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SSRN 2025年

作者： Catipovic, Luka Laney, Samuel R. Woods Hole Oceanographic Institution Woods HoleMA United States The MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering Cambridge and Woods Hole MA United States City College of New York New YorkNY United States

Highlights• ML algorithms can classify water and sea ice characteristics in the coastal Arctic.• Spring freshet runoff over sea ice is optically distinct from surface melt ponds.• Time-series analyses of satellite ima... 详细信息

关键词： Sea ice

Distributed Certifiably Correct Range-Aided SLAM

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arXiv 2025年

作者： Thoms, Alexander Papalia, Alan Velasquez, Jared Rosen, David M. Narasimhan, Sriram Sensing and Robotics for Infrastructure Laboratory University of California Los Angeles Los Angeles United States The Massachusetts Institute of Technology The MIT-WHOI Joint Program in Oceanography/Applied Ocean Science & Engineering Woods Hole CambridgeMA United States Robust Autonomy Lab Northeastern University BostonMA United States

Reliable simultaneous localization and mapping (SLAM) algorithms are necessary for safety-critical autonomous navigation. In the communication-constrained multi-agent setting, navigation systems increasingly use point-to-point range sensors as they afford measurements with low bandwidth requirements and known data association. The state estimation problem for these systems takes the form of range-aided (RA) SLAM. However, distributed algorithms for solving the RA-SLAM problem lack formal guarantees on the quality of the returned estimate. To this end, we present the first distributed algorithm for RA-SLAM that can efficiently recover certifiably globally optimal solutions. Our algorithm, distributed certifiably correct RA-SLAM (DCORA), achieves this via the Riemannian Staircase method, where computational procedures developed for distributed certifiably correct pose graph optimization are generalized to the RA-SLAM problem. We demonstrate DCORA’s efficacy on real-world multi-agent datasets by achieving absolute trajectory errors comparable to those of a state-of-the-art centralized certifiably correct RA-SLAM algorithm. Additionally, we perform a parametric study on the structure of the RA-SLAM problem using synthetic data, revealing how common parameters affect DCORA’s performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Navigation systems

Soft-Adaptive Turbo Equalization: Using Soft Information in Adaptation

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Soft-Adaptive Turbo Equalization: Using Soft Information in ...

46th Asilomar Conference on Signals, Systems and Computers

作者： Yellepeddi, Atulya Preisig, James C. Electrical Engineering/Applied Ocean Science and Engineering MIT-WHOI Joint Program USA

ISBN: (纸本)9781467350518

Turbo equalizers have been exploited for underwater wireless communication due to their excellent practical performance. As the equalizer is adaptive, it is possible to take advantage of the soft information available from the decoder in the adaptation process. In this paper, we consider a method to do this and compare it with the use of soft information in feedback, which is a method that has been considered in the past. Data from the KAM11 experiment demonstrates that using soft information in adaptation can improve performance over using it only in feedback. We also consider combining these different approaches and how this affects performance.

关键词： adaptive equalisers marine communication turbo codes

Coherent source direction estimation for three-row bathymetric sidescan sonars

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Coherent source direction estimation for three-row bathymetr...

Proceedings of the oceanS '99 MTS/IEEE - Riding the Crest into the 21st Century

作者： Xu, Wen Stewart, W.Kenneth MIT/WHOI Joint Program in Applied Ocean Physics and Engineering Cambridge United States

In the context of three-row bathymetric sidescan sonars, a coherent source direction estimator (CSDE) is proposed to resolve two sources from different direction. This method is compared with the ESPRIT-based approach using different sonar models. The simulations show that CSDE works very well for highly coherent sources and at the signal-to-noise ratio above 10dB while the ESPRIT-based method is quite robust for the incoherent source signal model. In addition, CSDE gives the source time series estimation on the basis of the direction estimation as well.

关键词： Sonar

Reprocessing the NEAT Dataset: Preliminary Results

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arXiv 2025年

作者： Nugent, C.R. Bauer, J.M. Benitez, O. Blain, M. D’Souza, N. Garimella, S. Goldwater, M. Kim, Y. Larsen, H.C.G. Linder, T. Mackowiak, K. McGinnis, Z. Pan, E. Pedersen, C.C. Sadhwani, P. Spoto, F. Tan, N.J. Vereš, P. Xue, C. Olin College of Engineering 1000 Olin Way NeedhamMA02492 United States Department of Astronomy University of Maryland College ParkMD20742 United States Department of Earth and Space Sciences College of the Canyons Santa ClaritaCA91355 United States MIT-WHOI Joint Program in Applied Ocean Science & Engineering Cambridge and Woods Hole MA United States Department of Applied Ocean Physics & Engineering Woods Hole Oceanographic Institution 266 Woods Hole Road MS #55 Woods HoleMA02543-1050 United States Aalborg University Fibigerstræde 16 Aalborg9220 Denmark Planetary Science Institute TucsonAZ85719 United States Minor Planet Center Smithsonian Astrophysical Observatory 60 Garden Street CambridgeMA02138 United States School of Physical and Chemical Sciences Te Kura Matū University of Canterbury Private Bag 4800 Christchurch8140 New Zealand

