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REVIEW 4 major objections 6 minor 47 references

Communication-Efficient Cooperative SLAMMOT via Determining the Number of Collaboration Vehicles

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that a connected vehicle fleet can match raw-data cooperative SLAMMOT accuracy while sending only compact descriptors and spatially sparse feature maps, because each vehicle can determine which partners and which spatial…

desk verdict Useful integration of selective communication into C-SLAMMOT, but the headline bandwidth savings are unmeasured because the protocol's handshake traffic isn't counted. read the letter →

arxiv 2411.17432 v1 pith:AS4YY5XB submitted 2024-11-26 cs.RO cs.MA

Susu Fang , Hao Li This is my paper · ORCID
classification cs.ROcs.MA
keywords cooperativeSLAMmulti-objecttrackingcommunicationefficiencyspatialconfidencemapplacerecognitionrelativeposeestimationLiDARperceptionvehicle-to-vehiclecollaboration
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

Cooperative SLAMMOT, which simultaneously localizes a vehicle, builds a map, and tracks moving objects with help from other vehicles, normally requires every car to broadcast its raw LiDAR scan to every partner, which does not scale. The paper claims that the same accuracy is reachable by first sending two tiny request messages: a sequence-level global descriptor that identifies the one partner with the most similar map, and a spatial confidence map that marks where the ego vehicle's own detector is unsure. Only the chosen partners then send back local feature descriptors and spatially sparse, confidence-weighted feature maps, which are fused in a joint factor-graph backend. In experiments on simulated and real cooperative-driving datasets, ego-pose error stays within a few centimeters of the raw-sharing baseline while communication volume drops by up to a third, and the cooperative detector beats the previous best detector at equal or lower bandwidth. The value of the claim, if true, is that cooperative perception and localization become practical under realistic bandwidth limits as fleet size grows.

What carries the argument

The load-bearing mechanism is a two-stage request-and-response selection scheme. In the cooperative SLAM stage, a sequence-level global descriptor, a compact vector encoding spatio-temporal features of recent bird's-eye-view intensity frames, is broadcast as the request; the responding vehicle is the one whose local map matches best, and it sends only local feature descriptors for relative pose estimation. In the cooperative perception stage, the request is the spatial confidence map $C$, inverted to $N = 1 - C$, which scores where the ego detector is unsure; partners are ranked by how little their detected occupied regions overlap the ego's, and each sends only the confidence-weighted sparse feature map in the requested areas. The selected relative-pose factor and object-perception factors are then optimized together with odometry factors in a joint factor-graph backend, with asynchronous incorporation of non-keyframe object detections. The key identity is the complementarity rule $V_{e\rightarrow n} = f_{\mathrm{decide1}}(\mathrm{sum}(D_e \cap D_n)/\mathrm{sum}(D_e \cup D_n))$, which converts two confidence maps into a decision about whether a neighbor's information is complementary enough to request.

What would settle it

Use a scene where the ego detector is confidently wrong, for instance a fully occluded vehicle it places with high confidence or a hallucinated object in an empty region. If, under that confidence map, the request mechanism fails to request the occluded region and detection or tracking accuracy collapses to single-vehicle level while communication stays low, then the confidence-map assumption is the point of failure.

Watch

Extended reading notes

Core claim

The paper's central claim is that content-aware selection of collaboration vehicles, rather than raw observation sharing, is sufficient to preserve the benefits of tightly coupled cooperative SLAM and moving-object tracking. For localization, the ego vehicle broadcasts only a sequence global descriptor built from its recent bird's-eye-view intensity images; the neighbor with the most similar local map is chosen, and only that vehicle's local feature descriptors and current pose are transmitted, letting the ego solve an inter-vehicle relative pose by feature matching and RANSAC. For perception, the ego's spatial confidence map is inverted into a request map $N = 1 - C$, and partners whose detected occupied regions have the least overlap with the ego's are selected; each selected partner sends only the spatially sparse feature-map cells that the ego requested, and these are fused through an axial attention module. A joint factor graph then integrates the resulting relative-pose factor and object-perception factors with odometry and motion factors, and the reported experiments show this system matching the raw-data C-SLAMMOT baseline in ego-pose MEAN/RMSE, exceeding the previous best cooperative detector on the OPV2V and V2V4Real benchmarks, and cutting communication volume while keeping detection accuracy.

