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.

翻译：在GPS信号不可用的环境中导航无人机是一个引人注目且复杂的挑战。当处理纳米飞行器（NAV）时，由于其紧凑的尺寸、有效载荷限制和计算能力，这一挑战进一步加剧。本文提出了一种利用外部计算资源、外部单目相机以及改进的开源算法进行定位的方法。所提出的方法在外部计算机上使用三个并行比例-积分-微分控制器，通过无线通信提供速度校正，从而在定制的受控环境中稳定NAV。该方法具有3.1厘米的定位误差和仅50美元的适中设置成本，被证明在成本考量至关重要的环境中是最优的。它特别适用于学术机构中教授无人机控制等应用场景，其中指定的误差范围被认为是可接受的。设计了多种应用来验证所提出的技术，例如使NAV降落在移动的地面车辆上、在三维空间中进行路径规划以及对多NAV进行定位。所创建的软件包已在https://github.com/simmubhangu/eyantra_drone 公开提供，以促进该领域的研究。