ViVa-SAFELAND：一款用于飞行器视觉导航安全验证的新型免费软件
ViVa-SAFELAND: a New Freeware for Safe Validation of Vision-based Navigation in Aerial Vehicles
AI总结：
本研究提出开源软件库ViVa-SAFELAND，通过高清城市场景视频与模拟飞行器构建安全验证基准，可公平对比视觉导航方案、助力算法开发与训练，经两个案例验证有效，填补了相关领域空白。
摘要：
ViVa-SAFELAND是一个开源软件库，旨在测试和评估飞行器的视觉导航策略，重点关注自主着陆场景，同时符合法规要求并保障人员安全。该软件库包含一组高清航拍视频，聚焦真实非结构化城市场景，记录了汽车、行人等感兴趣的移动障碍物。随后，研究人员实现了搭载虚拟移动相机的模拟飞行器（Emulated Aerial Vehicle, EAV），可根据高阶指令在视频内部“导航”。ViVa-SAFELAND提供了一种全新、安全、简便且公平的对比基准，可在相同条件下评估和对比不同的视觉导航方案，还能在多次试验中对变量进行随机化设置。它还能为自主着陆和导航策略的开发提供便利，也可用于生成适配不同训练任务的图像数据集。此外，该工具还可用于训练人类飞行员或基于深度学习的自主飞行员。研究通过两个案例验证了该框架在验证视觉算法方面的有效性，分别是移动物体检测和风险评估分割。据我们所知，这是首个用于测试和对比飞行器视觉导航方案的同类安全验证框架，对于飞行器在复杂真实城市场景中的部署至关重要。
英文摘要：
ViVa-SAFELAND is an open source software library, aimed to test and evaluate vision-based navigation strategies for aerial vehicles, with special interest in autonomous landing, while complying with legal regulations and people's safety. It consists of a collection of high definition aerial videos, focusing on real unstructured urban scenarios, recording moving obstacles of interest, such as cars and people. Then, an Emulated Aerial Vehicle (EAV) with a virtual moving camera is implemented in order to ``navigate" inside the video, according to high-order commands. ViVa-SAFELAND provides a new, safe, simple and fair comparison baseline to evaluate and compare different visual navigation solutions under the same conditions, and to randomize variables along several trials. It also facilitates the development of autonomous landing and navigation strategies, as well as the generation of image datasets for different training tasks. Moreover, it is useful for training either human of autonomous pilots using deep learning. The effectiveness of the framework for validating vision algorithms is demonstrated through two case studies, detection of moving objects and risk assessment segmentation. To our knowledge, this is the first safe validation framework of its kind, to test and compare visual navigation solution for aerial vehicles, which is a crucial aspect for urban deployment in complex real scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
