Collaborative Infrastructure-Free Aerial–Ground Robotic System for Warehouse Inventory Data Capture

Efficient and reliable inventory management remains a challenge in modern warehouses, where manual counting is time-consuming, error-prone, and costly. We present an autonomous aerial–ground system for warehouse inventory data capture that operates without external infrastructure or prior mapping operations. A differential-drive unmanned ground vehicle (UGV) performs global localization and navigation from a simple 2D floor plan via 2D LiDAR scan-to-map matching fused in an Extended Kalman Filter. An unmanned aerial vehicle (UAV) uses fiducial-based relative localization to execute short, autonomous take-off, follow, precision landing, and close-range imaging of high shelves. By ferrying the UAV between aisles, the UGV extends the UAV’s effective endurance and coverage, limiting flight to brief, high-value segments. We validate the system in simulation and real environments. In simulation, the proposed localization method achieves higher accuracy and consistency than AMCL, GMapping, and KartoSLAM across varied layouts. In experiments, the UAV reliably follows and lands on the UGV, producing geo-referenced imagery of high shelves suitable for downstream inventory recognition.