Load-bearing premise

The load-bearing premise is that the ego vehicle's spatial confidence map reliably shows where its own perception is deficient, so every low-confidence region is actually a region where help is needed.

Editorial extensions

If this is right

  • Cooperative localization and perception can be decoupled from raw bandwidth: the same pose accuracy as raw sharing is reachable by exchanging compact descriptors and sparse features, so the number of collaborating vehicles is no longer limited by link capacity.
  • Dynamic vehicle selection introduces a tunable performance-communication knob, allowing operators to set a target confidence or accuracy and let the system choose how many vehicles to contact rather than always contacting all.
  • Because perception requests are spatial, a partner only needs to send the regions the ego lacks, which naturally suppresses irrelevant or distracting information and can improve detection relative to full-map fusion.
  • The two selection criteria, place similarity for SLAM and spatial complementarity for perception, can be tuned independently, so a fleet can optimize localization partners and perception partners separately.

Reading between the lines

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

  • The same request-map logic could be applied to heterogeneous sensor suites: a vehicle with cameras could request specific bird's-eye-view regions from a LiDAR-equipped partner, since the request is expressed in spatial confidence rather than raw sensor format.
  • A natural follow-up is to make the confidence map end-to-end trainable with a communication penalty, so the system learns where to be uncertain rather than treating the detector's confidence as fixed.
  • The two selection mechanisms suggest a unified utility: score each potential partner by a weighted combination of place-recognition similarity and spatial complementarity, which could determine a single number of collaboration vehicles serving both localization and perception simultaneously.
  • The observed improvement over full-map fusion hints at a broader principle: gating out irrelevant information can act as a regularizer for cooperative perception, not just a bandwidth saver.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper presents CE C-SLAMMOT, a cooperative SLAMMOT system that reduces communication by (i) using a sequence global descriptor (SeqVPR) and local features (SuperPoint/SuperGlue) to select a single best collaboration vehicle for inter-vehicle relative pose estimation, and (ii) using spatial confidence maps (from Where2comm) to select which neighboring vehicles and which spatial regions should be shared for cooperative object detection, fusing the selected features with a CoBEVT-style axial attention. The system is evaluated on OPV2V and V2V4Real, comparing ego-pose accuracy, detection AP, MOTA, and communication volume against SLAM-only, cooperative SLAM, C-SLAMMOT, and cooperative perception baselines. The reported results show that CE C-SLAMMOT achieves ego-pose errors within a few centimeters of the raw-data C-SLAMMOT baseline, and CE CoBEVT reaches or exceeds CoBEVT accuracy at lower reported communication volume.

Significance. If the reported results hold, the paper provides evidence that content-aware, confidence-guided selection can replace raw observation sharing in tightly coupled cooperative SLAMMOT without significant accuracy loss, and that the perception module advances the state of the art on two public benchmarks. The work integrates two previously separate research threads, and the experiments use public datasets and published baselines, which strengthens external validity. The proposed CE CoBEVT module and the unified factor-graph backend are concrete contributions. However, the headline efficiency result is currently gated by measurement and reporting gaps, so the significance depends on whether those gaps can be closed.

