Degradation-Aware Cooperative Multi-Modal GNSS-Denied Localization Leveraging LiDAR-Based Robot Detections

Martin Saska; Petr \v{S}t\v{e}p\'an; Tomi Westerlund; V\'aclav Pritzl; Xianjia Yu

REVIEW 3 major objections 5 minor 44 references

Degradation-Aware Cooperative Multi-Modal GNSS-Denied Localization Leveraging LiDAR-Based Robot Detections

T0 review · 3 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read This paper claims that a team of robots with complementary sensors can stay accurately localized without GNSS by fusing LiDAR and camera odometry with inter-robot detections, reweighting each sensor by its current reliability.

desk verdict Useful engineering with real experimental gains, but the Jacobian in Eq. (22a) is wrong and the evaluation is thinner than the abstract suggests. read the letter →

arxiv 2510.20480 v2 pith:QYLITOTN submitted 2025-10-23 cs.RO

Václav Pritzl , Xianjia Yu , Tomi Westerlund , Petr Štěpán , Martin Saska This is my paper · ORCID

classification cs.RO

keywords cooperativelocalizationmulti-robotfactorgraphLiDAR-inertialodometryvisual-inertialWassersteindistancesensordegradationGNSS-denied

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

The paper tries to show that a heterogeneous robot team—one robot with a LiDAR, others with cameras—can sustain accurate localization in GNSS-denied environments even when one robot's sensor is failing, as long as the robots can see each other. It fuses each robot's own odometry (LiDAR-inertial for the detecting robot, visual-inertial for the detected robots) with 3D detections of teammates in a sliding-window factor graph. The adaptive core is to detect when the LiDAR odometry is degenerate by inspecting the scan-matching Hessian, and to weight visual-inertial odometry by the Wasserstein distance between consecutive filter covariance matrices, which the paper finds correlates with real relative-position error. On real UGV-UAV and UAV-only datasets, this reduces absolute trajectory error dramatically in degraded scenes—for example, from 74.6 m to 10.4 m for a LiDAR-equipped UAV in an open field, and from 4.7 m to 0.1 m for a camera UAV under artificially blacked-out images. The paper also provides an observability analysis that identifies exactly which directions remain unobservable under LiDAR or visual-inertial degradation, and it confirms those predictions experimentally.

What carries the argument

The paper is carried by three mechanisms working inside a sliding-window factor graph. First, an interpolation-based quaternary detection factor connects two temporally adjacent poses of the detecting robot and two of the detected robot, using constant-velocity interpolation on the SE(3) manifold to fuse a 3D detection made at an arbitrary timestamp. Second, LiDAR odometry reliability is binarized by thresholding the minimum eigenvalue of the approximate scan-matching Hessian; when it falls below a threshold, the LiDAR relative-pose covariance is inflated so the graph stops trusting it. Third, the relative-pose covariance of the Kalman-filter visual-inertial odometry is set proportional to t

What would settle it

Measure, with motion-capture ground truth, the relative position error of a Kalman-filter VIO alongside the 2-Wasserstein distance between its consecutive output covariances across a scene with fluctuating visual texture. If the two stop tracking each other (the paper's own reported correlations range from 0.221 to 0.807), the adaptive weight will over-trust or under-trust the VIO, and the claimed rescue of a degraded camera robot fails; this is directly testable in a new deployment.

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Extended reading notes

Core claim

The central claim is that a loosely-coupled, degradation-aware factor-graph fusion of LiDAR-inertial odometry, visual-inertial odometry, and 3D inter-robot detections lets a team of robots with complementary sensors localize jointly in a shared frame more accurately than any single odometry source when conditions degrade. The method's novel components are an interpolation-based quaternary detection factor that handles asynchronous measurements, a Hessian-eigenvalue test that switches the LiDAR robot between trusted and untrusted modes, and a Wasserstein-distance-based weighting of visual-inertial relative poses that rises and falls with the estimated uncertainty of the filter. The theoretica

Load-bearing premise

The method's visual-inertial adaptivity depends on the assumption that the 2-Wasserstein distance between consecutive VIO output covariances grows in proportion to the actual relative-position error, with one per-robot scale constant fitted from ground truth; if that proportionality breaks in a new environment, the cooperative corrections that should rescue a degrading camera robot become unreliable.

Editorial extensions

If this is right

Under normal operation, the factor graph roughly preserves the better of the two odometries; the detected camera robot's 3D absolute trajectory error drops from about half a meter to around a tenth of a meter indoors.
Under visual-inertial degradation (blacked-out images), the camera robot's 3D error drops from 4.671 m to 0.101 m because the Wasserstein-based weighting downweights the drifting odometry and lets detections anchored by the LiDAR robot take over.
Under LiDAR degradation in a large open field, the LiDAR robot's 3D error drops from 74.591 m to 10.373 m and its yaw error from 1.219 rad to 0.360 rad using a single detected camera robot.
If the LiDAR robot is degraded and only one teammate is detected, one degree of freedom (yaw plus perpendicular translation) stays unobservable and the estimate can drift; detecting two teammates makes the problem fully observable and removes the drift.
The detected camera robot's yaw orientation cannot be corrected by cooperative detections alone, so correcting it requires an extra source of yaw information or the camera robot detecting other robots itself.

