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Metadata of a Large Sonar and Stereo Camera Dataset Suitable for Sonar-to-RGB Image Translation
<h1>Metadata of a Large Sonar and Stereo Camera Dataset Suitable for Sonar-to-RGB Image Translation</h1> <h2>Introduction</h2> <p>This is a set of metadata describing a large dataset of synchronized sonar and stereo camera recordings, that were captured between August 2021 and September 2023 during the project <a href="https://robotik.dfki-bremen.de/en/research/projects/deepersense/">DeeperSense</a> (https://robotik.dfki-bremen.de/en/research/projects/deepersense/), as training data for Sonar-to-RGB image translation. <a href="../records/7728089">Parts</a> <a href="../records/10220989">of</a> the sensor data have been published (https://zenodo.org/records/7728089, https://zenodo.org/records/10220989). Due to the size of the sensor data corpus, it is currently impractical to make the entire corpus accessible online. Instead, this metadatabase serves as a relatively compact representation, allowing interested researchers to inspect the data, and select relevant portions for their particular use case, which will be made available on demand. This is an effort to comply with the <a href="https://www.go-fair.org/fair-principles/">FAIR</a> principle A2 (https://www.go-fair.org/fair-principles/) that metadata shall be accessible, even when the base data is not immediately.</p> <h3>Locations and sensors</h3> <p>The sensor data was captured at four different locations, including one laboratory (Maritime Exploration Hall at DFKI RIC Bremen) and three field locations (Chalk Lake Hemmoor, Tank Wash Basin Neu-Ulm, Lake Starnberg). At all locations, a ZED camera and a Blueprint Oculus M1200d sonar were used. Additionally, a SeaVision camera was used at the Maritime Exploration Hall at DFKI RIC Bremen and at the Chalk Lake Hemmoor. The <code>examples/</code> directory holds a typical output image for each sensor at each available location.</p> <h3>Data volume per session</h3> <p>Six data collection sessions were conducted. The table below presents an overview of the amount of data captured in each session:</p> <table> <tbody> <tr> <th>Session dates</th> <th>Location</th> <th>Number of datasets</th> <th>Total duration of datasets [h]</th> <th>Total logfile size [GB]</th> <th>Number of images</th> <th>Total image size [GB]</th> </tr> <tr> <td>2021-08-09 - 2021-08-12</td> <td>Maritime Exploration Hall at DFKI RIC Bremen</td> <td>52</td> <td>10.8</td> <td>28.8</td> <td>389’047</td> <td>88.1</td> </tr> <tr> <td>2022-02-07 - 2022-02-08</td> <td>Maritime Exploration Hall at DFKI RIC Bremen</td> <td>35</td> <td>4.4</td> <td>54.1</td> <td>629’626</td> <td>62.3</td> </tr> <tr> <td>2022-04-26 - 2022-04-28</td> <td>Chalk Lake Hemmoor</td> <td>52</td> <td>8.1</td> <td>133.6</td> <td>1’114’281</td> <td>97.8</td> </tr> <tr> <td>2022-06-28 - 2022-06-29</td> <td>Tank Wash Basin Neu-Ulm</td> <td>42</td> <td>6.7</td> <td>144.2</td> <td>824’969</td> <td>26.9</td> </tr> <tr> <td>2023-04-26 - 2023-04-27</td> <td>Maritime Exploration Hall at DFKI RIC Bremen</td> <td>55</td> <td>7.4</td> <td>141.9</td> <td>739’613</td> <td>9.6</td> </tr> <tr> <td>2023-09-01 - 2023-09-02</td> <td>Lake Starnberg</td> <td>19</td> <td>2.9</td> <td>40.1</td> <td>217’385</td> <td>2.3</td> </tr> <tr> <th> </th> <th> </th> <th>255</th> <th>40.3</th> <th>542.7</th> <th>3’914’921</th> <th>287.0</th> </tr> </tbody> </table> <h2>Data and metadata structure</h2> <h3>Sensor data corpus</h3> <p>The sensor data corpus comprises two processing stages:</p> <ul> <li>raw data streams stored in ROS bagfiles (aka <strong>logfiles</strong>),</li> <li>camera and sonar images (aka <strong>datafiles</strong>) extracted from the logfiles.