major comments (4)
  1. [III-A, Table IV] The communication-volume metric is not defined end-to-end, and the headline reductions do not follow from the table. Section III-A describes a two-stage handshake: the ego vehicle broadcasts a global descriptor to all neighbors and sends spatial confidence maps before receiving selected local features or feature maps. The absolute SLAM numbers in Section IV-B (250-286 KB) count only local features from the selected vehicle, and Table IV's CommVol is adopted from Where2comm without stating whether it includes the descriptor broadcast and confidence-map messages. The claimed savings (32.6% on OPV2V Culver City, 6.5% on Default Towns, 16.1% on V2V4Real) also cannot be read off Table IV, because no CE CoBEVT row at the matched AP exists for those percentages. Please provide a full byte-level accounting for the complete protocol, including control messages, and specify exactly how the percentage savings are computed, for example by interpolation on the AP-CommVol curve.
  2. [Tables III-VII] No error bars, confidence intervals, or significance tests are reported. The central claim that selective communication maintains accuracy comparable to full communication rests on point estimates whose differences are often very small, e.g., MEAN 0.226 versus 0.219 in the OPV2V T-junction in Table III. Without variance across runs, scenes, or resamples, the reader cannot judge whether the observed differences are noise. Please add repeated-run or per-scene variability and, where possible, paired significance tests for the main comparisons, especially for Table III, Table IV, and Table VII.
  3. [III-B3, Eqs. (1)-(2)] The relative pose estimation converts a 2D BEV transformation into 3D by zero-padding, which sets z, roll, and pitch to zero. This assumption is load-bearing for the inter-vehicle factor in the graph optimization, but the paper provides no justification or sensitivity analysis. On non-flat or banked roads the zero-padding could bias the ego-pose estimates. Please either validate that the test scenes are sufficiently planar, quantify the error introduced by the 2D assumption, or extend the method to estimate the full 3D transformation.
  4. [III-C] The selection mechanism for cooperative perception assumes that the ego spatial confidence map, with request map N = 1 - C, reliably indicates where perception is deficient. If the ego detector is confidently wrong, for example due to a missed occluded object or a hallucinated object, the request map will direct attention to irrelevant areas and the performance-communication trade-off will degrade. No calibration or failure analysis of the confidence map is provided. Please add a discussion of this failure mode and an experiment that quantifies degradation when the confidence map is imperfect.
minor comments (6)
  1. [Table VII] The table header contains the typo "sptical", which should be "spatial". Also, the text mentions a communication rate of 0.808 without defining how this rate is calculated.
  2. [Eq. (8)] Equation (8) has duplicated superscripts in the first sum, and the accompanying text says "standard derivation" where "standard deviation" is intended; please fix the equation formatting and the wording.
  3. [Table IV] The table caption should clarify that the six rows for each method group correspond to different datasets (Default Towns, Culver City, V2V4Real), as the current layout is easy to misread.
  4. [IV-B, Fig. 3] The text says Figure 3 shows results with different numbers of collaboration vehicles, but the caption only lists two scenes and does not explain how the additional vehicles are selected from the ranking; please clarify the selection procedure for the multi-vehicle cases.
  5. [III-C] The paper states that pre-trained SuperPoint weights are used on BEV intensity images without discussing whether this introduces a domain gap; a sentence on why the transfer is reasonable, or a comparison with a BEV-trained feature extractor, would help.
  6. [Abstract, Conclusion] The title and abstract claim that the method determines the number of collaboration vehicles, but the two modules select numbers independently and the conclusion states that finding a unique number when both aspects are considered is future work; this scope limitation should be acknowledged earlier in the paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the efficiency and accuracy claims are empirical benchmark results, not derivation-equivalent to their inputs.

full rationale

CE C-SLAMMOT's central claims are empirical evaluations on the OPV2V and V2V4Real datasets against published baselines such as Where2comm, CoBEVT, and the authors' prior C-SLAMMOT work. The selection mechanisms, namely SeqVPR-based place recognition and spatial confidence map-based cooperation, are heuristics whose value is established by benchmark tables; none of the reported AP, MEAN/RMSE, MOTA, or CommVol numbers is a fitted parameter renamed as a prediction. Equations (1) through (8) are standard pose composition, relative pose, and factor-graph constraints; they do not encode the claimed efficiency result by construction. The paper does cite the authors' own prior work, notably C-SLAMMOT [16] as a baseline and Where2comm [18] as a component and comparison method, but these are externally published, peer-reviewed results and the present paper's comparisons are direct measurements against them, so the citations are not load-bearing in a circular sense. The limitations noted in the paper, such as relying on the ego confidence map to request complementary information, zero-padding a 2D relative transformation into 3D, and the absence of full end-to-end byte accounting for the communication handshake, are robustness or measurement-scope concerns rather than circular derivation steps.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central claim rests on several unverified domain assumptions: the reliability of sequence descriptor ranking, the transferability of SuperPoint to BEV images, the validity of zero-padded 2D-to-3D relative poses, and the calibration of ego spatial confidence maps. The factor graph and noise injection add unreported tunable parameters. No invented entities are introduced.

