The UMA-SAR Dataset: Multimodal data collection from a ground vehicle during outdoor disaster response training exercises

Morales, Jesús ; Vázquez-Martín, Ricardo ; Mandow, Anthony ; Morilla-Cabello, David (Universidad de Zaragoza) ; García-Cerezo, Alfonso

Resumen: This article presents a 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.

Idioma: Inglés

DOI: 10.1177/02783649211004959

Año: 2021

Publicado en: The International Journal of Robotics Research 40, 6-7 (2021), 835-847
ISSN: 0278-3649

Factor impacto JCR: 6.887 (2021)

Categ. JCR: ROBOTICS rank: 6 / 30 = 0.2 (2021) - Q1 - T1

Factor impacto CITESCORE: 14.8 - Engineering (Q1) - Mathematics (Q1) - Computer Science (Q1)

Factor impacto SCIMAGO: 3.403 - Applied Mathematics (Q1) - Artificial Intelligence (Q1) - Software (Q1) - Modeling and Simulation (Q1) - Mechanical Engineering (Q1)

Financiación: info:eu-repo/grantAgreement/ES/AEI/RTI2018-093421-B-I00

Tipo y forma: Article (Published version)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)