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ShareScore release 0.9.0
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3 results for “Forest Robotics”
Robotic Monitoring of Forests: a Dataset from the EU habitat 9210* in the Tuscan Apennines (Central Italy)
<p>Data collected between the 27th and the 28th of April 2022, in Chiusi Della Verna, Arezzo 52010 (AR), Italy, inside the Natura 2000 SAC IT5180101. The data has been acquired mainly by the legged robot ANYmal C guided by a team of both roboticists and plant scientists. </p><p>The dataset contains four different sets of data: </p><p>1) species data - photos of four indicator species of the habitat 9210 (3 typical species and 1 early warning species).</p><p>2) mapping data - three dimensional point clouds of the habitat environment.</p><p>3) autonomous monitoring mission data - photos and videos taken by the robot during the surveys, robot status, and external videos of the autonomous mission.</p><p>4) teleoperated monitoring mission data - photos and videos taken by the robot during the surveys, robot status, and external videos of the teleoperated mission.</p><p>Researchers from a variety of disciplines can benefit from using this dataset because of its multidisciplinary scope. On the one hand, robotic engineers could, for instance, benchmark the performance of the robots and test or validate their own methods using the point clouds and the information about the robot state. On the other hand, botanists could evaluate the accuracy of this data as well as the habitat's conditions using the plant videos and images that the robot captured, or computer scientists could test their AI algorithms for identifying and classifying different species using these data.</p>
A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots
<p>This dataset is a part of the supplementary materials to the 2017 RAL <a href="https://ieeexplore.ieee.org/document/7358076">article</a> with the same title.</p> <blockquote> <p>A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots</p> <p>IEEE Robotics and Automation Letters</p> <p>Alessandro Giusti, Jerome Guzzi, Dan Ciresan, Fang Lin He, Juan Pablo Rodriguez, Flavio Fontana, Matthias Faessler, Christian Forster, Jurgen Schmidhuber, Gianni A. Di Caro, Davide Scaramuzza, Luca Gambardella</p> </blockquote> <p>You can find more information on the <a href="http://bit.ly/perceivingtrails">project web page</a> (alessandrog@idsia.ch).</p> <p><strong>Dataset</strong></p> <p>Folders 001..010 contain the dataset used to train the networks. Folder 000 contains preliminary test data. Folders 011..014 contain data for testing the system.</p> <ul> <li>000 and 003 were shot with an handheld cellphone.</li> <li>001 and 002 were shot with 3 GOPRO Hero 3 cameras, fixed on the head with straps.</li> <li>004..014 were shot with 3 Bluefox cameras, fixed on a rigid helm (the same model and with the same lens as the camera mounted on the quadcopter).</li> </ul>
ROS2 bag multimodal perception dataset acquired in Portuguese forest with Modular-E robot for line vegetation clearing
<p>Rosbag data acquired:</p> <ul> <li>RGB-D data from OAK-D facing sideways;</li> <li>Pointcloud from Hokuyo 2D Lidar facing sideways;</li> <li>Pointcloud from Robosense 360º Lidar facing forward;</li> <li>RTK fix.</li> </ul>
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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.