</li> </ul> <p>The files are stored in a file tree hierarchy which groups them by session, dataset, and modality:</p> <pre><code>${session_key}/ ${dataset_key}/ ${logfile_name} ${modality_key}/ ${datafile_name}</code></pre> <p>A typical logfile path has this form:</p> <pre><code>2023-09_starnberg_lake/ 2023-09-02-15-06_hydraulic_drill/ stereo_camera-zed-2023-09-02-15-06-07.bag</code></pre> <p>A typical datafile path has this form:</p> <pre><code>2023-09_starnberg_lake/ 2023-09-02-15-06_hydraulic_drill/ zed_right/ 1693660038_368077993.jpg</code></pre> <p>All directory and file names, and their particles, are designed to serve as identifiers in the metadatabase. Their formatting, as well as the definitions of all terms, are documented in the file <code>entities.json</code>.</p> <h3>Metadatabase</h3> <p>The metadatabase is provided in two equivalent forms:</p> <ul> <li>as a standalone <a href="https://www.sqlite.org/index.html">SQLite</a> (https://www.sqlite.org/index.html) database file <code>metadata.sqlite</code> for users familiar with SQLite,</li> <li>as a collection of CSV files in the <code>csv/</code> directory for users who prefer other tools.</li> </ul> <p>The database file has been generated from the CSV files, so each database table holds the same information as the corresponding CSV file. In addition, the metadatabase contains a series of convenience views that facilitate access to certain aggregate information.</p> <p>An entity relationship diagram of the metadatabase tables is stored in the file <code>entity_relationship_diagram.png</code>. Each entity, its attributes, and relations are documented in detail in the file <code>entities.json</code></p> <p>Some general design remarks:</p> <ul> <li>For convenience, timestamps are always given in both a human-readable form (ISO 8601 formatted datetime strings with explicit local time zone), and as seconds since the UNIX epoch.</li> <li>In practice, each logfile always contains a single stream, and each stream is stored always in a single logfile. Per database schema however, the entities <code>stream</code> and <code>logfile</code> are modeled separately, with a “many-streams-to-one-logfile” relationship. This design was chosen to be compatible with, and open for, data collections where a single logfile contains multiple streams.</li> <li>A <code>modality</code> is not an attribute of a <code>sensor</code> alone, but of a <code>datafile</code>: Because a <code>sensor</code> is an attribute of a <code>stream</code>, and a single stream may be the source of multiple modalities (e.g. RGB vs. grayscale images from the same camera, or cartesian vs. polar projection of the same sonar output). Conversely, the same modality may originate from different sensors.</li> </ul> <p>As a usage example, the data volume per session which is tabulated at the top of this document, can be extracted from the metadatabase with the following SQL query:</p> <div> <pre><code><span><span>SELECT</span></span> <span> PRINTF(</span> <span> <span>'%s - %s'</span>,</span> <span> <span>SUBSTR</span>(session_start, <span>1</span>, <span>10</span>),</span> <span> <span>SUBSTR</span>(session_end, <span>1</span>, <span>10</span>)) <span>AS</span> <span>'Session dates'</span>,</span> <span> location_name_english <span>AS</span> Location,</span> <span> number_of_datasets <span>AS</span> <span>'Number of datasets'</span>,</span> <span> total_duration_of_datasets_h <span>AS</span> <span>'Total duration of datasets [h]'</span>,</span> <span> total_logfile_size_gb <span>AS</span> <span>'Total logfile size [GB]'</span>,</span> <span> number_of_images <span>AS</span> <span>'Number of images'</span>,</span> <span> total_image_size_gb <span>AS</span> <span>'Total image size [GB]'</span></span> <span><span>FROM</span></span> <span> location</span> <span> <span>JOIN</span> <span>session</span> <span>USING</span> (location_id)</span> <span> <span>JOIN</span> (</span> <span> <span>SELECT</span></span> <span> session_id,</span> <span> <span>COUNT</span>(dataset_id) <span>AS</span> number_of_datasets,</span> <span> <span>ROUND</span>(</span> <span> <span>SUM</span>(dataset_duration) <span>/</span> <span>3600</span>,</span> <span> <span>1</span>) <span>AS</span> total_duration_of_datasets_h,</span> <span> <span>ROUND</span>(</span> <span> <span>SUM</span>(total_logfile_size) <span>/</span> <span>10e9</span>,</span> <span> <span>1</span>) <span>AS</span> total_logfile_size_gb</span> <span> <span>FROM</span></span> <span> location</span> <span> <span>JOIN</span> <span>session</span> <span>USING</span> (location_id)</span> <span> <span>JOIN</span> dataset <span>USING</span> (session_id)</span> <span> <span>JOIN</span> view__dataset_total_logfile_size <span>USING</span> (dataset_id)</span> <span> <span>GROUP</span> <span>BY</span></span> <span> session_id</span> <span> ) <span>USING</span> (session_id)</span> <span> <span>JOIN</span> (</span> <span> <span>SELECT</span></span> <span> session_id,</span> <span> <span>COUNT</span>(datafile_id) <span>AS</span> number_of_images,</span> <span> <span>ROUND</span>(<span>SUM</span>(datafile_size) <span>/</span> <span>10e9</span>, <span>1</span>) <span>AS</span> total_image_size_gb</span> <span> <span>FROM</span></span> <span> <span>session</span></span> <span> <span>JOIN</span> dataset <span>USING</span> (session_id)</span> <span> <span>JOIN</span> stream <span>USING</span> (dataset_id)</span> <span> <span>JOIN</span> <span>datafile</span> <span>USING</span> (stream_id)</span> <span> <span>GROUP</span> <span>BY</span></span> <span> session_id</span> <span> ) <span>USING</span> (session_id)</span> <span><span>ORDER</span> <span>BY</span> session_id;</span></code></pre> </div>
DS6.SSSA-02. Human_Walking_Dataset_at_SSSA. Dataset for characterizing the walking behavior of subjects and identification of changes in the motion patterns, based on RGB-D cameras.
<p>This dataset is used for characterizing the wakling behavior of subjects. It is based on RGB-D camerasand obtained through data collection experiments at the premises of the Percro Labotory, TeCIP Intitute, Scuola Superiore Sant'Anna (Pisa, Italy). Data are collected for the gait patterns of 9 healthy participants.</p>
YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation
<p>While a great variety of 3D cameras have been introduced in recent years, most publicly available datasets for object recognition and pose estimation focus on one single camera. This dataset consists of 32 scenes that have been captured by 7 different 3D cameras, totaling 49,294 frames. This allows evaluating the sensitivity of pose estimation algorithms to the specifics of the used camera and the development of more robust algorithms that are more independent of the camera model. Vice versa, our dataset enables researchers to perform a quantitative comparison of the data from several different cameras and depth sensing technologies and evaluate their algorithms before selecting a camera for their specific task. The scenes in our dataset contain 20 different objects from the common benchmark YCB object and model set. We provide full ground truth 6DoF poses for each object, per-pixel segmentation, 2D and 3D bounding boxes and a measure of the amount of occlusion of each object.</p> <p>If you use this dataset in your research, please cite the following publication:</p> <p>T. Grenzdörffer, M. Günther, and J. Hertzberg, “YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation,” in <em>2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31-June 4, 2020</em>. IEEE, 2020.