free parameters (5)
  • Spatial confidence threshold for dynamic mask = not reported
    Used in Sec. III-C to convert each vehicle's spatial confidence map into binary dynamic masks De and Dn before computing complementarity. The threshold value is not given and directly controls which vehicles and areas are selected.
  • Selection functions fdecide1 and fdecide2 parameters = not reported
    These functions decide whether a neighbor is complementary and which spatial areas are critical. The paper defines their input-output form but not the learned or hand-set parameters.
  • Factor graph covariance weights P_odo, P_en, P_op, P_mov, P_v = not reported
    The joint optimization in Eq. (8) weights odometry, inter-vehicle, object perception, motion, and velocity factors. The covariance matrices are not specified and likely tuned, affecting the reported tracking errors.
  • Gaussian noise standard deviations for Sim-Noisy test = 0.25, 0.35, 0.85 for three scenes
    In Sec. IV-C, simulated localization noise is injected with standard deviations taken from the proposed CE C-SLAM pose errors. These values are method-dependent and chosen for the test protocol.
  • Communication volume budget = 12.2 to 23.0 (log2 bytes), row dependent
    The trade-off curves in Tables IV and VI are generated by varying a communication budget. The budget is a free control parameter, not predicted by the method.
assumptions (6)
  • domain assumption SeqVPR sequence descriptors reliably rank inter-vehicle place similarity in dynamic environments.
    Sec. III-B2 assumes the most similar frame found by SeqVPR is the best collaboration partner for relative pose estimation. No failure analysis for dynamic scenes is provided.
  • ad hoc to paper Pre-trained SuperPoint features transfer to BEV intensity images.
    Sec. III-B3 states the authors lack a synthetic BEV dataset and use preset SuperPoint weights, assuming the network generalizes to BEV projections.
  • ad hoc to paper A 2D BEV transformation zero-padded to 3D represents the inter-vehicle relative pose.
    Sec. III-B3 converts the RANSAC 2D transform Delta T into 3D by zero-padding, which assumes ground-plane motion and ignores vertical, roll, and pitch differences.
  • domain assumption Ego spatial confidence map is a reliable proxy for missing complementary information.
    Sec. III-C sets the request map as 1 - C. If the ego detector is overconfident or miscalibrated, the selected vehicles and areas will not supply the needed information.
  • standard math Constant velocity and constant turn rate motion model for tracked objects.
    Sec. III-D Eqs. (6) and (7) assume short-time constant velocity, a standard tracking assumption.
  • domain assumption LeGO-LOAM provides sufficiently accurate single-vehicle odometry in evaluated scenes.
    Sec. III-B1 selects LeGO-LOAM as the single-vehicle SLAM backbone. The system's final accuracy inherits its limitations.

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Pith. "Pith review of Communication-Efficient Cooperative SLAMMOT via Determining the Number of Collaboration Vehicles." pith.science (2026). https://pith.science/paper/AS4YY5XB

@misc{pith2026241117432,
  author       = {Pith},
  title        = {Pith review of: Communication-Efficient Cooperative SLAMMOT via Determining the Number of Collaboration Vehicles},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AS4YY5XB}},
  note         = {Machine review of arXiv:2411.17432}
}
read the original abstract

The SLAMMOT, i.e. simultaneous localization, mapping, and moving object (detection and) tracking, represents an emerging technology for autonomous vehicles in dynamic environments. Such single-vehicle systems still have inherent limitations, such as occlusion issues. Inspired by SLAMMOT and rapidly evolving cooperative technologies, it is natural to explore cooperative simultaneous localization, mapping, moving object (detection and) tracking (C-SLAMMOT) to enhance state estimation for ego-vehicles and moving objects. C-SLAMMOT could significantly upgrade the single-vehicle performance by utilizing and integrating the shared information through communication among the multiple vehicles. This inevitably leads to a fundamental trade-off between performance and communication cost, especially in a scalable manner as the number of collaboration vehicles increases. To address this challenge, we propose a LiDAR-based communication-efficient C-SLAMMOT (CE C-SLAMMOT) method by determining the number of collaboration vehicles. In CE C-SLAMMOT, we adopt descriptor-based methods for enhancing ego-vehicle pose estimation and spatial confidence map-based methods for cooperative object perception, allowing for the continuous and dynamic selection of the corresponding critical collaboration vehicles and interaction content. This approach avoids the waste of precious communication costs by preventing the sharing of information from certain collaborative vehicles that may contribute little or no performance gain, compared to the baseline method of exchanging raw observation information among all vehicles. Comparative experiments in various aspects have confirmed that the proposed method achieves a good trade-off between performance and communication costs, while also outperforms previous state-of-the-art methods in cooperative perception performance.

