Vision-Based Unmanned Aerial Vehicle Swarm Cooperation and Online Point-Cloud Registration for Global Localization in Global Navigation Satellite System-Intermittent Environments

Reliable autonomy for drones operating in GNSS-intermittent or denied environments requires both stable inter-vehicle coordination and a shared global understanding of the environment. This paper presents a unified vision-based framework in which UAVs use biologically inspired swarm behaviors together with online monocular point-cloud registration to achieve real-time global localization. First, we apply a passive-perception strategy, bird-inspired drone swarm-keeping, enabling each UAV to estimate the relative motion and proximity of its neighbors using only monocular visual cues. This decentralized mechanism provides cohesive and collision-free group motion without GNSS, active ranging, or explicit communication. Second, we integrate this capability with a cooperative mapping pipeline in which one or more drones acting as global anchors generate a globally referenced monocular SLAM map. Vehicles lacking global positioning progressively align their locally generated point clouds to this shared global reference using an iterative registration strategy, allowing them to infer consistent global poses online. Other autonomous vehicles optionally contribute complementary viewpoints, but UAVs remain the core autonomous agents driving both mapping and coordination due to their privileged visual perspective. Experimental validation in simulation and indoor testbeds with drones demonstrates that the integrated system maintains swarm cohesion, improves spatial alignment by more than a factor of four over baseline monocular SLAM, and preserves reliable global localization throughout extended GNSS outages. The results highlight a scalable, lightweight, and vision-based approach to resilient UAV autonomy in tunnels, industrial environments, and other GNSS-challenged settings.