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arxiv: 2511.17166 · v2 · pith:L35QPAJQnew · submitted 2025-11-21 · 💻 cs.RO

Reflection-Based Relative Localization for Cooperative UAV Teams Using Active Markers

Tim Lakemann , Daniel Bonilla Licea , Viktor Walter , Martin Saska This is my paper

Pith reviewed 2026-05-21 18:20 UTC · model grok-4.3

classification 💻 cs.RO
keywords UAV relative localizationactive markersreflectionsmulti-robot teamsvisual localizationsurface independencemarine UAVs
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The pith

Reflections of active markers on UAVs enable relative localization over 30 meters without prior configurations or surface-specific knowledge.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a method that treats reflections of active markers in the environment as useful data rather than noise for determining relative positions among UAVs in a team. It demonstrates that this works reliably in both indoor and outdoor settings, including on dynamic water surfaces, while handling uncertainties from irregular surfaces and achieving longer range and better accuracy than existing techniques. A reader would care because many cooperative drone operations occur in open or marine areas where distance, lighting changes, and unknown surfaces make standard visual localization unreliable. The approach needs no advance knowledge of robot dimensions or fixed marker patterns, allowing flexible use across different UAV types.

Core claim

The central claim is that reflections of active markers can be segmented and matched in real time to support onboard relative localization for heterogeneous multi-UAV teams, remaining independent of surface properties while explicitly modeling uncertainties from irregularities such as moving water, and delivering effective ranges above 30 meters with higher accuracy than prior methods under varying lighting.

What carries the argument

The reflection segmentation and matching process that identifies environmental bounces of active markers and uses them to compute relative positions while propagating uncertainty estimates from surface variations.

If this is right

  • UAV teams maintain coordination and formation at distances exceeding those typical for direct marker visibility.
  • Localization functions consistently on moving or irregular surfaces such as water without requiring surface-specific adjustments.
  • Heterogeneous teams with varying UAV sizes operate without needing predefined marker arrangements or size data.
  • Operation continues across changing lighting conditions without loss of the reflection-based signal.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same reflection-handling logic could support localization in other reflective settings such as indoor spaces with glossy floors.
  • Marine UAV operations might benefit from reduced dependence on additional ranging sensors when working near water.
  • Experiments with rain or fog could test whether the uncertainty model still prevents matching errors under reduced visibility.

Load-bearing premise

Reflections can be reliably segmented and matched to their originating markers in real time without false positives from other light sources or multiple bounces.

What would settle it

Deployment in a scene containing additional uncontrolled light sources or highly variable reflections where the system produces position estimates that deviate significantly from ground-truth measurements obtained by an independent sensor.

Figures

Figures reproduced from arXiv: 2511.17166 by Daniel Bonilla Licea, Martin Saska, Tim Lakemann, Viktor Walter.

Figure 1
Figure 1. Figure 1: Outdoor (left) and indoor dark (right) experiments: [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Excerpt from an outdoor experiment showing active [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Two extreme cases: Incident light (blue) undergoes [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: One UAV equipped with a camera (yellow) constructs an elliptical cone based on the diffuse reflections of light [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Indoor experiments conducted under bright conditions [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Outdoor experiment: (Left) Image captured onboard the UAV using a UV-bandpass filter. The centroid of the UAV [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: Relative position estimates obtained using our [PITH_FULL_IMAGE:figures/full_fig_p007_10.png] view at source ↗
read the original abstract

