Fly, Track, Land: Infrastructure-less Magnetic Localization for Heterogeneous UAV-UGV Teaming

2026-03-09

AI Summary

This work addresses the challenge of high-precision landing of lightweight drones onto moving ground robots in infrastructure-free environments. The authors propose a magnetic induction–based relative localization system that integrates inertial and optical flow data with a single receiver coil, a real-time 3D position estimation algorithm, and a warm-start solver to achieve centimeter-level dynamic docking between a drone and a mobile quadruped robot—without relying on external anchors or global positioning. To the best of the authors’ knowledge, this is the first demonstration of compact embedded magnetic induction localization applied to autonomous landing in heterogeneous robotic systems. Experimental results show a 3D position root-mean-square error (RMSE) of 5 cm and a dynamic docking RMSE of 7.2 cm, successfully enabling fully autonomous landing.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Motion & Tracking
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We present a complete infrastructure-less magneto-inductive (MI) localization system enabling a lightweight UAV to autonomously hover, track, and land with centimeter precision on a mobile quadruped robot acting as a dynamic docking pad. This work advances the vision of heterogeneous robot collaboration, where ultra-lightweight flying robots serve as mobile perception agents for ground-based Unmanned Ground Vehicles (UGVs). By extending the sensing horizon and providing complementary viewpoints, the UAVs enhance exploration efficiency and improve the quality of data collection in large-scale, unknown environments. The proposed system aims to complements traditional localization modalities with a compact, embedded, and infrastructure-less magnetic sensing approach, providing accurate short-range relative positioning to bridge the gap between coarse navigation and precise UAV docking. A single lightweight receive coil and a fully embedded estimation pipeline on the UAV deliver 20 Hz relative pose estimates in the UGV's frame, achieving a 3D position root-mean-square error (RMSE) of 5 cm. The system uses real-time estimation and a warm-started solver to estimate the 3D position, which is then fused with inertial and optical-flow measurements in the onboard extended Kalman filter. Real-world experiments validate the effectiveness of the framework, demonstrating significant improvements in UAV--UGV teaming in infrastructure-less scenarios compared to state-of-the-art methods, requiring no external anchors or global positioning. In dynamic scenarios, the UAV tracks and docks with a moving UGV while maintaining a 7.2 cm RMSE and achieving successful autonomous landings.

Problem

• infrastructure-less localization
• UAV-UGV teaming
• magnetic localization
• autonomous landing
• relative positioning

Innovation

• magneto-inductive localization
• infrastructure-less navigation
• UAV-UGV teaming
• autonomous landing
• embedded pose estimation