多无人机协同追踪野生动物，无需中心控制
Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring
- 基于单个摄像头的分布式视觉算法，无需中心通信
- 在真实野外环境中实现对大型动物的稳定识别与追踪
- 适合大规模野外生态监测，低带宽、轻量级部署
野生动物野外作业需要高效的并行部署方法，以识别特定个体并开展同步的行为分析与健康干预。以往机器人方案多从群体视角出发，或依赖人工操作且规模受限。本文提出一种去中心化的视觉多旋翼系统，用于野生动物监测，具备可扩展性、低带宽和传感器最小化（仅需机载单个RGB相机）的特点。该方法能够在自然栖息地中实现对大型物种的鲁棒识别与追踪。我们开发了专为动态、非结构化环境设计的新型视觉协同与追踪算法，不依赖中央通信或控制。通过真实世界实验验证，系统在多种野外条件下均表现出可靠的部署能力。
原文摘要 · Abstract (English)
Wildlife field operations demand efficient parallel deployment methods to identify and interact with specific individuals, enabling simultaneous collective behavioral analysis, and health and safety interventions. Previous robotics solutions approach the problem from the herd perspective, or are manually operated and limited in scale. We propose a decentralized vision-based multi-quadrotor system for wildlife monitoring that is scalable, low-bandwidth, and sensor-minimal (single onboard RGB camera). Our approach enables robust identification and tracking of large species in their natural habitat. We develop novel vision-based coordination and tracking algorithms designed for dynamic, unstructured environments without reliance on centralized communication or control. We validate our system through real-world experiments, demonstrating reliable deployment in diverse field conditions.
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