Reflections of active markers in the environment are a common source of ambiguity in onboard visual relative localization. This work presents a novel approach that exploits these typically unwanted reflections for onboard relative localization in heterogeneous multi-UAV teams. The method operates without prior knowledge of robot size or predefined marker configurations, remains independent of surface properties, and explicitly accounts for uncertainties caused by surface irregularities, including dynamic water surfaces relevant for marine deployments. We validated the approach in both indoor and outdoor experiments, demonstrating reliable operation across varying lighting conditions and achieving greater effective range (above 30 m) and accuracy than state-of-the-art methods. The video is available under the following link: https://youtu.be/y0zp8cIwkig.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents a reflection-based method for relative localization in heterogeneous multi-UAV teams that exploits rather than suppresses reflections from active markers. It claims to require no prior knowledge of robot size or marker configurations, to remain independent of surface properties, and to explicitly model uncertainties from surface irregularities including dynamic water surfaces. Indoor and outdoor experiments are reported to demonstrate reliable operation under varying lighting with effective range above 30 m and accuracy superior to state-of-the-art approaches.

Significance. If the performance claims are substantiated, the work would offer a practical advance for cooperative UAV localization in environments where reflections are unavoidable, such as marine or urban settings. The explicit treatment of surface-induced uncertainties and the lack of dependence on predefined configurations are potentially valuable contributions for real-world deployment.

major comments (2)
  1. [Experimental results] Experimental results section: The central performance claims of >30 m range, improved accuracy, and reliable operation on dynamic surfaces rest on the unquantified reliability of real-time reflection segmentation and unambiguous matching. No false-positive rates, ablation studies on extraneous light sources or multiple bounces, or quantitative trials specifically on water surfaces are provided, which directly undermines the asserted independence from surface properties and superiority over SOTA.
  2. [Method] Method description: The approach is stated to operate without prior marker configurations, yet the manuscript does not detail the concrete matching procedure or uncertainty propagation that would allow unambiguous association of reflections to originating markers under real lighting variations; this is load-bearing for the novelty claim of exploiting reflections.
minor comments (2)
  1. The video link is provided but the manuscript would benefit from additional quantitative plots (e.g., error vs. distance, segmentation success rate) to support the textual claims.
  2. [Method] Notation for uncertainty modeling from surface irregularities should be clarified with an explicit equation or diagram showing how irregularities are incorporated.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments identify areas where additional quantification and clarification would strengthen the manuscript. We address each major comment below and will revise the paper accordingly.

read point-by-point responses

  1. Referee: [Experimental results] Experimental results section: The central performance claims of >30 m range, improved accuracy, and reliable operation on dynamic surfaces rest on the unquantified reliability of real-time reflection segmentation and unambiguous matching. No false-positive rates, ablation studies on extraneous light sources or multiple bounces, or quantitative trials specifically on water surfaces are provided, which directly undermines the asserted independence from surface properties and superiority over SOTA.

    Authors: We agree that explicit quantitative support for the segmentation reliability and surface independence would better substantiate the claims. Our existing indoor and outdoor experiments demonstrate the reported range and accuracy across lighting conditions with reflective surfaces present, but we acknowledge the absence of dedicated false-positive metrics, ablations on extraneous sources or multiple bounces, and isolated quantitative water-surface trials. In the revision we will add these analyses, including false-positive rates for reflection segmentation, ablation studies, and quantitative results from controlled dynamic water-surface experiments to directly support the asserted robustness and independence from surface properties. revision: yes

  2. Referee: [Method] Method description: The approach is stated to operate without prior marker configurations, yet the manuscript does not detail the concrete matching procedure or uncertainty propagation that would allow unambiguous association of reflections to originating markers under real lighting variations; this is load-bearing for the novelty claim of exploiting reflections.