Figures

Figures reproduced from arXiv: 2411.17432 by the authors.

Figure 1
Figure 1. Architecture of the presented CE C-SLAMMOT. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. The factor graph model in proposed CE C-SLAMMOT solution. (a) [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Ego-trajectory error maps for presented method with different numbers [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: The results of CE CoBEVT selecting cooperative vehicles. (a) and [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

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Reference graph

Works this paper leans on

47 extracted references · 37 canonical work pages

  1. [1]

    Simultaneous localization, mapping and moving object tracking,

    C.-C. Wang, C. Thorpe, S. Thrun, M. Hebert, and H. Durrant-Whyte, “Simultaneous localization, mapping and moving object tracking,” In- ternational Journal of Robotics Research , vol. 26, no. 9, pp. 889–916, 2007

    2007

  2. [2]

    DynaSLAM II: Tightly-coupled multi-object tracking and SLAM,

    B. Bescos, C. Campos, J. D. Tard ´os, and J. Neira, “DynaSLAM II: Tightly-coupled multi-object tracking and SLAM,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 5191–5198, 2021

    2021

  3. [3]

    DL-SLOT: Tightly- coupled dynamic LiDAR SLAM and 3D object tracking based on collaborative graph optimization,

    X. Tian, Z. Zhu, J. Zhao, G. Tian, and C. Ye, “DL-SLOT: Tightly- coupled dynamic LiDAR SLAM and 3D object tracking based on collaborative graph optimization,” IEEE Transactions on Intelligent Vehicles, vol. 9, no. 1, pp. 1017–1027, 2024

    2024

  4. [4]

    IMM-SLAMMOT: Tightly-coupled SLAM and IMM-based multi-object tracking,

    Z. Ying and H. Li, “IMM-SLAMMOT: Tightly-coupled SLAM and IMM-based multi-object tracking,” IEEE Transactions on Intelligent Vehicles, vol. 9, no. 2, pp. 3964–3974, 2024

    2024

  5. [5]

    Multi-vehicle cooperative perception and augmented reality for driver assistance: A possibility to see through front vehicle,

    H. Li and F. Nashashibi, “Multi-vehicle cooperative perception and augmented reality for driver assistance: A possibility to see through front vehicle,” in IEEE International Conference on Intelligent Transportation Systems, 2011, pp. 242–247

    work page 2011

  6. [6]

    Multi-vehicle cooperative local mapping: A methodology based on occupancy grid map merging,

    H. Li, M. Tsukada, F. Nashashibi, and M. Parent, “Multi-vehicle cooperative local mapping: A methodology based on occupancy grid map merging,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 5, pp. 2089–2100, 2014

    work page 2014

  7. [7]

    Cooperative multi-vehicle localization using split covariance intersection filter,

    H. Li and F. Nashashibi, “Cooperative multi-vehicle localization using split covariance intersection filter,” IEEE Intelligent Transportation Systems Magazine, vol. 5, no. 2, pp. 33–44, 2013. 10

    work page 2013

  8. [8]

    LiDAR SLAM based multi-vehicle cooperative localization using iterated split CIF,

    S. Fang, H. Li, and M. Yang, “LiDAR SLAM based multi-vehicle cooperative localization using iterated split CIF,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 11, pp. 21 137–21 147, 2022

    work page 2022

Show all 47 references
  1. [9]

    V2VNet: Vehicle-to-vehicle communication for joint perception and prediction,

    T.-H. Wang, S. Manivasagam, M. Liang, B. Yang, W. Zeng, and R. Ur- tasun, “V2VNet: Vehicle-to-vehicle communication for joint perception and prediction,” in European Conference on Computer Vision. Springer, 2020, pp. 605–621

    2020

  2. [10]

    CoBEVT: Coop- erative bird’s eye view semantic segmentation with sparse transformers,

    R. Xu, Z. Tu, H. Xiang, W. Shao, B. Zhou, and J. Ma, “CoBEVT: Coop- erative bird’s eye view semantic segmentation with sparse transformers,” in Conference on Robot Learning . PMLR, 2023, pp. 989–1000

    2023

  3. [11]

    Vehicular communica- tions for ITS: Standardization and challenges,

    S. Zeadally, M. A. Javed, and E. B. Hamida, “Vehicular communica- tions for ITS: Standardization and challenges,” IEEE Communications Standards Magazine, vol. 4, no. 1, pp. 11–17, 2020

    2020

  4. [12]

    A cooperative localization-aided tracking algorithm for THz wireless systems,

    G. Stratidakis, A.-A. A. Boulogeorgos, and A. Alexiou, “A cooperative localization-aided tracking algorithm for THz wireless systems,” inIEEE Wireless Communications and Networking Conference , 2019, pp. 1–7

    2019

  5. [13]

    Cooperative perception and localization for cooperative driving,

    A. Miller, K. Rim, P. Chopra, P. Kelkar, and M. Likhachev, “Cooperative perception and localization for cooperative driving,” in IEEE Interna- tional Conference on Robotics and Automation , 2020, pp. 1256–1262

    2020

  6. [14]

    Vision-based cooperative simultaneous localization and tracking,

    C.-H. Chang, S.-C. Wang, and C.-C. Wang, “Vision-based cooperative simultaneous localization and tracking,” in IEEE International Confer- ence on Robotics and Automation , 2011, pp. 5191–5197

    2011

  7. [15]

    Exploiting moving objects: Multi-robot simultaneous localization and tracking,

    ——, “Exploiting moving objects: Multi-robot simultaneous localization and tracking,” IEEE Transactions on Automation Science and Engineer- ing, vol. 13, no. 2, pp. 810–827, 2015

    2015

  8. [16]

    Multi-vehicle cooperative simultaneous LiDAR SLAM and object tracking in dynamic environments,

    S. Fang and H. Li, “Multi-vehicle cooperative simultaneous LiDAR SLAM and object tracking in dynamic environments,” IEEE Transac- tions on Intelligent Transportation Systems , vol. 25, no. 9, pp. 11 411– 11 421, 2024

    2024

  9. [17]

    RDC- SLAM: A real-time distributed cooperative SLAM system based on 3D LiDAR,

    Y . Xie, Y . Zhang, L. Chen, H. Cheng, W. Tu, D. Cao, and Q. Li, “RDC- SLAM: A real-time distributed cooperative SLAM system based on 3D LiDAR,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 9, pp. 14 721–14 730, 2021

    2021

  10. [18]

    Where2comm: Communication-efficient collaborative perception via spatial confidence maps,

    Y . Hu, S. Fang, Z. Lei, Y . Zhong, and S. Chen, “Where2comm: Communication-efficient collaborative perception via spatial confidence maps,” Advances in Neural Information Processing Systems, vol. 35, pp. 4874–4886, 2022

    2022

  11. [19]

    Cooperative localization and multi-target tracking in agent networks with the sum- product algorithm,

    M. Brambilla, D. Gaglione, G. Soldi, R. Mendrzik, G. Ferri, K. D. LePage, M. Nicoli, P. Willett, P. Braca, and M. Z. Win, “Cooperative localization and multi-target tracking in agent networks with the sum- product algorithm,” IEEE Open Journal of Signal Processing , vol. 3, pp...

    2022

  12. [20]

    Survey on ranging sensors and cooperative tech- niques for relative positioning of vehicles,

    F. de Ponte M ¨uller, “Survey on ranging sensors and cooperative tech- niques for relative positioning of vehicles,” Sensors, vol. 17, no. 2, pp. 271–298, 2017

    2017

  13. [21]

    A collaborative relative localization method for vehicles using vision and LiDAR sensors,

    Y . Li and H. Li, “A collaborative relative localization method for vehicles using vision and LiDAR sensors,” in International Conference on Control, Automation, Robotics and Vision. IEEE, 2020, pp. 281–286

    2020

  14. [22]

    Recursive decentralized localization for multi-robot systems with asynchronous pairwise communication,

    L. Luft, T. Schubert, S. I. Roumeliotis, and W. Burgard, “Recursive decentralized localization for multi-robot systems with asynchronous pairwise communication,” The International Journal of Robotics Re- search, vol. 37, no. 10, pp. 1152–1167, 2018

    2018

  15. [23]

    Delight: An efficient descriptor for global localisation using LiDAR intensities,

    K. P. Cop, P. V . Borges, and R. Dub ´e, “Delight: An efficient descriptor for global localisation using LiDAR intensities,” in IEEE International Conference on Robotics and Automation , 2018, pp. 3653–3660

    2018

  16. [24]

    SegMap: Segment-based mapping and localization using data-driven descriptors,

    R. Dube, A. Cramariuc, D. Dugas, H. Sommer, M. Dymczyk, J. Nieto, R. Siegwart, and C. Cadena, “SegMap: Segment-based mapping and localization using data-driven descriptors,” The International Journal of Robotics Research, vol. 39, no. 2-3, pp. 339–355, 2020

    2020

  17. [25]

    Fast point feature histograms (FPFH) for 3D registration,

    R. B. Rusu, N. Blodow, and M. Beetz, “Fast point feature histograms (FPFH) for 3D registration,” in IEEE International Conference on Robotics and Automation , 2009, pp. 3212–3217

    2009

  18. [26]

    PointNetVLAD: Deep point cloud based retrieval for large-scale place recognition,

    M. A. Uy and G. H. Lee, “PointNetVLAD: Deep point cloud based retrieval for large-scale place recognition,” in IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 4470–4479

    2018

  19. [27]

    Netvlad: CNN architecture for weakly supervised place recognition,

    R. Arandjelovic, P. Gronat, A. Torii, T. Pajdla, and J. Sivic, “Netvlad: CNN architecture for weakly supervised place recognition,” in IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 5297–5307

    2016

  20. [28]

    Scan context: Egocentric spatial descriptor for place recognition within 3D point cloud map,

    G. Kim and A. Kim, “Scan context: Egocentric spatial descriptor for place recognition within 3D point cloud map,” inIEEE/RSJ International Conference on Intelligent Robots and Systems , 2018, pp. 4802–4809

    2018

  21. [29]

    Intensity scan context: Coding intensity and geometry relations for loop closure detection,

    H. Wang, C. Wang, and L. Xie, “Intensity scan context: Coding intensity and geometry relations for loop closure detection,” in IEEE International Conference on Robotics and Automation , 2020, pp. 2095–2101

    2020

  22. [30]

    Learning sequential descriptors for sequence-based visual place recognition,

    R. Mereu, G. Trivigno, G. Berton, C. Masone, and B. Caputo, “Learning sequential descriptors for sequence-based visual place recognition,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10 383–10 390, 2022

    2022

  23. [31]

    Learning sequence descriptor based on spatio-temporal attention for visual place recognition,

    J. Zhao, F. Zhang, Y . Cai, G. Tian, W. Mu, C. Ye, and T. Feng, “Learning sequence descriptor based on spatio-temporal attention for visual place recognition,” IEEE Robotics and Automation Letters , vol. 9, no. 3, pp. 2351–2358, 2024

    2024

  24. [32]

    Distinctive image features from scale-invariant keypoints,

    D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision , vol. 60, pp. 91–110, 2004

    2004

  25. [33]

    ORB: An efficient alternative to SIFT or SURF,

    E. Rublee, V . Rabaud, K. Konolige, and G. Bradski, “ORB: An efficient alternative to SIFT or SURF,” in International Conference on Computer Vision. IEEE, 2011, pp. 2564–2571

    2011

  26. [34]

    Superpoint: Self- supervised interest point detection and description,

    D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superpoint: Self- supervised interest point detection and description,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018, pp. 224– 236

    2018

  27. [35]

    Who2com: Collaborative perception via learnable handshake communi- cation,

    Y .-C. Liu, J. Tian, C.-Y . Ma, N. Glaser, C.-W. Kuo, and Z. Kira, “Who2com: Collaborative perception via learnable handshake communi- cation,” in IEEE International Conference on Robotics and Automation , 2020, pp. 6876–6883

    2020

  28. [36]

    When2com: Multi-agent per- ception via communication graph grouping,

    Y .-C. Liu, J. Tian, N. Glaser, and Z. Kira, “When2com: Multi-agent per- ception via communication graph grouping,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4106–4115

    2020

  29. [37]

    Learning distilled collaboration graph for multi-agent perception,

    Y . Li, S. Ren, P. Wu, S. Chen, C. Feng, and W. Zhang, “Learning distilled collaboration graph for multi-agent perception,” Advances in Neural Information Processing Systems , vol. 34, pp. 29 541–29 552, 2021

    2021

  30. [38]

    LeGO-LOAM: Lightweight and ground- optimized LiDAR odometry and mapping on variable terrain,

    T. Shan and B. Englot, “LeGO-LOAM: Lightweight and ground- optimized LiDAR odometry and mapping on variable terrain,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2018, pp. 4758–4765

    2018

  31. [39]

    Low-drift and real-time LiDAR odometry and mapping,

    J. Zhang and S. Singh, “Low-drift and real-time LiDAR odometry and mapping,” Autonomous Robots, vol. 41, no. 2, pp. 401–416, 2017

    2017

  32. [40]

    A review of LiDAR radiometric processing: From ad hoc intensity correction to rigorous radiometric calibration,

    A. G. Kashani, M. J. Olsen, C. E. Parrish, and N. Wilson, “A review of LiDAR radiometric processing: From ad hoc intensity correction to rigorous radiometric calibration,” Sensors, vol. 15, no. 11, pp. 28 099– 28 128, 2015

    2015

  33. [41]

    Superglue: Learning feature matching with graph neural networks,

    P.-E. Sarlin, D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superglue: Learning feature matching with graph neural networks,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4938–4947

    2020

  34. [42]

    OPV2V: An open benchmark dataset and fusion pipeline for perception with vehicle-to- vehicle communication,

    R. Xu, H. Xiang, X. Xia, X. Han, J. Li, and J. Ma, “OPV2V: An open benchmark dataset and fusion pipeline for perception with vehicle-to- vehicle communication,” in IEEE International Conference on Robotics and Automation, 2022, pp. 2583–2589

    2022

  35. [43]

    V2V4Real: A real-world large-scale dataset for vehicle-to-vehicle cooperative perception,

    R. Xu, X. Xia, J. Li, H. Li, S. Zhang, Z. Tu, Z. Meng, H. Xiang, X. Dong, R. Song et al. , “V2V4Real: A real-world large-scale dataset for vehicle-to-vehicle cooperative perception,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 712–13 722

    2023

  36. [44]

    Pointpillars: Fast encoders for object detection from point clouds,

    A. H. Lang, S. V ora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 697–12 705

    2019

  37. [45]

    Asynchronous state estimation of simultaneous ego-motion estimation and multiple object tracking for LiDAR-inertial odometry,

    Y .-K. Lin, W.-C. Lin, and C.-C. Wang, “Asynchronous state estimation of simultaneous ego-motion estimation and multiple object tracking for LiDAR-inertial odometry,” in IEEE International Conference on Robotics and Automation , 2023, pp. 10 616–10 622

    2023

  38. [46]

    Factor graph based 3D multi- object tracking in point clouds,

    J. P ¨oschmann, T. Pfeifer, and P. Protzel, “Factor graph based 3D multi- object tracking in point clouds,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2020, pp. 10 343–10 350

    2020

  39. [47]

    Adaptive cubature split covariance inter- section filter for multi-vehicle cooperative localization,

    S. Fang, H. Li, and M. Yang, “Adaptive cubature split covariance inter- section filter for multi-vehicle cooperative localization,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 1158–1165, 2021. 11 Susu Fang , currently a Ph.D. candidate at the Department of Automa...

    2021

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.