用于室内搜索和救援任务的小型商用无人机

H. Surmann, Tiffany Kaiser, Artur Leinweber, Gerhard Senkowski, Dominik Slomma, Marchell E. Thurow
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引用次数: 2

摘要

本技术报告是关于室内搜索和救援任务中非常小的商用无人机(< 40厘米对角线)的架构和集成。一架无人机仅由一名人工操作员手动控制,为以后的3D场景建模和检查提供实时视频流和图像系列。为了帮助必须同时观察环境并在其中导航的操作员,我们使用多个深度神经网络来提供引导自主性,自动对象检测和分类以及局部3D场景建模。我们的方法有助于减少操作员的认知负荷。我们描述了一个框架,用于快速集成来自深度学习领域的新方法,从而能够在真实场景中进行快速评估,包括方法的交互。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Small Commercial UAVs for Indoor Search and Rescue Missions
This technical report is about the architecture and integration of very small commercial UAVs (< 40 cm diagonal) in indoor Search and Rescue missions. One UAV is manually controlled by only one single human operator delivering live video streams and image series for later 3D scene modelling and inspection. In order to assist the operator who has to simultaneously observe the environment and navigate through it we use multiple deep neural networks to provide guided autonomy, automatic object detection and classification and local 3D scene modelling. Our methods help to reduce the cognitive load of the operator. We describe a framework for quick integration of new methods from the field of Deep Learning, enabling for rapid evaluation in real scenarios, including the interaction of methods.
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