    Authors: We accept that the current method section would benefit from expanded detail to make the matching and uncertainty handling fully transparent. While the manuscript outlines the overall reflection-exploitation strategy and its independence from predefined configurations, we agree that a concrete description of the association procedure and uncertainty propagation is needed to support the novelty. In the revision we will add a dedicated subsection with the step-by-step matching algorithm, the criteria used for unambiguous reflection-to-marker association under lighting variations, and the explicit uncertainty propagation equations. revision: yes

Circularity Check

0 steps flagged

No circularity: claims rest on experimental validation of reflection exploitation

full rationale

The paper introduces a method to exploit reflections of active markers for relative localization in UAV teams, claiming independence from surface properties and superior range/accuracy validated through indoor and outdoor experiments under varying lighting. No derivation chain, equations, or first-principles results are presented that reduce by construction to fitted inputs, self-citations, or ansatzes; the central performance claims derive from direct empirical testing rather than any self-referential fitting or renaming of known results. The approach is self-contained against external benchmarks via reported experiments, with no load-bearing steps that collapse to the paper's own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The method relies on standard computer-vision assumptions about marker detection and reflection geometry plus domain assumptions about surface reflection behavior; no free parameters or invented entities are described in the abstract.

axioms (2)
  • domain assumption Reflections of active markers can be segmented and associated with their sources in camera images under varying lighting.
    Invoked by the claim of reliable operation across lighting conditions and independence from surface properties.
  • domain assumption Surface irregularities produce measurable uncertainty that can be explicitly modeled without requiring surface-specific calibration.
    Central to the robustness claim for dynamic water surfaces.

pith-pipeline@v0.9.0 · 5649 in / 1327 out tokens · 44841 ms · 2026-05-21T18:20:58.928362+00:00 · methodology

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Reference graph

Works this paper leans on

30 extracted references · 30 canonical work pages

  1. [1]

    Relative transformation estimation based on fusion of odometry and uwb ranging data,

    T. H. Nguyen and L. Xie, “Relative transformation estimation based on fusion of odometry and uwb ranging data,”IEEE Transactions on Robotics, vol. 39, no. 4, pp. 2861–2877, 2023

    work page 2023

  2. [2]

    Best Integer Equiv- ariant Estimation With Quality Control in GNSS RTK for Canyon Environments,

    Z. Zhang, H. Yuan, X. He, B. Li, and J. Geng, “Best Integer Equiv- ariant Estimation With Quality Control in GNSS RTK for Canyon Environments,”IEEE Transactions on Aerospace and Electronic Sys- tems, vol. 59, no. 4, pp. 4105–4117, 2023-08

    work page 2023

  3. [3]

    VIDO: A Robust and Consistent Monocular Visual-Inertial-Depth Odometry,

    Y . Gao, J. Yuan, J. Jiang, Q. Sun, and X. Zhang, “VIDO: A Robust and Consistent Monocular Visual-Inertial-Depth Odometry,”IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 3, pp. 2976–2992, 2023-03

    work page 2023

  4. [4]

    Distance- Based Cooperative Relative Localization for Leader-Following Control of MA Vs,

    T.-M. Nguyen, Z. Qiu, T. H. Nguyen, M. Cao, and L. Xie, “Distance- Based Cooperative Relative Localization for Leader-Following Control of MA Vs,”IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3641–3648, 2019-10

    work page 2019

  5. [5]

    An optical spatial localization system for tracking unmanned aerial vehicles using a single dynamic vision sensor,

    H. Stuckey, A. Al-Radaideh, L. Escamilla, L. Sun, L. G. Carrillo, and W. Tang, “An optical spatial localization system for tracking unmanned aerial vehicles using a single dynamic vision sensor,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 3093–3100

    work page 2021

  6. [6]

    A flexible method for perfor- mance evaluation of robot localization,

    S. Scheideman, N. Ray, and H. Zhang, “A flexible method for perfor- mance evaluation of robot localization,” in2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 8302– 8308

    work page 2020

  7. [7]

    Decentralized Visual- Inertial-UWB Fusion for Relative State Estimation of Aerial Swarm,

    H. Xu, L. Wang, Y . Zhang, K. Qiu, and S. Shen, “Decentralized Visual- Inertial-UWB Fusion for Relative State Estimation of Aerial Swarm,” in2020 IEEE International Conference on Robotics and Automation (ICRA), 2020-05, pp. 8776–8782

    work page 2020

  8. [8]

