基于视觉的去中心化自主空中野生动物监测
Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring
AI总结：
针对现有野生动物监测机器人方案的不足，提出基于视觉的去中心化多旋翼无人机系统，通过新型协调与追踪算法实现野外环境下的稳健监测，经实验验证可可靠部署。
摘要：
野生动物野外作业需要高效的并行部署方法，以识别并与特定个体交互，实现同步的集体行为分析及健康安全干预。现有机器人解决方案要么从群体视角处理该问题，要么为手动操作且规模受限。我们提出一种基于视觉的多旋翼无人机去中心化野生动物监测系统，具备可扩展、低带宽、传感器极简（仅搭载单台机载RGB相机）的特点，可在自然栖息地中稳健识别并追踪大型物种。我们开发了专为动态非结构化环境设计的新型基于视觉的协调与追踪算法，不依赖集中式通信或控制。通过真实世界实验验证了该系统，证明其可在多样野外条件下可靠部署。
英文摘要：
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.
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。
