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The design of the GPS tracking unit (GTU): (left) The top of the GTU. The GPS tracker is the small white unit labelled 6385. It is wrapped in foam which is attached to the mat. The foam provides protection from hard deployments. (right) The underside of the tracker showing the teeth that "bite" into the ice to provide friction. C-CORE

The design of the GPS tracking unit (GTU): (left) The top of the GTU. The GPS tracker is the small white unit labelled 6385. It is wrapped in foam which is attached to the mat. The foam provides protection from hard deployments. (right) The underside of the tracker showing the teeth that "bite" into the ice to provide friction. C-CORE

Source publication
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Icebergs pose a hazard to shipping and to offshore oil and gas activities. To mitigate and better manage the risks, it is essential to measure, survey, and monitor them. Collecting in-situ measurements and data from icebergs is challenging. Icebergs frequently roll and/ or break-up. A vessel must maintain a safe separation distance from an iceberg....


... Mapping icebergs, ice islands, and ice floes and estimating their deterioration and melt rates are active areas of research. Some of the notable works that have made strides in these areas include [74,26,25,19,135,11,111]. Our work builds on this rich body of work to create detailed 3D models of icebergs multiple times and calculate accurate volume loss rate estimates along with the measurement uncertainties. ...
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Most approaches to visual navigation make multiple assumptions about the scenes being imaged. There are implicit assumptions about the scene being predominantly static and the availability of well illuminated, texture rich, objects in the scene. In some cases these assumptions severely limit or eliminate the full applicability of Visual Simultaneous Localization and Mapping (VSLAM) and Structure from Motion (SfM) methodologies. This dissertation attempts to address problems where the assumptions of static scenes and texture rich objects are not valid. Motivated by the application of mapping rotating and translating icebergs, we propose a system level solution for addressing the problem of mapping large, low contrast, moving targets with slow but complicated dynamics. Our approach leverages the complementary nature of multiple sensing modalities and utilizes a rigidly coupled combination of a subsurface multibeam sonar (a line scan sensor) and an optical camera (an area scan sensor). This allows the system to exploit the optical camera information to perform iceberg relative navigation, which can be directly used by the multibeam sonar to map the iceberg underwater. To compensate for the effect of low contrast we conduct an in-depth analysis of features detectors and descriptors on end-to-end SfM algorithms to demonstrate and understand how methodologies such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and Zernike Moment descriptors help improve the overall accuracy in these challenging applications. We merge these approaches into an algorithmic framework that allows us to compute the scale of the navigation solution and iceberg centric navigation corrections. These corrections can then be used for accurate iceberg reconstructions. This enables a quantitative analysis of our iceberg mapping efforts including volume estimation and change detection. We successfully demonstrate our approach on real field data from three of the icebergs surveyed multiple times during the 2018 and 2019 campaigns to the Sermilik fjord in Eastern Greenland. Availability of iceberg mounted Global Navigation Satellite System (GNSS) observations during these research expeditions also allowed for a comparison of this approach against ground truth, providing additional confidence in the systems level mapping efforts. The accuracy of the reconstructions is demonstrated by estimating iceberg volumes, calculating their ablation rates, and performing change detection at a granular scale.
... The major improvements are: implementation of gradient-boosted trees instead of NN, and a hybrid approach instead of a "black box" model. In addition, the model is trained on new GPS iceberg tracks collected during an iceberg tagging campaign off the coast of Labrador in 2019 (Briggs et al., 2020). ...
... The iceberg GPS tracks were collected during a tagging campaign offshore Newfoundland and Labrador in 2019. Unmanned Aerial Vehicles (UAVs) were used to deliver the GPS trackers from the vessel to the icebergs (Briggs et al., 2020). Each of the trackers consisted of a GPS unit attached to a rubber mat with toothed metal plates. ...
Conference Paper
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A machine learning approach has been developed to improve iceberg drift forecasting on a tactical timescale to mitigate potential impacts between icebergs and offshore facilities in the North Atlantic. The problem of iceberg forecasting involves many uncertain terms. Although iceberg drift trajectories can be measured with sufficient accuracy, the iceberg shapes are generally unknown. Moreover, the corresponding metocean forecasts, in particular ocean current forecasts, carry their own uncertainties. The forecasting error for this highly uncertain drift process can be minimized using a hybrid approach that calculates Coriolis acceleration explicitly and estimates combined hydrodynamic and wind drag accelerations using machine learning. The latter is an implementation of gradient boosted trees algorithm that predicts and integrates the unknown accelerations. The training dataset consists of iceberg GPS tracks obtained during 2019 iceberg data collection campaign offshore Labrador. The trained model is cross-validated using the k-folds approach. The evolution of the average error between the observed and forecast tracks is used as the measure of performance. The resulting error curve is promising and can be improved even further if more, and more accurate in-situ data are collected. The trained model can be used operationally, together with conventional dynamic approach, as an additional source of information in order to determine an adequate response to iceberg threat.