Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring

2025-08-20

AI Summary

To address the challenges of scaling up and autonomizing individual identification, collaborative tracking, and health intervention in wildlife field monitoring, this paper proposes a decentralized multi-quadcopter visual monitoring system. The system relies solely on onboard RGB monocular cameras—requiring no centralized communication infrastructure or high-bandwidth connectivity—and employs a novel distributed vision-based coordination algorithm to achieve robust target detection, cross-platform identity association, and multi-agent formation tracking in dynamic, unstructured environments. Our approach integrates a lightweight deep learning detection model, distributed cooperative navigation, and visual SLAM-enhanced pose estimation. Extensive field validation demonstrates continuous identification and tracking of large wildlife (e.g., horses, deer) in natural habitats. The system supports concurrent operation of数十 quadcopters, significantly improving monitoring scalability, autonomy, and environmental adaptability.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Coordination and Collaboration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

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.

Problem

• Decentralized multi-quadrotor system for wildlife monitoring
• Robust identification and tracking of large species
• Vision-based coordination in dynamic unstructured environments

Innovation

• Decentralized vision-based multi-quadrotor wildlife monitoring system
• Novel vision-based coordination algorithms for dynamic environments
• Single onboard RGB camera enables scalable low-bandwidth operation