受鱼群启发，用密度估计实现无人机群高效相对定位。
Bio-inspired visual relative localization for large swarms of UAVs
- 通过回归邻居密度而非逐个识别，提升定位精度与可扩展性。
- 在不同目标姿态下仍保持稳定，适合大规模无人机群控制。
- 适用于需要高鲁棒性的群体协同场景，如智能巡检、编队飞行。
本文提出一种新型视觉感知方法，用于大规模无人机群的相对定位。受沙丁鱼群、蜂群等动物群体协作行为启发，该方法不依赖于单个智能体逐一检测邻近个体并估算位置，而是通过回归距离上的邻居密度来实现距离估计。该方式显著提升了定位精度，并在邻居数量增加时仍保持良好可扩展性。同时，提出一种新的群体控制算法，使其与新定位方法兼容。实验表明，该密度回归方法对目标相对姿态变化更具鲁棒性，适合作为无人机群稳定性的主要相对定位来源。
原文摘要 · Abstract (English)
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.
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