Implementation of Monocular Visual SLAM with ARCog-NET for Aerial Robot Swarm Indoor Mapping

This paper presents a real-time distributed framework for Unmanned Aerial Vehicle (UAV) swarms operating in GPS-deniedindoor environments, built on the Aerial Robot Cognitive Network Architecture (ARCog-NET) cognitive architecture andenhanced with monocular visual Simultaneous Localization and Mapping (SLAM). ARCog-NET employs a multi-layeredEdge-Fog-Cloud (EFC) hierarchy that supports decentralized decision-making and adaptive coordination, including dynamicpath optimization. Through a novel formulation, each UAV jointly estimates its own trajectory and contributes to a shared 3Dreconstruction of the environment by exchanging matched visual landmarks across the network. The system dynamicallyadapts navigation paths in response to operational events using reinforcement learning guided by trajectory coverage metricsand historical decision weights. A full deployment with six DJI Ryze Tello UAVs was conducted in a controlled indoor labenvironment, demonstrating autonomous swarm navigation and collaborative 3D mapping. Performance was evaluated throughmetrics such as trajectory error, point cloud fidelity, decision convergence, and knowledge reuse rate. Results confirm that theproposed method enables scalable, autonomous SLAM and planning capabilities in real-world UAV networks, highlighting thecognitive synergy between navigation and perception in distributed aerial robotics.