A Multi-Camera Coordinated Localization Approach for Robust State Estimation of Flying Robots

Abstract

Ground-air-based visual localization for UAVs, which typically relies on a single camera to track artificial markers, is often hampered by a limited field of view and sensitivity to illumination. Although active camera scheduling can mitigate these issues to some extent, it often incurs high motion costs and intermittent tracking losses during large-scale or agile UAV maneuvers. This letter presents a coordinated ground-air localization approach based on multi-camera scheduling to achieve continuous and full-coverage tracking. We equip UAVs with active infrared markers arranged in a distinctive structure, enabling robust 6D relative state estimation through a coarse-to-fine feature extraction and geometric matching. Building on this, we propose a predictive sparse scheduling framework that jointly optimizes camera activation and control over a receding horizon, explicitly balancing observation quality with actuation effort. Experimental results show our method achieves high-precision, robust localization under strong illumination interference. Compared to a fixed-camera baseline, the approach reduces the target loss rate by 29.74%, and decreases camera rotations by 70.32% compared to a basic active-vision method, demonstrating both efficient and reliable performance.