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5 results for “auv”
CMOP 2012 AUV-WP Columbia River estuary benchmark data set
<p>Observational data set from the Columbia River estuary North Channel. The data were collected during two campaigns during May and October-November 2012.</p> <p>Included are Automated Underwater (AUV) mission, and ship-mounted whiched profiler (WP) data sets. Outputs from numerical circulation model are also included.</p> <p>See the README.txt in each zip file for further information.</p>
AUV-Based Multi-Sensor Dataset: Forward-Looking Camera (FLC) and Forward-Looking Sonar (FLS) Observations in the Red Sea
<p><strong>Context</strong></p> <p>This dataset is the first part of a dataset collection comprised of forward-looking sonar (FLS) and forward-looking camera (FLC) underwater images. The entire data was collected during the years 2021-2023 using 2 underwater vehicles in both the Red Sea and the Mediterranean along the Israeli shoreline, depicting both man-made and natural underwater environments. The data is part of a research project aimed at developing fusion models for improved obstacle detection and navigation in autonomous underwater vehicles.</p> <p><strong>Content</strong></p> <p>This dataset consists of FLC and FLS images and their metadata, collected by the ALICE-AUV. Both sensors were installed in the front payload section in a configuration having aligned fields of view to achieve matching pairs of data. The data was collected to train and evaluate a complete perception and obstacle avoidance framework.</p> <p>A series of diving sessions were performed in the Red Sea, off the coast of Eilat, Israel. The experiments focused on two main sites: A "Sunboat" shipwreck and the Eilat-Ashkelon Pipeline Company (EAPC) pier pillars. The "Sunboat" shipwreck is a 40-meter long vessel resting at a depth of approximately 12 meters, with the surrounding seabed at a depth of 18-24 meters. This dataset contains approximately 8,000 FLC-FLS sample pairs from the first session conducted at the "Sun boat" shipwreck site on September 3, 2023. The data was recorded at depths ranging from 10 to 15 meters.</p> <p>The dataset is organized into separate sessions, each representing a specific dive or experiment. Within each session, the data is further categorized into modalities: camera (FLC images), sonar (FLS images), and navigation (dead reckoning data). The navigation data is derived from a combination of GPS, DVL, and IMU sensors, providing estimated positions when GPS is unavailable. Inside each modality directory, you will find the corresponding data files in PNG format for images and CSV format for navigation data. The file names follow a sequential numbering scheme (e.g., 00001.png, 00002.png, etc.). Each modality directory also contains a CSV file (e.g., camera.csv) that maps each data file to its respective timestamp. Additionally, the samples.json file documents the relationship between uni-modal and multi-modal samples, allowing for easy association of data from different modalities.</p> <p>By providing synchronized and aligned camera and sonar imagery, along with corresponding navigation data, this dataset enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in the context of autonomous underwater vehicles.</p> <p><strong>Technical Details</strong></p> <ul> <li>Sonar: Blueprint Oculus M1200d <ul> <li>Operating frequency: 1.2 MHz (low frequency mode)</li> <li>Maximum range: 40 m (set to 20 m for this dataset)</li> <li>Horizontal aperture: 130°</li> <li>Vertical aperture: 20°</li> <li>Number of beams: 512</li> <li>Angular resolution: 0.6°</li> <li>Beam separation: 0.25°</li> <li>Image resolution: 902x497 pixels</li> <li>Coordinate system: Polar</li> </ul> </li> <li>Camera: Allied-Vision Manta G-917 <ul> <li>Image dimensions: 3384x2710 pixels (downscaled to 1692x1355 for this dataset)</li> <li>Sensor type: CCD Progressive</li> <li>Sensor bit depth: 12-bit</li> <li>Captured bit depth: 8-bit</li> <li>Camera model: Pinhole with Plumb Bob (Brown–Conrady) distortion coefficients</li> <li>Focal length (fx, fy): (1638.36157, 1641.95202)</li> <li>Principal point (cx, cy): (1705.03529, 1380.27954)</li> <li>Radial distortion coefficients (k1, k2, k3): (-0.124823, 0.048851, 0.000000)</li> <li>Tangential distortion coefficients (p1, p2): (0.000259, -0.002945)</li> </ul> </li> <li>Navigation: <ul> <li>Data format: CSV</li> <li>Contains fused dead reckoning data based on GPS, DVL, and IMU sensors</li> <li>Columns: <ul> <li>timestamp: Unix timestamp (seconds)</li> <li>latitude: Latitude (degrees)</li> <li>longitude: Longitude (degrees)</li> <li>altitude: Altitude (meters)</li> <li>yaw: Yaw angle (degrees)</li> <li>pitch: Pitch angle (degrees)</li> <li>roll: Roll angle (degrees)</li> <li>velocity_x: Velocity along the x-axis (meters per second)</li> <li>velocity_y: Velocity along the y-axis (meters per second)</li> <li>velocity_z: Velocity along the z-axis (meters per second)</li> <li>depth: Depth (meters)</li> </ul> </li> </ul> </li> <li>Frame rate: 2 Hz for both sonar and camera</li> </ul> <p>More datasets from this collection will be uploaded in the future, and a link to access them will be provided on this page.</p> <p><strong>Acknowledgements</strong></p> <p>The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.</p>
A Deep-Learning Approach for Visual Detection of an AUV Docking Station - Dataset
<div>This dataset was used to train the models from the paper "A Deep-Learning Approach for Visual Detection of an AUV Docking Station" published by Ahmad et al. at Oceans 2024 Conference in Halifax.</div> <div> </div> <div># Dataset</div> <div> </div> <div>The dataset contains:</div> <div> </div> <div>1. images from Abisko Lake in Sweden[1]</div> <div>2. images recorded in the Maritime Hall basin at DFKI</div> <div> </div> <div># File contents</div> <div>Each of these datasets are put into seperate directories. The images were annotated using CVAT[2].</div> <div> </div> <div>The dataset has been exported into the following formats:</div> <div> </div> <div>1. YOLO</div> <div>2. PascalVOC</div> <div>3. COCO</div> <div> </div> <div>The exported datasets does not contain raw images, rather they are places into a seperate zip folder.</div> <div> </div> <div> </div> <div> </div> <div># References</div> <div>[1]: <a href="https://zenodo.org/records/7035132">https://zenodo.org/record/7035132#.ZDfKE5FBzJU</a></div> <div>[2]: https://www.cvat.ai/</div>
AUV observations of Langmuir turbulence in a stratified shelf sea
Open the record for dataset details and reuse information.
Processed grid of bathymetry and backscatter data at the Southwest Indian Ridge 50°28'E (AUV, DY40 cruise in 2016)
<p>Bathymetry and backscatter data used for this dataset were recorded during the DY40 cruise aboard R/V Xiangyanghong-10, using High-Resolution Bathymetric Sidescan Sonar System equipped by AUV QianLong-Ⅱ. The cruise took place in 2016 in the Southwest Indian Ridge 50°28'E. The sonar system was operated at 150 Hz with a swath width of 250 m, and the AUV was surveyed at an altitude of ~100 m above the seafloor at a speed of 1-2 kt with a track spacing of 400 m.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.