We propose a new approach to visual perception for relative localization of agents within large-scale swarms of UAVs. Inspired by biological perception utilized by schools of sardines, swarms of bees, and other large groups of animals capable of moving in a decentralized yet coherent manner, our method does not rely on detecting individual neighbors by each agent and estimating their relative position, but rather we propose to regress a neighbor density over distance. This allows for a more accurate distance estimation as well as better scalability with respect to the number of neighbors. Additionally, a novel swarm control algorithm is proposed to make it compatible with the new relative localization method. We provide a thorough evaluation of the presented methods and demonstrate that the regressing approach to distance estimation is more robust to varying relative pose of the targets and that it is suitable to be used as the main source of relative localization for swarm stabilization.

翻译：我们提出了一种用于大规模无人机集群内部智能体相对定位的视觉感知新方法。该方法受到沙丁鱼群、蜂群以及其他能够以去中心化但协调一致方式运动的大型动物群体所利用的生物感知机制启发，不依赖于每个智能体检测单个邻居并估计其相对位置，而是提出回归一个随距离变化的邻居密度函数。这种方法能够实现更精确的距离估计，并在邻居数量方面具有更好的可扩展性。此外，我们提出了一种新颖的集群控制算法，使其与新的相对定位方法兼容。我们对所提出的方法进行了全面评估，结果表明这种回归式距离估计方法对目标相对位姿的变化具有更强的鲁棒性，适合作为维持集群稳定的主要相对定位信息来源。