</p> <pre><code>@InProceedings{Grenzdoerffer2020ycbm, title = {{YCB-M}: A Multi-Camera {RGB-D} Dataset for Object Recognition and {6DoF} Pose Estimation}, author = {Grenzd{\"{o}}rffer, Till and G{\"{u}}nther, Martin and Hertzberg, Joachim}, booktitle = {2020 {IEEE} International Conference on Robotics and Automation, {ICRA} 2020, Paris, France, May 31-June 4, 2020}, year = {2020}, publisher = {{IEEE}} }</code></pre> <p>This paper is also available on arXiv: <a href="https://arxiv.org/abs/2004.11657">https://arxiv.org/abs/2004.11657</a></p> <p> </p> <p>To visualize the dataset, follow these instructions (tested on Ubuntu Xenial 16.04):</p> <pre><code class="language-bash"># IMPORTANT: the ROS setup.bash must NOT be sourced, otherwise the following error occurs: # ImportError: /opt/ros/kinetic/lib/python2.7/dist-packages/cv2.so: undefined symbol: PyCObject_Type # nvdu requires Python 3.5 or 3.6 sudo add-apt-repository -y ppa:deadsnakes/ppa # to get python3.6 on Ubuntu Xenial sudo apt-get update sudo apt-get install -y python3.6 libsm6 libxext6 libxrender1 python-virtualenv python-pip # create a new virtual environment virtualenv -p python3.6 venv_nvdu cd venv_nvdu/ source bin/activate # clone our fork of NVIDIA's Dataset Utilities that incorporates some essential fixes pip install -e 'git+https://github.com/mintar/Dataset_Utilities.git#egg=nvdu' # download and transform the meshes # (alternatively, unzip the meshes contained in the dataset # to <path to venv_nvdu>/lib/python3.6/site-packages/nvdu/data/ycb/aligned_cm) nvdu_ycb -s # run nvdu_viz to visualize the dataset cd <a subdirectory of the YCB-M dataset with some frames> nvdu_viz --name_filters '*.jpg' </code></pre> <p>For further details, see README.md.</p>
mmWave Radar and RGB-D Camera Sensor Data for Human Activity Recognition
<p>This is a human activity recognition dataset with measurements from both mmWave radar and camera sensor. Meanwhile, we set multiple people scenario to mimic more realistic scenes. The other dataset collected in non-LOS(line-of-sight) environment, you can visit https://zenodo.org/record/7096889#.YynBvuhBwQ8 to get it. The mmWave radar sensors used in our experiments are composed of TI IWR6843ISK-ODS, eradar ESRR(corner radar), eradar EMRR(front radar). We appreciate the support of the eradar company, that provides corner radars and front radars for us, you can visit http://en.eradartech.com/ to get more information. </p>
Mechanical Intelligence: Method Comparison Electrosensing and RGB Camera Examples Data
<p>Method Comparison Datasets for "Mechanical Intelligence for Learning Embodied Sensor-Object Relationships" published in Nature Communications [1]. </p> <p>This contains the datasets for method comparisons of ergodic sampling (our approach) and random sampling for the Electrosensory and RGB Camera examples in the paper.</p> <p>[1] Prabhakar, A., Murphey, T. Mechanical intelligence for learning embodied sensor-object relationships. <em>Nat Commun</em> 13, 4108 (2022). https://doi.org/10.1038/s41467-022-31795-2 .</p>
mmWave Radar and RGB-D Camera Sensor Data for Human Activity Recognition(2)
<p>This is a supplementary dataset, which is linked to https://zenodo.org/record/7088054#.YyVF3ehBwQ8. The dataset is composed of corner radar point cloud data and front radar point cloud data collected from environments existing obstacles between volunteers and sensors. </p>
RGB Camera Walnut Site 60 meters
Open the record for dataset details and reuse information.
Validation and Feasibility in Clinical Practice and Concordance of an Automated System Coupling an RGB-D Camera and a Software Based on Artificial Intelligence for the Measurement of Shoulder Range of
ClinicalTrials.gov study NCT05292157. IPD Sharing: NO. Countries: 1. Publications: 0.
mmWave Radar and RGB-D Camera Sensor Data for Human Activity Recognition(2)
<p>This is a supplementary dataset, which is linked to https://zenodo.org/record/7088054#.YyVF3ehBwQ8. The dataset is composed of corner radar point cloud data and front radar point cloud data collected from environments existing obstacles between volunteers and sensors. </p>
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