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

Valerio Brunacci; Davide Plozza; Alessio De Angelis; Michele Magno; Tommaso Polonelli

cs.ROMar 9, 2026

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

Authors: Valerio Brunacci, Davide Plozza, Alessio De Angelis, Michele Magno, Tommaso Polonelli

Organizations: Department of Engineering, University of Perugia, Perugia, 06125, Italy · Multi-Robot Systems Group, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 16627, Czech Republic · Department of Information Technology and Electrical Engineering (D-ITET), ETH Zurich, Zurich, 8092, Switzerland

Abstract

Persistent air ground robot teams require nano UAVs that can leave a mobile UGV, perform short scouting or inspection tasks, and reliably return to a compact landing interface. This final docking phase remains difficult: it demands centimeter-scale relative localization on a moving platform, while nano UAVs provide only a few grams of payload and severe onboard sensing, power, and computation constraints. We address this problem with an infrastructure-less magneto inductive anchor-tag localization system for heterogeneous UAV-UGV teaming. Unlike passive magnetic docking aids or approaches exploiting environmental magnetic features, the proposed system actively generates a frequency multiplexed AC magnetic field on the UGV and uses it as an onboard localization reference for closed loop flight. The nano UAV carries only a lightweight passive receive coil and estimates its 3D position directly in the UGV frame, providing a local close range reference for hovering, tracking, and landing without external anchors, visual fiducials, GNSS, or motion capture online. The magnetic estimate is fused with the native onboard sensing stack, enabling operation under nano UAV SWaP constraints. Experiments with a Unitree A1 quadruped and a Crazyflie nano UAV, evaluated against motion capture ground truth, show centimeter level accuracy in static hovering and landing and approximately 8-11 cm RMSE during UGV motion, while the flow only baseline frequently violates the safety bound and fails the task.

Figures & tables

Figure 1: Representative picture of a nano-size UAV autonomously landing on a moving legged robot and GNSS-denied environment. The infrastructure-free localization systems relies on onboard sensors, combining IMUs, optical-flow camera, UWB, and the MI system. The four reference coils are represented by the red lines with a magnetic dipole shape. The landing deck can be used to recharge the UAV’s batteries.

Paper	Localization Method	Infrastructure-less	Moving Landing	Landing metric	UAV-UGV Teaming	Field Test	nano UAV
Liu et al. [2025]	UWB+IMU+Vision	✗	✓	-	✗	Outdoor	✗
De Silva and Katupitiya [2024]	UWB ranging (2 UAVs)	✓	✗	$14\text{\,}\mathrm{cm}$	✓	Outdoor	✗
Polonelli et al. [2022a]	GNSS + UWB	✗	✗	$\pm$ $10\text{\,}\mathrm{cm}$	✗ (static sensors)	Outdoor	✗
Nguyen et al. [2024]	Vision + motion	✓	✓	$\pm$ 5 °	✗	Simulation	✗
Ladosz et al. [2024]	Vision	✓	✓	80% SC	✗	Sim. + Indoor	✓
Zheng et al. [2025]	SLAM (shared map.) + manual	✗	✗	-	✓	Outdoor	✗

Table 1: Comparison of UAV-UGV landing and cooperative systems in recent literature.

Panel of Figure 2 — Figure 2: Custom Magnetic Hardware. (a) The Unitree A1 legged robot UGV. (b) One of the four lightweight MI coils used for precise localization. (c) The Crazyflie 2.1 nano UAV. (d) The landing pad with a diameter of $22\text{\,}\mathrm{cm}$ mounted on UGV. (e) The wiring for the AnchorDeck and the four coils, plus the connections for the wireless battery charger. (f) The ultra-lightweight MI coil mounted on the Crazyflie 2.1 nano UAV and the MagneticDeck. (g) Motion caption system markers used to assess the system performances.

Parameter	Value
Platform
UGV / UAV	Unitree A1 / Crazyflie 2.1
UAV mass / MI payload	47 g / 9 g
Landing pad / Target	25 $\times$ 44 cm / $\varnothing$ 22 cm
Coils
TX anchors / RX tag	4 / 1 passive coil

Table 2: Main parameters of the MI anchor-tag localization system.

Panel of Figure 4 — Figure 4: Visual overview of the experimental validation scenarios using the proposed magnetic localization system. (a)-(c) S1 (Static Hovering & Landing): The nano UAV performs an autonomous sequence of takeoff, hovering, and precision landing on the stationary UGV. (d)-(f) S2 (Linear Tracking & Landing): The nano UAV tracks the UGV moving along a linear trajectory. Visualization includes (d) 3D perspective, (e) lateral view showing the relative distance maintenance ( $T_{0}$ to $T_{1}$ ), and (f) top-down view of the alignment. (g)-(h) S3 (Composite Motion Tracking): The UAV tracks and follows the UGV performing complex planar maneuvers with varying velocity and direction, maintaining the relative position within the magnetic workspace.

