Vision-Based Target Localization with Cooperative UAVs Towards Indoor Surveillance

Unmanned aerial vehicles (UAVs) have been widely adopted for a variety of civilian and military applications. Despite their many advantages, most UAVs are not suitable for indoor missions due to the lack of global position system (GPS) and the large size of UAV with diverse sensors. In this work, a lightweight and cost-effective indoor multi-UAV surveillance system is presented for accurate target localization. The proposed system employs a vision-based architecture, leveraging ORB-SLAM for self-localization and YOLOv3 for object detection. The triangulation method is employed for target positioning, offering reliable performance with negligible time delays and improved detection compared to depth camera approaches. After that, the Riccati observer is introduced to address the stochastic nature of the system. Through a series of experiments involving real UAVs, the system demonstrates its ability to accurately localize and track targets in indoor environments, updating the UAVs' positions in real-time with optimal performance.