    Vision-Based Drone Flocking in Outdoor Environments,

    F. Schilling, F. Schiano, and D. Floreano, “Vision-Based Drone Flocking in Outdoor Environments,”IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2954–2961, 2021-04

    work page 2021

  9. [9]

    VI-RPE: Visual-Inertial Relative Pose Estimation for Aerial Vehicles,

    L. Teixeira, F. Maffra, M. Moos, and M. Chli, “VI-RPE: Visual-Inertial Relative Pose Estimation for Aerial Vehicles,”IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 2770–2777, 2018-10

    work page 2018

  10. [10]

    Apriltag: A robust and flexible visual fiducial system,

    E. Olson, “Apriltag: A robust and flexible visual fiducial system,” in 2011 IEEE International Conference on Robotics and Automation, 2011, pp. 3400–3407

    work page 2011

  11. [11]

    Real- Time Optical Localization and Tracking of UA V Using Ellipse De- tection,

    H. Stuckey, L. Escamilla, L. R. Garcia Carrillo, and W. Tang, “Real- Time Optical Localization and Tracking of UA V Using Ellipse De- tection,”IEEE Embedded Systems Letters, vol. 16, no. 1, pp. 1–4, 2024-03

    work page 2024

  12. [12]

    UVDAR System for Visual Relative Localization With Application to Leader–Follower Formations of Multirotor UA Vs,

    V . Walter, N. Staub, A. Franchi, and M. Saska, “UVDAR System for Visual Relative Localization With Application to Leader–Follower Formations of Multirotor UA Vs,”IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2637–2644, 2019-07

    work page 2019

  13. [13]

    A monocular pose estimation system based on infrared leds,

    M. Faessler, E. Mueggler, K. Schwabe, and D. Scaramuzza, “A monocular pose estimation system based on infrared leds,” in2014 IEEE International Conference on Robotics and Automation (ICRA), 2014, pp. 907–913

    work page 2014

  14. [14]

    Depth from stereo polarization in specular scenes for urban robotics,

    K. Berger, R. V oorhies, and L. H. Matthies, “Depth from stereo polarization in specular scenes for urban robotics,” in2017 IEEE International Conference on Robotics and Automation (ICRA), May 2017, pp. 1966–1973

    work page 2017

  15. [15]

    Multibaseline stereo in the presence of specular reflections,

    Y . Li, S. Lin, H. Lu, S. B. Kang, and H.-Y . Shum, “Multibaseline stereo in the presence of specular reflections,” in2002 International Conference on Pattern Recognition, vol. 3, 2002, pp. 573–576

    work page 2002

  16. [16]

    Polarization structured light 3d depth image sensor for scenes with reflective surfaces,

    X. Huang, C. Wu, X. Xu, B. Wang, S. Zhang, C. Shen, C. Yu, J. Wang, N. Chi, S. Yu, and C. Chang-Hasnain, “Polarization structured light 3d depth image sensor for scenes with reflective surfaces,”Nature Communications, vol. 14, 10 2023

    work page 2023

  17. [17]

    MimoLoc: Indoor Localization With Assistance of Microwave Reflection of Downlink Signal in Sub- 6G MIMO Networks,

    Z. Li, P. Wang, K. Liu, and Z. Tian, “MimoLoc: Indoor Localization With Assistance of Microwave Reflection of Downlink Signal in Sub- 6G MIMO Networks,”IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 4, pp. 2655–2668, Apr. 2024

    work page 2024

  18. [18]

    TriLoc: Toward Accurate Indoor Localization With Assistance of Microwave Reflections,

    Z. Li, P. Wang, Z. Tian, and K. Liu, “TriLoc: Toward Accurate Indoor Localization With Assistance of Microwave Reflections,”IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 6, pp. 2734–2747, Jun. 2023

    work page 2023

  19. [19]

    Multi- Person Passive WiFi Indoor Localization With Intelligent Reflecting Surface,