We have created a new image analysis pipeline to reprocess images taken by the Near Earth Asteroid Tracking survey and have applied it to ten nights of observations. This work is the first large-scale reprocessing of images from an asteroid discovery survey in which thousands of archived images are re-calibrated, searched for minor planets, and resulting observations are reported to the Minor Planet Center. We describe the software used to extract, calibrate, and clean sources from the images, including specific techniques that accommodate the unique features of these archival images. This pipeline is able to find fainter asteroids than the original pipeline. © 2025, CC BY.

关键词： Asteroids

Corrigendum to “Biochemical implications of the Bottom Boundary Layer Detachment at the Mid-Atlantic Bight shelf-break front” [Continent. Shelf Res. 286 (2025) 105408]

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Continental Shelf Research 2025年 288卷

作者： Serena Negroni Dennis J. McGillicuddy Glen G. Gawarkiewicz Paolo Oddo Nadia Pinardi Department of Physics and Astronomy (DIFA) University of Bologna Bologna 40126 Italy MIT/WHOI Joint Program in Oceanography/Applied Ocean Sciences and Engineering Woods Hole 02543 MA USA Woods Hole Oceanographic Institution Woods Hole 02543 MA USA

CUREE: A Curious Underwater Robot for Ecosystem Exploration

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CUREE: A Curious Underwater Robot for Ecosystem Exploration

IEEE International Conference on Robotics and Automation (ICRA)

作者： Yogesh Girdhar Nathan McGuire Levi Cai Stewart Jamieson Seth McCammon Brian Claus John E. San Soucie Jessica E. Todd T. Aran Mooney Woods Hole Oceanographic Institution (WHOI) MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering

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.

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Exactly sparse delayed-state filters

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Exactly sparse delayed-state filters

2005 IEEE International Conference on Robotics and Automation

作者： Eustice, Ryan M. Singh, Hanumant Leonard, John J. MIT/WHOI Joint Program in Applied Ocean Science and Engineering Woods Hole Oceanographic Institution Woods Hole MA United States Department of Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution Woods Hole MA United States Department of Ocean Engineering Massachusetts Institute of Technology Cambridge MA United States

ISBN: (纸本)078038914X

This paper presents the novel insight that the SLAM information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment which rely upon scan-matching 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 feature-based 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. ©2005 IEEE.

关键词： Digital filters

DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methods

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arXiv 2023年

作者： Jamieson, Stewart How, Jonathan P. Girdhar, Yogesh The MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering United States Applied Ocean Physics and Engineering Department

Successful applications of complex vision-based behaviours underwater have lagged behind progress in terrestrial and aerial domains. This is largely due to the degraded image quality resulting from the physical phenomena involved in underwater image formation. Spectrally-selective light attenuation drains some colors from underwater images while backscattering adds others, making it challenging to perform vision-based tasks underwater. State-of-the-art methods for underwater color correction optimize the parameters of image formation models to restore the full spectrum of color to underwater imagery. However, these methods have high computational complexity that is unfavourable for realtime use by autonomous underwater vehicles (AUVs), as a result of having been primarily designed for offline color correction. Here, we present DeepSeeColor, a novel algorithm that combines a state-of-the-art underwater image formation model with the computational efficiency of deep learning frameworks. In our experiments, we show that DeepSeeColor offers comparable performance to the popular "Sea-Thru" algorithm [1] while being able to rapidly process images at up to 60Hz, thus making it suitable for use onboard AUVs as a preprocessing step to enable more robust vision-based behaviours. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous underwater vehicles

Active reward learning for co-robotic vision based exploration in bandwidth limited environments

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arXiv 2020年

作者： Jamieson, Stewart How, Jonathan P. Girdhar, Yogesh MIT-WHOI Joint Program in Applied Ocean Science and Engineering United States United States

We present a novel POMDP problem formulation for a robot that must autonomously decide where to go to collect new and scientifically relevant images given a limited ability to communicate with its human operator. From this formulation we derive constraints and design principles for the observation model, reward model, and communication strategy of such a robot, exploring techniques to deal with the very highdimensional observation space and scarcity of relevant training data. We introduce a novel active reward learning strategy based on making queries to help the robot minimize path "regret" online, and evaluate it for suitability in autonomous visual exploration through simulations. We demonstrate that, in some bandwidth-limited environments, this novel regret-based criterion enables the robotic explorer to collect up to 17% more reward per mission than the next-best criterion. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine design