Pith. sign in

REVIEW 4 major objections 5 minor 55 references

FERMI: Flexible Radio Mapping with a Hybrid Propagation Model and Scalable Autonomous Data Collection

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A hybrid physics-plus-neural model predicts radio signal strength between unseen transmitter-receiver pairs from sparse data.

desk verdict FERMI's LOS-point decomposition is a genuinely useful idea with solid real-world results, but the paper overclaims its generalization guarantee and needs error bars and a described baseline. read the letter →

arxiv 2504.14862 v1 pith:6HEDT26P submitted 2025-04-21 cs.RO

Yiming Luo , Yunfei Wang , Hongming Chen , Chengkai Wu , Ximin Lyu , Jinni Zhou , Jun Ma , Fu Zhang
show 1 more author
Boyu Zhou
This is my paper · ORCID
classification cs.RO
keywords radiomappinghybridpropagationmodelmultipathneuralradiancefieldsmulti-robotdatacollectionvisibility-basedpartitioningsignalstrengthprediction
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

FERMI claims that radio mapping in obstacle-rich environments can be made accurate and data-efficient by splitting propagation into two explicit physical stages and one learned stage. The direct path from a transmitter to its line-of-sight (LOS) surface points, and the direct path from a receiver's LOS surface points to the receiver, are computed with classical distance-based attenuation; only the multipath exchange between the two LOS point sets is learned by a neural network. Because the network's inputs are LOS-point coordinates rather than absolute transmitter or receiver positions, it is not tied to any specific pair and can predict signal strength for transmitter-receiver combinations never seen in training, provided their LOS points appear in the training data. The paper reports the lowest mean absolute error among tested methods in all five simulated scenes (5.59-11.29 dBm) and in a real corridor (6.24 dBm), using roughly 1-2 m sparse sampling and three robots.

What carries the argument

The central object is the multipath propagation network $F_\Theta: (P_{T_j}, P_{R_{w_k s_i}}) \rightarrow (\delta(P_R), S(P_T, P_R))$, built from two MLPs with hash-encoded inputs, where $\delta$ is a material-related attenuation coefficient at a receiver LOS point and $S = a\,e^{j\theta}$ is the complex signal retransmitted from a transmitter LOS point to a receiver LOS point. Each transmitter LOS point is treated as an independent retransmitter weighted by $\alpha_j$, the normalized direct-path energy (Eq. 2); receiver LOS point signals are integrated along rays with transmittance weights $L = \exp(-\sum \sigma \delta)$ (Eqs. 3-4), summed over directions with receiver gain $G(\omega)$ (Eq. 5), and combined with the LOS direct-path term (Eq. 6). The same LOS-point representation also drives the data-collection planner: visibility-based scene partitioning groups mutually visible surfaces so that placing robots across region pairs covers all mutually invisible surface-point pairs needed for multipath training.

What would settle it

Place a transmitter in a corner of a highly reflective room, train FERMI on dense coverage of that transmitter's LOS points, then predict signal strength at a receiver in a deep shadow niche and compare against ray-tracing ground truth that includes multi-bounce paths; a systematic large error in those receiver positions would falsify the independent-retransmitter assumption.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that the received signal strength for an arbitrary transmitter-receiver pair can be rendered from surface-level interactions rather than learned as a function of the two absolute positions. The transmitter's LOS points act as independent retransmitters, each carrying a weight equal to the normalized direct-path energy from the transmitter; the receiver-side signal is accumulated from its LOS points through a volume-rendering equation with learned attenuation coefficients, and under LOS conditions a physics-computed direct component is added. Trained only on sparsely sampled robot-collected measurements, this construction yields accurate predictions for unseen Tx-Rx pairs because the network's inputs are LOS point coordinates, which are largely shared between training and test pairs, and because each measured pair contributes thousands of LOS-point pairs to training.

Load-bearing premise

The key assumption is that each surface point near the transmitter re-radiates based only on the unoccluded direct path from the transmitter, so a surface point lit indirectly by other reflected signals is treated as if that indirect illumination did not exist; if this fails in highly reflective or deeply shadowed scenes, the claimed generalization collapses.

Editorial extensions

If this is right

  • A sparse radio map can predict signal strength for arbitrary points in the scene, not just the measured transmitter-receiver combinations, which is the property needed for communication-aware planning.
  • The autonomous collection method removes the manual Tx-Rx deployment bottleneck; a three-robot team collected around 40,000 valid measurements in a 94 m by 64 m corridor within one hour.
  • The planning solution scales to larger teams: using 9 robots cuts the number of configuration transitions from 88 (three robots) to 10, and planning time stays under six seconds for 15 robots.
  • Because each collected data point trains thousands of LOS-point pairs, the model degrades only mildly when the training set is sparsified, an effect the paper demonstrates in its data-reduction experiments.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same LOS-point decomposition could plausibly transfer to other wave fields with Huygens-like secondary sources, such as acoustic impulse responses, although the paper only demonstrates Wi-Fi.
  • The visibility-based region partitioning is a reusable primitive for any pairwise field measurement where multipath interactions matter, not only radio mapping.
  • Because the model is trained per scene, a natural next step the paper does not take is to test whether features learned in one building warm-start or transfer to a new building, potentially removing the retraining requirement.
  • The planner's greedy set-cover step could be replaced by an uncertainty-guided objective that prioritizes LOS point pairs with the largest predicted variance, making collection even more data-efficient.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes FERMI, a framework for building radio signal strength maps in large, occluded environments using sparse measurements collected by multiple robots. The approach decomposes propagation into three stages: a physics-based direct-path model from the transmitter to sampled line-of-sight (LOS) surface points, a neural network that models multipath propagation between pairs of LOS points associated with the transmitter and receiver, and a physics-based direct-path model from the receiver's LOS points to the receiver. A visibility-based scene partitioning method and a configuration-planning formulation are introduced to coordinate multi-robot data collection. The authors evaluate FERMI on five simulated scenes using Sionna ray tracing and in a real corridor environment with three robots, reporting lower mean absolute error than PropEM-L, Gaussian processes, and NeRF2 baselines, as well as improved scalability of data collection planning compared with a prior greedy method.

Significance. If the claims hold, FERMI would be a practically valuable contribution: it targets a real limitation of existing radio mapping methods, namely prediction for transmitter-receiver pairs not seen in training, and it combines this with an autonomous multi-robot collection strategy. The paper's strengths include open-sourced code, a real-world validation with a three-robot system, a clear problem formulation for data collection planning, and an explicit decomposition of propagation that is more physically motivated than fully black-box baselines. However, the central generalization claim is not established as stated, and several parameters and baseline adaptations are left underspecified; these issues are load-bearing for the paper's main contribution and need to be addressed before the results can be fully credited.

major comments (4)
  1. [Section IV-A, Generalization] The paper's central claim that the network generalizes to unseen Tx-Rx pairs 'provided their corresponding LOS points are covered by those in the training data' is not supported. The network input in Eq. (1) is a single pair of LOS points (P_Tj, P_Rwksi), and training supervises only the aggregate rendered signal for collected Tx-Rx pairs. Coverage of the individual LOS point sets does not imply coverage of the Cartesian product of Tx-LOS and Rx-LOS point pairs that is needed for a novel pair. Please provide either a proof of an interpolation or Lipschitz property of the hash-encoded MLP over this pair space, or experiments that quantify performance as a function of pair-novelty (e.g., error broken down by the fraction of LOS-point pairs seen in training). Without this, the generalization mechanism asserted in the abstract and Section IV-A remains untested.
  2. [Section IV-B, Eq. (2)] In Eq. (2), the weight alpha_j is defined as the normalized energy of the direct path from the transmitter to the LOS point P_Tj. Under the stated Huygens-Fresnel motivation, each surface point should be driven by the total incident field, including indirect illumination, and the phase of that field matters. Using scalar direct-path energy discards phase and assigns zero weight to surface points that are not directly visible from the transmitter, even if they are strongly illuminated by multipath. Consequently, the model cannot represent multipath contributions whose first surface interaction is with an NLOS point from the transmitter. The authors should quantify this approximation error (for example, by comparing against full ray tracing in deliberately shadowed scenes) or explain why such contributions are negligible in the tested environments.
  3. [Section VI-A, Tables I-III and Figs. 8-9] All quantitative results appear to be based on single runs without error bars or repeated-seed statistics. The reported MAE differences (for example, 5.59 dBm versus 9.12 dBm for the Room scene in Table I) are hard to interpret without variance estimates. In addition, the adaptation of the NeRF2 baseline to the multi-transmitter training and test protocol is not described; since NeRF2 in [53] models a fixed transmitter, the comparison may disadvantage the baseline. Please describe the baseline adaptations and report repeated-run statistics (mean and standard deviation) for the main tables and figures.
  4. [Section IV-B, Eq. (6)] The LOS compensation in Eq. (6) introduces alpha_los and Rref without specifying how they are computed. The text says alpha_los is a distance-related attenuation factor and equals zero under NLOS conditions, but no formula or calibration procedure is given, even though Fig. 9 shows this term is important for accuracy. The notation also mixes the complex quantity RRx with the magnitude |RRx| and the presumably real Rref. Please provide the exact definition of alpha_los, the normalization of Rref, and clarify the units and whether Eq. (6) operates on complex values or real magnitudes.
minor comments (5)
  1. [Section IV-B, Eq. (6)] Please clarify the relationship between RRx, |RRx|, and Rref in Eq. (6); it is unclear whether the equation combines complex amplitudes and then takes a magnitude, or whether it operates directly on dBm values.
  2. [Section V-C] The scene partitioning depends on a distance threshold D, but no value is reported; please state the threshold used in the experiments, along with the ray sampling count and hash-encoding resolution for the LOS point generation.
  3. [Section VI-A] For the Gaussian process baseline, the choice of kernel and hyperparameter settings is not given; please provide these details for reproducibility.
  4. [Section VI-A] Figure 6 is described as illustrating all five simulation scenes, but only one scene appears to be shown; please show all scenes or adjust the caption.
  5. [References and text] There are minor typographical and stylistic issues throughout, such as inconsistent capitalization in the reference to 'Ego-Swarm' [55]; a careful proofread is recommended.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: FERMI's radio predictions are evaluated on held-out Tx positions against external ray-tracing and real measurements.

full rationale

The core prediction pipeline is not circular. Training minimizes L_energy=... against measured RSSI (Eq. 7), and generalization is tested on Tx positions held out from training in five Sionna-simulated scenes (Table I) and on a 50/50 split of real corridor data (Table III). The test quantities are not used to fit alpha_j, delta, or the MLP weights. The Sec. IV-A statement that unseen Tx-Rx pairs can be predicted 'provided their corresponding LOS points are covered by those in the training data' is an asserted sufficiency condition; even if it is too strong (pairwise LOS-point combinations and indirect illumination are not guaranteed to be covered), that is a correctness/soundness concern, not a reduction of the prediction to its inputs. No load-bearing uniqueness theorem or ansatz is imported from the authors' prior work: citations to [45,46,47,54] are background for planning/localization/exploration and are not used to justify the propagation model; the volume-rendering analogy [29] and hash encoding [32] are external, standard tools. The autonomous collection planner is evaluated by transition counts/costs (Table II), metrics defined independently of the radio model, so it is not a renamed fit either. Overall, the central claims are self-contained against external benchmarks, and no equation in the paper reduces a predicted output to a fitted input by construction.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim depends on two unproven domain assumptions: the LOS-point secondary-source decomposition (Equations 1 to 5) and the visibility-partition coverage guarantee (Section V). These are not derived from first principles. Free parameters include the LOS compensation coefficient alpha_los in Equation 6 and several unreported thresholds and ray counts. No new physical entities are introduced; LOS points are algorithmic constructs, not postulated physical objects.

free parameters (3)
  • alpha_los (LOS direct-path coefficient) = not reported
    Equation 6 blends direct-path and multipath energy linearly, and Figure 9 shows it affects accuracy, but the paper never defines how alpha_los is computed or fitted.
  • scene-partition distance threshold D = not reported
    Section V-C uses D to decide whether surface points join a cluster; it shapes the region set used for the coverage guarantee but its value is not reported.
  • LOS point ray sampling count and hash-encoding resolution = not reported
    Section IV-A samples rays on a sphere for LOS points, but the number of rays and hash grid resolutions are not listed, so the sparse-data and coverage claims cannot be reproduced from the text alone.
assumptions (5)
  • domain assumption Secondary-source independence: each Tx LOS point re-radiates according only to the normalized direct-path energy alpha_j from the transmitter (Eq. 2).
    Sections IV-A and IV-B treat each Tx LOS point as an independent retransmitter; indirect illumination of surfaces is ignored, and no error bound is given for this approximation.
  • domain assumption LOS-point coverage: for unseen Tx-Rx positions, the sampled LOS points are largely present in the training set.
    Section IV-A uses this to claim generalization to unseen Tx-Rx pairs; it is an untested spatial coverage assumption.
  • domain assumption Region-pair coverage guarantee: placing Tx/Rx deployments between visibility-partitioned regions covers all necessary multipath components.
    Section V states this as a guarantee, yet matrix diagonal pairs within a region are marked as not required, so within-region multipath is assumed negligible.
  • domain assumption Volume rendering model: RF multipath accumulation follows the optical volume rendering equations of Max with a learned attenuation coefficient delta.
    Equations 3 to 5 borrow optical volume rendering; the mapping from RF attenuation to optical density is heuristic and learned, not derived from wave physics.
  • domain assumption Known static map: a point cloud or occupancy map of a static scene is available.
    Section III and the limitations section state this assumption; dynamic scenes are excluded.

how reviews work

0 comments
Cite this review

Pith. "Pith review of FERMI: Flexible Radio Mapping with a Hybrid Propagation Model and Scalable Autonomous Data Collection." pith.science (2026). https://pith.science/paper/6HEDT26P

@misc{pith2026250414862,
  author       = {Pith},
  title        = {Pith review of: FERMI: Flexible Radio Mapping with a Hybrid Propagation Model and Scalable Autonomous Data Collection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6HEDT26P}},
  note         = {Machine review of arXiv:2504.14862}
}
read the original abstract

Communication is fundamental for multi-robot collaboration, with accurate radio mapping playing a crucial role in predicting signal strength between robots. However, modeling radio signal propagation in large and occluded environments is challenging due to complex interactions between signals and obstacles. Existing methods face two key limitations: they struggle to predict signal strength for transmitter-receiver pairs not present in the training set, while also requiring extensive manual data collection for modeling, making them impractical for large, obstacle-rich scenarios. To overcome these limitations, we propose FERMI, a flexible radio mapping framework. FERMI combines physics-based modeling of direct signal paths with a neural network to capture environmental interactions with radio signals. This hybrid model learns radio signal propagation more efficiently, requiring only sparse training data. Additionally, FERMI introduces a scalable planning method for autonomous data collection using a multi-robot team. By increasing parallelism in data collection and minimizing robot travel costs between regions, overall data collection efficiency is significantly improved. Experiments in both simulation and real-world scenarios demonstrate that FERMI enables accurate signal prediction and generalizes well to unseen positions in complex environments. It also supports fully autonomous data collection and scales to different team sizes, offering a flexible solution for creating radio maps. Our code is open-sourced at https://github.com/ymLuo1214/Flexible-Radio-Mapping.

Figures

Figures reproduced from arXiv: 2504.14862 by the authors.

Figure 1
Figure 1. Flexible Radio Mapping (FERMI) is a data-driven, accurate, and efficient framework for signal data collection and propagation modeling. The framework enables the prediction of signal strength between any two positions using only sparse data. We demonstrate radio mapping results in an indoor corridor environment (94m × 64m × 3m), where (a) illustrates the point cloud map of the scene, (b) shows a photograph of the en… view at source ↗
Figure 2
Figure 2. Overview of FERMI: (a) With the collected signal data, we first sample Line-of-Sight (LOS) points for both the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. An illustration demonstrates the rendering process for [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: An illustration showing the robot team configuration [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: A schematic for our visibility-based scene partitioning [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: The simulation experiment scenarios and a schematic [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Visualization of spatial signal data generated by different methods in each simulation scenario. Ground truth data is [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 9
Figure 9. Figure 9: A comparison of prediction results with and with [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]
Figure 11
Figure 11. Figure 11: Examples for collected signal data. TABLE III: Results of Real-world Signal Prediction Test Set MAE (dBm) Ours 6.24 PropEM-L [8] 8.52 NeRF2 [53] 9.15 Additionally, we collect dense signal data generated from a fixed Tx point to perform qualitative analysis. As shown i…
Figure 10
Figure 10. Figure 10: The scene partitioning result and the configuration [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

55 extracted references · 49 canonical work pages

  1. [53]

    Nerf2: Neural radio-frequency radiance fields

    Xiaopeng Zhao, Zhenlin An, Qingrui Pan, and Lei Yang. Nerf2: Neural radio-frequency radiance fields. In Pro- ceedings of the 29th Annual International Conference on Mobile Computing and Networking , pages 1–15, 2023

    work page 2023

  2. [1]

    Online up- date of communication maps for exploring multirobot systems under connectivity constraints

    Francesco Amigoni, Jacopo Banfi, Nicola Basilico, Ioan- nis Rekleitis, and Alberto Quattrini Li. Online up- date of communication maps for exploring multirobot systems under connectivity constraints. In Distributed Autonomous Robotic Systems: The 14th International Symposium, pages 513–526. Springer, 2019

    work page 2019

  3. [2]

    Radar: An in-building rf-based user location and tracking sys- tem

    Paramvir Bahl and Venkata N Padmanabhan. Radar: An in-building rf-based user location and tracking sys- tem. In Proceedings IEEE INFOCOM 2000. Conference on computer communications. Nineteenth annual joint conference of the IEEE computer and communications societies (Cat. No. 00CH37064) , volume 2, pages 775–

    work page 2000

  4. [3]

    Multirobot online construction of communication maps

    Jacopo Banfi, Alberto Quattrini Li, Nicola Basilico, Ioan- nis Rekleitis, and Francesco Amigoni. Multirobot online construction of communication maps. In 2017 IEEE International Conference on Robotics and Automation (ICRA), pages 2577–2583. IEEE, 2017

    work page 2017

  5. [4]

    Op- timal redeployment of multirobot teams for communi- cation maintenance

    Jacopo Banfi, Nicola Basilico, and Stefano Carpin. Op- timal redeployment of multirobot teams for communi- cation maintenance. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages 3757–3764. IEEE, 2018

    work page 2018

  6. [5]

    Hollinger, and Sebastian Scherer

    Graeme Best, Rohit Garg, John Keller, Geoffrey A. Hollinger, and Sebastian Scherer. Resilient Multi-Sensor Exploration of Multifarious Environments with a Team of Aerial Robots. In Proceedings of Robotics: Science and Systems, New York City, NY , USA, June 2022. doi: 10.15607/RSS.2022.XVIII.004

    work page doi:10.15607/rss.2022.xviii.004 2022

  7. [6]

    Navigation strategy with multi-robots in con- fined environments to improve radio signal coverage

    Sofia Pereira Campos, Andr ´e Luiz Maciel Cid, Arthur Da Costa Vangasse, Gilmar J ´unior, Jo˜ao Bai ˜ao, Luciano Pimenta, Luiz Barros, Gustavo Pessin, and Gustavo Freitas. Navigation strategy with multi-robots in con- fined environments to improve radio signal coverage. In 2023 Latin American Robotics Symposium (LARS), 2023 Brazilian Symposium on Robotics...

    work page 2023

  8. [7]

    Representation granularity enables time-efficient autonomous exploration in large, complex worlds

    Chao Cao, Hongbiao Zhu, Zhongqiang Ren, Howie Choset, and Ji Zhang. Representation granularity enables time-efficient autonomous exploration in large, complex worlds. Science Robotics, 8(80):eadf0970, 2023

    work page 2023

Show all 55 references
  1. [8]

    PropEM-L: Radio Propagation Environment Modeling and Learning for Communication-Aware Multi-Robot Exploration

    Lillian Clark, Jeffrey Edlund, Marc Sanchez Net, Tiago Stegun Vaquero, and Ali akbar Agha-mohammadi. PropEM-L: Radio Propagation Environment Modeling and Learning for Communication-Aware Multi-Robot Exploration. In Proceedings of Robotics: Science and Systems, New York City, N...

    2022 doi

  2. [9]

    Graph- based subterranean exploration path planning using aerial and legged robots

    Tung Dang, Marco Tranzatto, Shehryar Khattak, Frank Mascarich, Kostas Alexis, and Marco Hutter. Graph- based subterranean exploration path planning using aerial and legged robots. Journal of Field Robotics , 37(8): 1363–1388, 2020

    2020

  3. [10]

    Maintaining optimal communication chains in robotic sensor networks using mobility control

    Cory Dixon and Eric W Frew. Maintaining optimal communication chains in robotic sensor networks using mobility control. Mobile Networks and Applications , 14: 281–291, 2009

    2009

  4. [11]

    Online methods for radio signal mapping with mobile robots

    Jonathan Fink and Vijay Kumar. Online methods for radio signal mapping with mobile robots. In 2010 IEEE International Conference on Robotics and Automation , pages 1940–1945. IEEE, 2010

    2010

  5. [12]

    Robust control of mobility and communications in au- tonomous robot teams

    Jonathan Fink, Alejandro Ribeiro, and Vijay Kumar. Robust control of mobility and communications in au- tonomous robot teams. IEEE Access, 1:290–309, 2013

    2013

  6. [13]

    Multirobot data gathering under buffer constraints and intermittent com- munication

    Meng Guo and Michael M Zavlanos. Multirobot data gathering under buffer constraints and intermittent com- munication. IEEE transactions on robotics , 34(4):1082– 1097, 2018

    2018

  7. [14]

    Gaussian processes for signal strength-based location estimation

    Brian Ferris Dirk H ¨ahnel and Dieter Fox. Gaussian processes for signal strength-based location estimation. In Proceeding of robotics: science and systems . Citeseer, 2006

    2006

  8. [15]

    An effective implementation of the lin– kernighan traveling salesman heuristic

    Keld Helsgaun. An effective implementation of the lin– kernighan traveling salesman heuristic. European journal of operational research, 126(1):106–130, 2000

    2000

  9. [16]

    Sionna: An open-source library for next-generation physical layer research

    Jakob Hoydis, Sebastian Cammerer, Fayc ¸al Ait Aoudia, Avinash Vem, Nikolaus Binder, Guillermo Marcus, and Alexander Keller. Sionna: An open-source library for next-generation physical layer research. arXiv preprint, Mar. 2022

    2022

  10. [17]

    Sionna rt: Differentiable ray tracing for radio propagation modeling

    Jakob Hoydis, Fayc ¸al A¨ıt Aoudia, Sebastian Cammerer, Merlin Nimier-David, Nikolaus Binder, Guillermo Mar- cus, and Alexander Keller. Sionna rt: Differentiable ray tracing for radio propagation modeling. In 2023 IEEE Globecom Workshops (GC Wkshps) , pages 317–

    2023

  11. [18]

    Learning radio environ- ments by differentiable ray tracing

    Jakob Hoydis, Fayc ¸al A ¨ıt Aoudia, Sebastian Cam- merer, Florian Euchner, Merlin Nimier-David, Stephan Ten Brink, and Alexander Keller. Learning radio environ- ments by differentiable ray tracing. IEEE Transactions on Machine Learning in Communications and Networking , 2024

    2024

  12. [19]

    Con- structing radio signal strength maps with multiple robots

    M-Y A Hsieh, Vijay Kumar, and Camillo J Taylor. Con- structing radio signal strength maps with multiple robots. In IEEE International Conference on Robotics and Au- tomation, 2004. Proceedings. ICRA’04. 2004 , volume 4, pages 4184–4189. IEEE, 2004

    2004

  13. [20]

    2d gaussian splatting for geometri- cally accurate radiance fields

    Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2d gaussian splatting for geometri- cally accurate radiance fields. In ACM SIGGRAPH 2024 conference papers, pages 1–11, 2024

    2024

  14. [21]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139– 1, 2023

    2023

  15. [22]

    Spotfi: Decimeter level localization using wifi

    Manikanta Kotaru, Kiran Joshi, Dinesh Bharadia, and Sachin Katti. Spotfi: Decimeter level localization using wifi. In Proceedings of the 2015 ACM conference on special interest group on data communication , pages 269–282, 2015

    2015

  16. [23]

    An autonomous unmanned aerial vehicle system for fast exploration of large complex indoor environ- ments

    V ´ıt Kr ´atk`y, Pavel Petr ´aˇcek, Tom ´aˇs B ´aˇca, and Martin Saska. An autonomous unmanned aerial vehicle system for fast exploration of large complex indoor environ- ments. Journal of field robotics, 38(8):1036–1058, 2021

    2021

  17. [24]

    Autonomous teamed exploration of subterranean environments using legged and aerial robots

    Mihir Kulkarni, Mihir Dharmadhikari, Marco Tranzatto, Samuel Zimmermann, Victor Reijgwart, Paolo De Petris, Huan Nguyen, Nikhil Khedekar, Christos Papachristos, Lionel Ott, et al. Autonomous teamed exploration of subterranean environments using legged and aerial robots. In 202...

    2022

  18. [25]

    Acoustic volume rendering for neural impulse response fields

    Zitong Lan, Chenhao Zheng, Zhiwei Zheng, and Ming- min Zhao. Acoustic volume rendering for neural impulse response fields. arXiv preprint arXiv:2411.06307 , 2024

    2024 arXiv

  19. [26]

    A scalable and generalizable pathloss map prediction

    Ju-Hyung Lee and Andreas F Molisch. A scalable and generalizable pathloss map prediction. IEEE Transac- tions on Wireless Communications , 2024

    2024

  20. [27]

    Radiounet: Fast radio map estimation with convo- lutional neural networks

    Ron Levie, C ¸ a˘gkan Yapar, Gitta Kutyniok, and Giuseppe Caire. Radiounet: Fast radio map estimation with convo- lutional neural networks. IEEE Transactions on Wireless Communications, 20(6):4001–4015, 2021

    2021

  21. [28]

    A deep learning framework for wireless radiation field reconstruction and channel prediction

    Haofan Lu, Christopher Vattheuer, Baharan Mirza- soleiman, and Omid Abari. A deep learning framework for wireless radiation field reconstruction and channel prediction. arXiv preprint arXiv:2403.03241 , 2024

    2024 arXiv

  22. [29]

    Optical models for direct volume render- ing

    Nelson Max. Optical models for direct volume render- ing. IEEE Transactions on Visualization and Computer Graphics, 1(2):99–108, 1995

    1995

  23. [30]

    Nerf: Representing scenes as neural radiance fields for view synthesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM , 65(1):99– 106, 2021

    2021

  24. [31]

    Improving gaussian processes based mapping of wireless signals using path loss models

    Renato Miyagusuku, Atsushi Yamashita, and Hajime Asama. Improving gaussian processes based mapping of wireless signals using path loss models. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages 4610–4615. IEEE, 2016

    2016

  25. [32]

    Instant neural graphics primitives with a multiresolution hash encoding

    Thomas M ¨uller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM transactions on graphics (TOG), 41(4):1–15, 2022

    2022

  26. [33]

    Winert: Towards neural ray tracing for wireless channel mod- elling and differentiable simulations

    Tribhuvanesh Orekondy, Pratik Kumar, Shreya Kadambi, Hao Ye, Joseph Soriaga, and Arash Behboodi. Winert: Towards neural ray tracing for wireless channel mod- elling and differentiable simulations. In The Eleventh International Conference on Learning Representations , 2023

    2023

  27. [34]

    Multi- robot exploration for building communication maps with prior from communication models

    Phani Krishna Penumarthi, Alberto Quattrini Li, Ja- copo Banfi, Nicola Basilico, Francesco Amigoni, Jason O’Kane, Ioannis Rekleitis, and Srihari Nelakuditi. Multi- robot exploration for building communication maps with prior from communication models. In 2017 International Sym...

    2017

  28. [35]

    Calibrating wireless ray tracing for digital twinning using local phase error estimates

    Clement Ruah, Osvaldo Simeone, Jakob Hoydis, and Bashir Al-Hashimi. Calibrating wireless ray tracing for digital twinning using local phase error estimates. IEEE Transactions on Machine Learning in Communications and Networking, 2024

    2024

  29. [36]

    Decentralized connectivity maintenance for cooperative control of mobile robotic systems

    Lorenzo Sabattini, Nikhil Chopra, and Cristian Secchi. Decentralized connectivity maintenance for cooperative control of mobile robotic systems. The International Journal of Robotics Research , 32(12):1411–1423, 2013

    2013

  30. [37]

    Gpps: A gaussian process positioning system for cellular networks

    Anton Schwaighofer, Marian Grigoras, V olker Tresp, and Clemens Hoffmann. Gpps: A gaussian process positioning system for cellular networks. Advances in Neural Information Processing Systems , 16, 2003

    2003

  31. [38]

    Mri: Model-based radio interpolation for indoor war-walking

    Hyojeong Shin, Yohan Chon, Yungeun Kim, and Hojung Cha. Mri: Model-based radio interpolation for indoor war-walking. IEEE Transactions on Mobile Computing , 14(6):1231–1244, 2014

    2014

  32. [39]

    Connec- tivity management in mobile robot teams

    Ethan Stump, Ali Jadbabaie, and Vijay Kumar. Connec- tivity management in mobile robot teams. In 2008 IEEE international conference on robotics and automation , pages 1525–1530. IEEE, 2008

    2008

  33. [40]

    Visibility-based deployment of robot formations for communication maintenance

    Ethan Stump, Nathan Michael, Vijay Kumar, and V olkan Isler. Visibility-based deployment of robot formations for communication maintenance. In 2011 IEEE international conference on robotics and automation , pages 4498–

    2011

  34. [41]

    Enforcing network connectivity in robot team missions

    Danilo Tardioli, Alejandro R Mosteo, Luis Riazuelo, Jos´e Luis Villarroel, and Luis Montano. Enforcing network connectivity in robot team missions. The Inter- national Journal of Robotics Research , 29(4):460–480, 2010

    2010

  35. [42]

    Robotic routers: Algorithms and implementation

    Onur Tekdas, Wei Yang, and V olkan Isler. Robotic routers: Algorithms and implementation. The Inter- national Journal of Robotics Research , 29(1):110–126, 2010

