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

Alejandro Lorite Mora; Dimitrios Arapis; Andrés Faíña

cs.ROOct 7, 2026

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

Authors: Alejandro Lorite Mora, Dimitrios Arapis, Andrés Faíña

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

Abstract

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.

Figures & tables

Fig. 1: The quadruped localizes the nano-UAV with no marker on the drone and no localization sensing aboard it. It computes the drone’s position with its arm camera, in its own map, and sends it over radio. Overlays are projected into the footage from the trial’s own recording: the yellow cross is the markerless estimate the drone flew on, the blue circle is the motion-capture ground truth used for evaluation only. Insets: the quadruped’s camera view and a $3\times$ magnification of the drone.

Panel of Figure 2 — Fig. 2: The deployed system. Solid path: one position per frame, expressed in the quadruped’s own map frame. Shaded stages are the three integrity gates of the deployed gripper-camera path. A failed gate publishes nothing and the drone’s filter coasts on its IMU, a dropout instead of a wrong position. Section IV-A reports their aggregate effect. Dashed: the accepted position also drives the perception-aware controller (Section III-E ), which commands the arm and body so the drone stays observable.

Fig. 3: Live accuracy of the deployed estimator (gripper camera) against motion capture over

17\,363

positions from eight closed-loop flights. Each point is the median 3D error of the positions recorded within a

0.2

m interval of distance, the band around the curve their interquartile range, and intervals holding fewer than

80

positions are not drawn. The gray span marks the distance range all three conditions cover, and the inset box gives each condition’s median and interquartile range over that shared range.

Fig. 4: Localization accuracy on the earlier stereo camera, comparing the fiducial baseline against three markerless configurations. Each point is the median 3D error of the positions recorded within a distance interval, and the band around a curve their interquartile range. The bare raw-depth series is medians only. Turning the gates on removes the bare airframe from the plot entirely, safely converting fatal false depths into dropouts (

4

0

wrong publications over

5\,539

held-out frames).

Controller	$n$	Off-axis	Range	Body	Hand
		med / P95 ( ∘ )	(m)	(m 2 )	(m 2 )
Reactive	6	10.7 / 13.7	0.86	1.49	1.52
NMPC, low-motion	3	0.70 / 7.5	1.25	0.42	0.79
NMPC, deployed	3	0.54 / 2.7	1.25	1.72	1.61

TABLE I: Perception-aware control on the quadruped.

Fig. 5: The closed-loop flights of Section IV-C . (a) One flight from above, with the commanded trajectory (dashed, waypoints W1 to W10) drawn over the translucent traces: the drone by motion capture (blue), the hand-camera estimate (orange), the quadruped’s body heading (red), and the hand carrying the camera (purple). The loop begins and ends over the landing pad (W0 & W10). (b) Error against progress along the trajectory, defined as the instantaneous distance from the commanded position to the drone’s true motion-capture position (mean

\pm

one standard deviation). Shaded vertical bands indicate the

1.0

s hold at each waypoint. Gray traces represent the baseline control error from nine flights flown entirely on motion capture. Orange traces represent markerless flights, grouped by their commanded heights (top strip), which change dynamically at each waypoint.

Drone height	$n$	Config.	Range	Look-up	Avail.	3D error (mm)		Reached
(m)			(m)	med ( ∘ )	(%)	med	P95
0.6 to 0.95	3	S	1.2	2 to 4	99.3 to 99.8	58 to 60	121 to 124	10/10
0.95 to 1.65	2	C	1.2	31 to 33	98.5 to 99.6	50 to 59	131 to 143	10/10
2.0	1	C	1.3	58	63.4	84	146	9/10
2.0	2	L	1.8	44 to 45	77.7 to 84.5	87 to 89	150 to 153	10/10
2.5	2	C	-	-	-	-	-	0/10

TABLE II: Flown envelope on a clear scene.

Fig. 6: Where a blind drone’s drift goes. Each trace is one of

25

human occlusions, separated by whether the drone was hovering (top, solid medians) or flying the trajectory (bottom, dashed medians). Drift is resolved on the camera’s axes at the instant of loss: motion along the optical axis affects range, cross-frame motion affects tracking angle. The along-axis median reaches

0.22

m while cross-frame drift stays under

0.08

m. Medians terminate when fewer than five occlusions remain active.

Fig. 7: Two flights behind a step ladder, against time from the first waypoint command, with top arrows indicating the direction of travel for each leg. Error is measured against motion capture (blue) and the drone’s onboard filter (green), and the gap between them is the unobservable drift while blind. Shaded regions mark when the camera lost the drone: dark where a ladder rail crossed the line of sight, light where the line stayed clear and the detector instead failed against background clutter. Red marks the safety hand-back to the reference feed.

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Oct 5, 2026, cs.ROJ/K move · Enter open · S save

Towards Quadruped-Provided Localization and Active Tracking for Micro-UAVs

Alejandro Lorite Mora, Andrés Faíña

IT University of Copenhagen, Rued Langgaards Vej 7, Copenhagen 2300, Denmark · Helix Lab, Campus Kalundborg 3, 4400 Kalundborg, Denmark · Novo Nordisk A/S, Hallas Alle 1, 4400 Kalundborg, Denmark

UAV Navigation Cooperative Localization Robot Localization

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Micro unmanned aerial vehicles (micro-UAVs) are small enough to reach confined spaces that larger robots cannot access, but too small to carry the sensing and computing power required for autonomous flight. We move the localization stack entirely off the aerial platform onto a quadruped robot with a 7-degree-of-freedom (DOF) arm, which supplies the micro-UAV (27 g bare, 42 g with fiducial markers) its full 6-DOF pose. A camera at the arm's end-effector detects AprilTag fiducial markers on the drone and composes that observation with the quadruped's own self-localization to place the drone in a shared map frame, so the ground robot localizes its partner, rather than only tracking it relative to the camera. The arm acts as an actively-controlled observer, repositioning to keep the drone in view as both robots move; the drone carries only an inertial measurement unit and fuses the external pose to fly commanded setpoints. In lab flights the external pose is accurate to 12-16 mm, enough to fly the drone autonomously within 2-5 cm of motion-capture-fed control. Having the quadruped actively follow the drone reduces the tracking error from 11.0 cm to 6.9 cm by holding the camera in the close range, where the markers are most accurate.

Abstract

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