Christopher N. ; Eustice, Ryan M. ; Riggs, Lauren ; Garcia-Moliner, Graciela
    The benthic communities of the deep insular shelf at the Hind Bank Marine Conservation District (MCD), an important spawning grouper aggregation site, were studied with the Seabed autonomous underwater vehicle (AUV) at depths between 32 to 54 m. Four digital phototransects provided data on benthic species composition and abundance of the insular shelf off St. Thomas, U.S. Virgin Islands. Within the western side of the MCD, well developed coral reefs with 43% mean living coral cover were found. The Montastrea annularis complex was dominant at all four sites between 33 to 47 m, the depth range where reefs were present. Maximum coral cover found was 70% at depths of 38 to 40 m. Quantitative determinations of sessile-benthic populations, as well as the presence of motile-megabenthic invertebrates and algae were obtained. The Seabed AUV provided new quantitative and descriptive information of a unique coral reef habitat found within this deeper insular shelf area.
  • Preprint
    Active methane venting observed at giant pockmarks along the U.S. mid-Atlantic shelf break
    ( 2007-11) Newman, Kori R. ; Cormier, Marie-Helene ; Weissel, Jeffrey K. ; Driscoll, Neal W. ; Kastner, Miriam ; Solomon, Evan A. ; Robertson, Gretchen ; Hill, Jenna C. ; Singh, Hanumant ; Camilli, Richard ; Eustice, Ryan M.
    Detailed near-bottom investigation of a series of giant, kilometer scale, elongate pockmarks along the edge of the mid-Atlantic continental shelf confirms that methane is actively venting at the site. Dissolved methane concentrations, which were measured with a commercially available methane sensor (METS) designed by Franatech GmbH mounted on an autonomous underwater vehicle (AUV), are as high as 100 nM. These values are well above expected background levels (1-4 nM) for the open ocean. Sediment pore water geochemistry gives further evidence of methane advection through the seafloor. Isotopically light carbon in the dissolved methane samples indicates a primarily biogenic source. The spatial distribution of the near-bottom methane anomalies (concentrations above open ocean background), combined with water column salinity and temperature vertical profiles, indicate that methane-rich water is not present across the entire width of the pockmarks, but is laterally restricted to their edges. We suggest that venting is primarily along the top of the pockmark walls with some advection and dispersion due to local currents. The highest methane concentrations observed with the METS sensor occur at a small, circular pockmark at the southern end of the study area. This observation is compatible with a scenario where the larger, elongate pockmarks evolve through coalescing smaller pockmarks.
  • Article
    The 2005 Chios ancient shipwreck survey : new methods for underwater archaeology
    (American School of Classical Studies at Athens, 2009-04) Foley, Brendan P. ; Dellaporta, Katerina ; Sakellariou, Dimitris ; Bingham, Brian S. ; Camilli, Richard ; Eustice, Ryan M. ; Evagelistis, Dionysis ; Ferrini, Vicki L. ; Katsaros, Kostas ; Kourkoumelis, Dimitris ; Mallios, Angelos ; Micha, Paraskevi ; Mindell, David A. ; Roman, Christopher N. ; Singh, Hanumant ; Switzer, David S. ; Theodoulou, Theotokis
    In 2005 a Greek and American interdisciplinary team investigated two shipwrecks off the coast of Chios dating to the 4th-century b.c. and the 2nd/1st century. The project pioneered archaeological methods of precision acoustic, digital image, and chemical survey using an autonomous underwater vehicle (AUV) and in-situ sensors, increasing the speed of data acquisition while decreasing costs. The AUV recorded data revealing the physical dimensions, age, cargo, and preservation of the wrecks. The earlier wreck contained more than 350 amphoras, predominantly of Chian type, while the Hellenistic wreck contained about 40 Dressel 1C amphoras. Molecular biological analysis of two amphoras from the 4th-century wreck revealed ancient DNA of olive, oregano, and possibly mastic, part of a cargo outbound from Chios.
