The ever increasing intensity and number of disasters make even more difficult the work of First Responders (FRs). Artificial intelligence and robotics solutions could facilitate their operations, compensating these difficulties. To this end, we propose a dataset for gesture-based UGV control by FRs, introducing a set of 12 commands, drawing inspiration from existing gestures used by FRs and tactical hand signals and refined after incorporating feedback from experienced FRs. Then we proceed with the data collection itself, resulting in 3312 RGBD pairs captured from 2 viewpoints and 7 distances. To the best of our knowledge, this is the first dataset especially intended for gesture-based UGV guidance by FRs. Finally we define evaluation protocols for our RGBD dataset, termed FR-GESTURE, and we perform baseline experiments, which are put forward for improvement. We have made data publicly available to promote future research on the domain: https://doi.org/10.5281/zenodo.18131333.

翻译：灾害强度和频次的持续攀升使得应急响应人员的工作愈发艰巨。人工智能与机器人技术有望缓解其操作压力，弥补现有困难。为此，我们提出一个用于应急响应人员通过手势控制无人地面车辆的数据集，设计了一套包含12种指令的手势体系——该体系借鉴现有应急响应手势与战术手语，并整合资深应急响应人员的反馈意见进行优化。通过数据采集工作，我们最终获得从2个视角、7种距离采集的3312组RGBD配对数据。据我们所知，这是首个专门针对应急响应人员手势引导无人地面车辆任务构建的数据集。我们进一步为这个命名为FR-GESTURE的RGBD数据集制定了评估标准，并开展基线实验以供后续改进。所有数据已公开以推动该领域研究：https://doi.org/10.5281/zenodo.18131333。