基于视觉的受控环境中纳级无人机室内定位及其应用
Vision-based indoor localization of nano drones in controlled environment with its applications
AI总结：
本文提出利用外置计算设备、单目相机和改进算法实现纳级无人机定位，实现3.1cm定位误差和低成本方案，适用于教学等场景。
摘要：
在GPS信号不可用的环境中导航无人机是一项具有挑战性的任务，尤其是对于体积小、载荷受限的纳级无人机。本文提出一种利用外置计算设备、单目相机和改进的开源算法进行定位的方法。该方法使用三个并行的PID控制器通过无线通信提供速度校正，以在受控环境中稳定无人机。该方法具有3.1厘米的定位误差和50美元的低成本设置，适用于成本敏感的环境。特别适用于学术机构的无人机教学应用，其中指定的误差范围被认为是可接受的。各种应用被设计来验证所提出的方法，如在移动地面车辆上着陆、三维空间中的路径规划以及多纳级无人机的定位。创建的软件包在https://github.com/simmubhangu/eyantra_drone上公开，以促进该领域的研究。
英文摘要：
Navigating unmanned aerial vehicles in environments where GPS signals are unavailable poses a compelling and intricate challenge. This challenge is further heightened when dealing with Nano Aerial Vehicles (NAVs) due to their compact size, payload restrictions, and computational capabilities. This paper proposes an approach for localization using off-board computing, an off-board monocular camera, and modified open-source algorithms. The proposed method uses three parallel proportional-integral-derivative controllers on the off-board computer to provide velocity corrections via wireless communication, stabilizing the NAV in a custom-controlled environment. Featuring a 3.1cm localization error and a modest setup cost of 50 USD, this approach proves optimal for environments where cost considerations are paramount. It is especially well-suited for applications like teaching drone control in academic institutions, where the specified error margin is deemed acceptable. Various applications are designed to validate the proposed technique, such as landing the NAV on a moving ground vehicle, path planning in a 3D space, and localizing multi-NAVs. The created package is openly available at https://github.com/simmubhangu/eyantra_drone to foster research in this field.
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。
