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dc.contributorRobotics and Mechatronics Group (TEP-119)es_ES
dc.contributor.authorMorales-Rodríguez, Jesús 
dc.contributor.authorVázquez-Martín, Ricardo 
dc.contributor.authorMandow, Anthony 
dc.contributor.authorMorilla-Cabello David
dc.contributor.authorGarcía-Cerezo, Alfonso José 
dc.contributor.otherIngeniería de Sistemas y Automáticaes_ES
dc.date.accessioned2022-04-05T12:36:01Z
dc.date.available2022-04-05T12:36:01Z
dc.date.issued2022-04-05
dc.identifier.urihttps://hdl.handle.net/10630/23918
dc.description.abstract- [The full description of the dataset can be found at: https://www.uma.es/robotics-and-mechatronics/info/124594/sar-datasets/ ] - Collection of multimodal raw data captured from a manned all-terrain vehicle in the course of two realistic outdoor search and rescue (SAR) exercises for actual emergency responders conducted in Málaga (Spain) in 2018 and 2019: the UMA-SAR dataset. The sensor suite, applicable to unmanned ground vehicles (UGVs), consisted of overlapping visible light (RGB) and thermal infrared (TIR) forward-looking monocular cameras, a Velodyne HDL-32 three-dimensional (3D) lidar, as well as an inertial measurement unit (IMU) and two global positioning system (GPS) receivers as ground truth. Our mission was to collect a wide range of data from the SAR domain, including persons, vehicles, debris, and SAR activity on unstructured terrain. In particular, four data sequences were collected following closed-loop routes during the exercises, with a total path length of 5.2 km and a total time of 77 min. In addition, we provide three more sequences of the empty site for comparison purposes (an extra 4.9 km and 46 min). Furthermore, the data is offered both in human-readable format and as rosbag files, and two specific software tools are provided for extracting and adapting this dataset to the users’ preference. The review of previously published disaster robotics repositories indicates that this dataset can contribute to fill a gap regarding visual and thermal datasets and can serve as a research tool for cross-cutting areas such as multispectral image fusion, machine learning for scene understanding, person and object detection, and localization and mapping in unstructured environments.es_ES
dc.description.sponsorshipThis work has been performed in the frame of the project “TRUST-ROB: Towards Resilient UGV and UAV Manipulator Teams for Robotic Search and Rescue Tasks,” funded by the Spanish Government (grant number RTI2018-093421-B-I00) and project UMA18-FEDERJA-090 funded by the Andalusian Regional Government (Junta de Andalucía)es_ES
dc.language.isoenges_ES
dc.publisherUniversidad de Málagaes_ES
dc.relation.isreferencedbyMorales J, Vázquez-Martín R, Mandow A, Morilla-Cabello D, García-Cerezo A. The UMA-SAR Dataset: Multimodal data collection from a ground vehicle during outdoor disaster response training exercises. The International Journal of Robotics Research. 2021;40(6-7):835-847. doi:10.1177/02783649211004959es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectRobóticaes_ES
dc.subject.otherDisaster roboticses_ES
dc.subject.other3D LIDARes_ES
dc.subject.otherSearch and rescuees_ES
dc.subject.otherMultimodal sensorses_ES
dc.subject.otherMultispectral imaginges_ES
dc.subject.otherThermal infrared cameraes_ES
dc.subject.otherDatasetes_ES
dc.titleThe UMA-SAR Dataset: Multimodal data collection from a ground vehicle during outdoor disaster response training exerciseses_ES
dc.typeinfo:eu-repo/semantics/datasetes_ES
dc.identifier.doi10.24310/riuma.23918
dc.rights.ccAtribución-NoComercial-CompartirIgual 4.0 Internacional*
dc.publication.year2022
dc.version1es_ES


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