  • Article
    Exactly sparse delayed-state filters for view-based SLAM
    (IEEE, 2006-12) Eustice, Ryan M. ; Singh, Hanumant ; Leonard, John J.
    This paper reports the novel insight that the simultaneous localization and mapping (SLAM) information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment that rely upon scan-matching raw sensor data to obtain virtual observations of robot motion with respect to a place it has previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature-based SLAM information algorithms, such as sparse extended information filter or thin junction-tree filter, since these methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparsity of the delayed-state framework is that it allows one to take advantage of the information space parameterization without incurring any sparse approximation error. Therefore, it can produce equivalent results to the full-covariance solution. The approach is validated experimentally using monocular imagery for two datasets: a test-tank experiment with ground truth, and a remotely operated vehicle survey of the RMS Titanic.
  • Article
    Visually augmented navigation for autonomous underwater vehicles
    (IEEE, 2008-04) Eustice, Ryan M. ; Pizarro, Oscar ; Singh, Hanumant
    As autonomous underwater vehicles (AUVs) are becoming routinely used in an exploratory context for ocean science, the goal of visually augmented navigation (VAN) is to improve the near-seafloor navigation precision of such vehicles without imposing the burden of having to deploy additional infrastructure. This is in contrast to traditional acoustic long baseline navigation techniques, which require the deployment, calibration, and eventual recovery of a transponder network. To achieve this goal, VAN is formulated within a vision-based simultaneous localization and mapping (SLAM) framework that exploits the systems-level complementary aspects of a camera and strap-down sensor suite. The result is an environmentally based navigation technique robust to the peculiarities of low-overlap underwater imagery. The method employs a view-based representation where camera-derived relative-pose measurements provide spatial constraints, which enforce trajectory consistency and also serve as a mechanism for loop closure, allowing for error growth to be independent of time for revisited imagery. This article outlines the multisensor VAN framework and demonstrates it to have compelling advantages over a purely vision-only approach by: 1) improving the robustness of low-overlap underwater image registration; 2) setting the free gauge scale; and 3) allowing for a disconnected camera-constraint topology.
  • Thesis
    Large-area visually augmented navigation for autonomous underwater vehicles
    (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 2005-06) Eustice, Ryan M.
    This thesis describes a vision-based, large-area, simultaneous localization and mapping (SLAM) algorithm that respects the low-overlap imagery constraints typical of autonomous underwater vehicles (AUVs) while exploiting the inertial sensor information that is routinely available on such platforms. We adopt a systems-level approach exploiting the complementary aspects of inertial sensing and visual perception from a calibrated pose-instrumented platform. This systems-level strategy yields a robust solution to underwater imaging that overcomes many of the unique challenges of a marine environment (e.g., unstructured terrain, low-overlap imagery, moving light source). Our large-area SLAM algorithm recursively incorporates relative-pose constraints using a view-based representation that exploits exact sparsity in the Gaussian canonical form. This sparsity allows for efficient O(n) update complexity in the number of images composing the view-based map by utilizing recent multilevel relaxation techniques. We show that our algorithmic formulation is inherently sparse unlike other feature-based canonical SLAM algorithms, which impose sparseness via pruning approximations. In particular, we investigate the sparsification methodology employed by sparse extended information filters (SEIFs) and offer new insight as to why, and how, its approximation can lead to inconsistencies in the estimated state errors. Lastly, we present a novel algorithm for efficiently extracting consistent marginal covariances useful for data association from the information matrix. In summary, this thesis advances the current state-of-the-art in underwater visual navigation by demonstrating end-to-end automatic processing of the largest visually navigated dataset to date using data collected from a survey of the RMS Titanic (path length over 3 km and 3100 m2 of mapped area). This accomplishment embodies the summed contributions of this thesis to several current SLAM research issues including scalability, 6 degree of freedom motion, unstructured environments, and visual perception.