Test	Hovering		in-out
Test	Mag+Flow	Flow	Mag+Flow	Flow
1	1.14	4.56	6.93	FAIL
2	0.80	4.55	6.21	FAIL
3	1.79	2.56	FAIL	FAIL
4	1.76	5.36	6.94	FAIL
5	4.01	5.46	6.07	FAIL

Table 3: Scenario S1: The UGV is static. For Hovering, the UAV performs the sequence in Figure 4(a) , Figure 4(b) , Figure 4(c) . For the test in-out , the sequence includes also a movement (

0.6\text{\,}\mathrm{m}

) outside the landing pad. In summary, the sequence is: takeoff, fly

0.6\text{\,}\mathrm{m}

ahead, fly back, landing. Values represent RMSE in centimeter, success rate (SC) in percentage.

Figure 5: Touchdown accuracy analysis for scenario S1 -Hovering. The plot shows the planar 2D position of the UAV relative to the docking pad center at the moment of landing for the proposed Mag+Flow method (circles).

Figure 6: Scenario S2 : time-history qualitative results for a representative linear docking sequence. The colored bands highlight the mission phases (pre-takeoff, takeoff/approach, hovering, return over the UGV, landing). Top: $x$ position in the world frame. Middle: altitude $z$ . Bottom: magnitude of the FFT peaks of the coil voltage, which provides a proxy of the received magnetic signal strength over time.

	1	2	3	4	5	6	7	8	9	10	Mean	SC
S2	6.04	14.7	7.11	6.09	10.7	5.56	13.5	FAIL	6.51	FAIL	8.77	80%
S3	9.39	8.14	27.1	7.14	11.3	7.09	7.34	8.52	9.53	9.46	10.5	100%

Table 4: Linear tracking and composite motion results. Performance comparison during UGV planar forward-backward motion ( S2 ) and during complex UGV trajectories ( S3 ). RMSE calculated on the whole path. Values represent RMSE in centimeter, success rate (SC) in percentage.

Figure 7: Scenario S3 (Test 1): qualitative time-aligned analysis of dynamic tracking. The numbered markers (1–7) identify key flight phases via synchronized video snapshots and the corresponding dashed vertical lines in the time traces. The trajectory plots (3D and top-down) compare Ref. (World frame) , UGV (World frame) , and EKF (World frame) . Markers 1–2 (early takeoff/settling) highlight small transient oscillations.

Panel of Figure 8 — Figure 8: Model-based sensitivity of the magnetic localization pipeline to uncompensated UGV attitude. The comparison shows that uncompensated UGV yaw, roll, and pitch are mapped by the current position-only estimator into systematic position biases.

Motion	Angle	Final projected error	Final 3D error	Mean 3D error
	[deg]	[cm]	[cm]	[cm]
Yaw	0	2.28	3.45	4.51
Yaw	10	14.05	14.15	14.76
Yaw	40	66.33	66.38	67.38
Roll	0	3.39	3.45	4.51
Roll	5	10.74	10.81	10.76

Table 5: model based sensitivity of the position only magnetic estimator to uncompensated UGV attitude. Errors are computed in the world frame. The projected error is reported in the dominant affected plane:

xy

for yaw,

yz

for roll, and

xz

for pitch.

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Borrowed Eyes: Markerless Nano-UAV Flight with an Active Quadruped Observer

Alejandro Lorite Mora, Dimitrios Arapis, Andrés Faíña

IT University of Copenhagen, Denmark · Novo Nordisk A/S, Hillerød, Denmark

UAV Navigation GNSS-Denied Localization Cooperative Localization Active Perception

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Nano unmanned aerial vehicles (nano-UAVs) can navigate confined spaces that larger robots cannot, but their payload capacity severely limits the sensors and compute available for self-localization in global navigation satellite system (GNSS)-denied environments. We present a vision-based system that localizes a nano-UAV from a quadruped robot with an arm-mounted camera. The quadruped tracks the drone, estimates its position in its own coordinate system using segmentation masks and depth, and transmits that position over a real-time radio link. To ensure continuous tracking, we developed a perception-aware nonlinear Model Predictive Controller (NMPC) that dynamically adjusts the quadruped's body and arm to maximize the drone's visibility, treating observation reliability as a primary control objective. Relying solely on this external estimate, the nano-UAV executed predefined trajectories from takeoff to landing with a median 3D localization error of 59 mm. The system demonstrates robustness against short visual occlusions by smoothly transitioning to onboard inertial flight when line-of-sight is temporarily lost. Ultimately, this framework allows a quadruped to offload the localization burden of a nano-UAV, enabling inspection of complex spaces that neither robot could navigate alone.

Abstract

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