    G. Zhang, D. Zhang, Y . He, J. Chen, F. Zhou, and Y . Chen, “Multi- Person Passive WiFi Indoor Localization With Intelligent Reflecting Surface,”IEEE Transactions on Wireless Communications, vol. 22, no. 10, pp. 6534–6546, Oct. 2023

    work page 2023

  20. [20]

    Diffraction- and Reflection-Aware Multiple Sound Source Localization,

    I. An, Y . Kwon, and S.-e. Yoon, “Diffraction- and Reflection-Aware Multiple Sound Source Localization,”IEEE Transactions on Robotics, vol. 38, no. 3, pp. 1925–1944, Jun. 2022

    work page 1925

  21. [21]

    Reflection Assisted Sound Source Localization Through a Harmonic Domain MUSIC Framework,

    L. I. Birnie, T. D. Abhayapala, and P. N. Samarasinghe, “Reflection Assisted Sound Source Localization Through a Harmonic Domain MUSIC Framework,”IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 279–293, 2020

    work page 2020

  22. [22]

    Depth from water reflection,

    L. Yang, J. Liu, and X. Tang, “Depth from water reflection,”IEEE Transactions on Image Processing, vol. 24, no. 4, pp. 1235–1243, 2015

    work page 2015

  23. [23]

    Optical communication-based identification for multi-uav systems: theory and practice,

    D. Bonilla Licea, V . Walter, M. Ghogho, and M. Saska, “Optical communication-based identification for multi-uav systems: theory and practice,”Autonomous Robots, vol. 49, no. 3, p. 24, Sep 2025

    work page 2025

  24. [24]

    M. Born, E. Wolf, A. B. Bhatia, P. C. Clemmow, D. Gabor, A. R. Stokes, A. M. Taylor, P. A. Wayman, and W. L. Wilcock,Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light, 7th ed. Cambridge University Press, 1999

    work page 1999

  25. [25]

    Hecht,Optics, ser

    E. Hecht,Optics, ser. Pearson education. Addison-Wesley, 2002

    work page 2002

  26. [26]

    Characterization of surface roughness,

    T. Thomas, “Characterization of surface roughness,”Precision Engi- neering, vol. 3, no. 2, pp. 97–104, 1981

    work page 1981

  27. [27]

    Turbulence of capillary waves forced by steep gravity waves,

    M. Berhanu, E. Falcon, and L. Deike, “Turbulence of capillary waves forced by steep gravity waves,”Journal of Fluid Mechanics, vol. 850, p. 803–843, 2018

    work page 2018

  28. [28]

    A Flexible Technique for Accurate Omnidirectional Camera Calibration and Structure from Motion,

    D. Scaramuzza, A. Martinelli, and R. Siegwart, “A Flexible Technique for Accurate Omnidirectional Camera Calibration and Structure from Motion,” inF ourth IEEE International Conference on Computer Vision Systems (ICVS’06), 2006-01, pp. 45–45

    work page 2006

  29. [29]

    MRS Drone: A Modular Platform for Real-World Deployment of Aerial Multi-Robot Systems,

    D. Hert, T. Baca, P. Petraceket al., “MRS Drone: A Modular Platform for Real-World Deployment of Aerial Multi-Robot Systems,”Journal of Intelligent & Robotic Systems, vol. 108, no. 4, p. 64, Aug. 2023

    work page 2023

  30. [30]

    Fast Swarming of UA Vs in GNSS-Denied Feature-Poor Environments Without Explicit Communication,

    J. Horyna, V . Kr ´atk´y, V . Pritzl, T. B ´aˇca, E. Ferrante, and M. Saska, “Fast Swarming of UA Vs in GNSS-Denied Feature-Poor Environments Without Explicit Communication,”IEEE Robot. Autom. Lett., vol. 9, no. 6, pp. 5284–5291, Jun. 2024

    work page 2024