Abstract-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.Index Terms-Information filters, Kalman filtering, machine vision, mobile robot motion planning, mobile robots, recursive estimation, robot vision systems, simultaneous localization and mapping (SLAM), underwater vehicles.

Discharge of surface‐derived meltwater at the submerged base of Greenland's marine‐terminating glaciers creates subglacial discharge plumes that rise along the glacier/ocean interface. These plumes impact submarine melting, calving, and fjord circulation. Observations of plume properties and dynamics are challenging due to their proximity to the calving edge of glaciers. Therefore, to date information on these plumes has been largely derived from models. Here we present temperature, salinity, and velocity data collected in a plume that surfaced at the edge of Saqqarliup Sermia, a midsized Greenlandic glacier. The plume is associated with a narrow core of rising waters approximately 20 m in diameter at the ice edge that spreads to a 200 m by 300 m plume pool as it reaches the surface, before descending to its equilibrium depth. Volume flux estimates indicate that the plume is primarily driven by subglacial discharge and that this has been diluted in a ratio of 1:10 by the time the plume reaches the surface. While highly uncertain, meltwater fluxes are likely 2 orders of magnitude smaller than the subglacial discharge flux. The overall plume characteristics agree with those predicted by theoretical plume models for a convection‐driven plume with limited influence from submarine melting.

No abstract

No abstract

Abstract-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 environmentallybased navigation technique robust to the peculiarities of lowoverlap 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 multi-sensor VAN framework and demonstrates it to have compelling advantages over a purely vision-only approach by: (i) improving the robustness of lowoverlap underwater image registration, (ii) setting the free gauge scale, and (iii) allowing for a disconnected camera-constraint topology.

Abstract-This paper presents the novel insight that the SLAM information matrix is exactly sparse in a delayedstate framework. Such a framework is used in view-based representations of the environment which rely upon scanmatching raw sensor data. Scan-matching raw data results in virtual observations of robot motion with respect to a place its previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent featurebased SLAM information algorithms like Sparse Extended Information Filters or Thin Junction Tree Filters. These methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparseness of the delayed-state framework is that it allows one to take advantage of the information space parameterization without having to make any approximations. Therefore, it can produce equivalent results to the "full-covariance" solution.

No abstract

Roughly 60% of the Earth's outer surface is comprised of oceanic crust formed by volcanic processes at mid-ocean ridges (MORs). Although only a small fraction of this vast volcanic terrain has been visually surveyed and/or sampled, the available evidence suggests that explosive eruptions are rare on MORs, particularly at depths below the critical point for