    2010

  36. [43]

    ihero: Interactive human-oriented exploration and supervision under scarce communication

    Zhuoli Tian, Yuyang Zhang, Jinsheng Wei, and Meng Guo. ihero: Interactive human-oriented exploration and supervision under scarce communication. arXiv preprint arXiv:2405.12571, 2024

    2024 arXiv

  37. [44]

    Wrf-gs: Wireless radiation field reconstruction with 3d gaussian splatting

    Chaozheng Wen, Jingwen Tong, Yingdong Hu, Zehong Lin, and Jun Zhang. Wrf-gs: Wireless radiation field reconstruction with 3d gaussian splatting. arXiv preprint arXiv:2412.04832, 2024

    2024 arXiv

  38. [45]

    Real-time whole-body motion planning for mobile manipulators using environment- adaptive search and spatial-temporal optimization

    Chengkai Wu, Ruilin Wang, Mianzhi Song, Fei Gao, Jie Mei, and Boyu Zhou. Real-time whole-body motion planning for mobile manipulators using environment- adaptive search and spatial-temporal optimization. In 2024 IEEE International Conference on Robotics and Automation (ICRA), ...

    2024

  39. [46]

    Relink: Real-time line-of-sight-based deployment framework of multi-robot for maintaining a communication network

    Lijia Xia, Beiming Deng, Jie Pan, Xiaoxun Zhang, Peiming Duan, Boyu Zhou, and Hui Cheng. Relink: Real-time line-of-sight-based deployment framework of multi-robot for maintaining a communication network. IEEE Robotics and Automation Letters , 2023

    2023

  40. [47]

    Fast-lio: A fast, robust lidar- inertial odometry package by tightly-coupled iterated kalman filter

    Wei Xu and Fu Zhang. Fast-lio: A fast, robust lidar- inertial odometry package by tightly-coupled iterated kalman filter. IEEE Robotics and Automation Letters , 6(2):3317–3324, 2021

    2021

  41. [48]

    Robotic router formation in realistic communication environments

    Yuan Yan and Yasamin Mostofi. Robotic router formation in realistic communication environments. IEEE Transac- tions on Robotics , 28(4):810–827, 2012

    2012

  42. [49]

    A survey and analysis of multi-robot coordination

    Zhi Yan, Nicolas Jouandeau, and Arab Ali Cherif. A survey and analysis of multi-robot coordination. Inter- national Journal of Advanced Robotic Systems , 10(12): 399, 2013

    2013

  43. [50]

    Minimally constrained multi-robot coordination with line-of-sight connectivity maintenance

    Yupeng Yang, Yiwei Lyu, and Wenhao Luo. Minimally constrained multi-robot coordination with line-of-sight connectivity maintenance. In 2023 IEEE International Conference on Robotics and Automation (ICRA) , pages 7684–7690. IEEE, 2023

    2023

  44. [51]

    Integrating online learning and connec- tivity maintenance for communication-aware multi-robot coordination

    Yupeng Yang, Yiwei Lyu, Yanze Zhang, Ian Gao, and Wenhao Luo. Integrating online learning and connec- tivity maintenance for communication-aware multi-robot coordination. In 2024 IEEE/RSJ International Confer- ence on Intelligent Robots and Systems (IROS) , pages 5770–5776. IEEE, 2024

    2024

  45. [52]

    Cellular network radio propagation modeling with deep convolutional neural networks

    Xin Zhang, Xiujun Shu, Bingwen Zhang, Jie Ren, Lizhou Zhou, and Xin Chen. Cellular network radio propagation modeling with deep convolutional neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on knowledge discovery & data mining , pages 2378–2386, 2020

    2020

  46. [54]

    Racer: Rapid collaborative exploration with a decentralized multi-uav system

    Boyu Zhou, Hao Xu, and Shaojie Shen. Racer: Rapid collaborative exploration with a decentralized multi-uav system. IEEE Transactions on Robotics , 39(3):1816– 1835, 2023

    2023

  47. [55]

    Ego-swarm: A fully autonomous and decentral- ized quadrotor swarm system in cluttered environments

    Xin Zhou, Jiangchao Zhu, Hongyu Zhou, Chao Xu, and Fei Gao. Ego-swarm: A fully autonomous and decentral- ized quadrotor swarm system in cluttered environments. In 2021 IEEE international conference on robotics and automation (ICRA), pages 4101–4107. IEEE, 2021

    2021

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.