Reading between the lines

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

The Wasserstein-distance weighting is a general reliability signal for Kalman-filter odometry: it could improve single-robot multi-sensor fusion just as well, because it sidesteps the unknown cross-covariance problem without modifying the odometry filter.
The observability analysis implies an active-control rule the authors leave implicit: during LiDAR degradation, the team should deliberately change the relative bearing between robots, since the unobservable direction lies perpendicular to the robot-robot line and rotates as that line rotates.
The per-robot scale factor is fitted from ground-truth error, so the method is not parameter-free; estimating that constant online from detection-consistency residuals would be a natural extension and would make the adaptivity self-tuning.
Since the detections provide no orientation, the detected robot's yaw can remain unobservable indefinitely; a detection that resolves two points on the teammate, such as a marker pair, would close this gap and is testable with the same factor graph.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this is a genuinely useful engineering paper on cooperative localization for heterogeneous teams, and the improvements it reports are real and large. But there is a concrete math error in the central interpolation factor, and the validation has enough soft spots that the claims need to be scaled back or re-supported.

What is new and good: the interpolation-based quaternary factor for asynchronous detections is a sensible idea, and the Wasserstein-distance weighting for VIO covariances is a clever, practical heuristic. The observability analysis is a legitimate contribution: it is a rank/nullspace calculation, not circular, and the two experiments that confirm the predicted unobservable directions are convincing. The overall system — LIO on one robot, VIO on another, LiDAR detections in between — addresses a real gap in heterogeneous multi-robot deployment.

Now the problems. First, I checked Eq. (22a). Under the paper's stated right-perturbation convention, the derivative of e = [(T_W^{Xint})^{-1} T_W^{Yint}]_tr with respect to a perturbation of T_W^{Xint} should be [e]_x, -I3. The paper writes [e]_x R_Xint, -I3. The extra R is not a convention artifact; it fails a numerical derivative test for non-identity rotations. So the paper, as written, does not specify the Jacobian that would make the factor graph minimize the cost it defines. The authors may have implemented it correctly in code, but the manuscript does not contain the correct specification of the main novel factor.

Second, the evaluation. Each experiment is a single run with no error bars, and several parameters (mu, lambda_thr, covariance bounds, OpenVINS settings) are fit to ground truth, likely on the same data. The ATE is computed after trajectory alignment, which removes global yaw and position drift, so the headline numbers overstate absolute localization accuracy. There is no quantitative comparison against a static-weight baseline, only a mention that the static covariance failed in one case. The Wasserstein correlation table shows the heuristic is not universally strong — 0.221 in one indoor set, 0.807 in the best outdoor set — which makes the fitted mu a real concern.

Who this is for: researchers working on multi-robot SLAM and GNSS-denied operation, especially teams with mixed LiDAR and camera platforms. They will find the system design and the observability analysis useful once the Jacobian is corrected.

Recommendation: this deserves serious peer review — not a desk reject — but I would ask for major revision: fix Eq. (22a), rerun with held-out parameters and repeated trials, report errors without alignment or with clearly stated alignment, and add a static-weights baseline.

Referee Report

3 major / 5 minor

Summary. The paper proposes a loosely-coupled factor-graph framework for cooperative localization of a LiDAR-inertial robot X and one or more VIO-equipped robots Y,Z, using 3D LiDAR inter-robot detections. Relative LIO/VIO pose factors are adaptively weighted: LIO degeneracy is detected from the minimum eigenvalue of the scan-matching approximate Hessian, and VIO relative-pose uncertainty is set proportional to the 2-Wasserstein distance between consecutive VIO covariance matrices. A novel quaternary factor interpolates robot poses on SE(3) to fuse asynchronous detections. An observability analysis of a simplified two-pose graph identifies unobservable directions under LIO and VIO degradation. Real-world UGV-UAV and multi-UAV experiments report large ATE reductions (e.g., Outdoor #1 X: 74.591 m to 10.373 m; Indoor #2 Y: 4.671 m to 0.101 m), and two targeted experiments qualitatively confirm the predicted unobservable behavior.

Significance. If the method is correctly specified, the contribution is practically significant: it demonstrates a low-bandwidth, heterogeneous, asynchronous multi-robot localization architecture with explicit degradation awareness, and it provides testable observability predictions that are checked on real data. The paper's strengths are the real-world experimental corpus, the direct experimental tests of the theoretically predicted unobservable directions (Sec. IV-D), and the fact that the observability analysis is a concrete rank/nullspace calculation rather than a heuristic claim. However, the manuscript currently contains a load-bearing Jacobian error in the main novel factor, and the Wasserstein weighting is validated only in-sample with modest correlation in one dataset. Both issues must be addressed before the central claims can be accepted.

major comments (3)

[Sec. II-D, Eq. (22a)] Under the right-perturbation convention stated in Eq. (2), for e = [(T_W_Xint)^-1 T_W_Yint]_tr = R_Xint^T (t_Yint - t_Xint), a perturbation T_W_Xint <- T_W_Xint Exp([φ; δt]) gives to first order e -> e + [e]_× φ - δt. Therefore ∂e/∂ξ_Xint = [[e]_×, -I3]. Eq. (22a) instead writes [[R_Xint^T Δt]_× R_Xint, -I3]. The extra R_Xint is not a convention artifact; it fails a finite-difference test whenever R_Xint is not the identity. Since this factor is the paper's main novel fusion element, and since the same expression enters the observability Jacobians (e.g., Eq. (30c) and analogous entries), the manuscript as written does not specify a factor graph that minimizes Eq. (1). Please correct Eq. (22a), re-derive or re-check the Sec. III Jacobians, and provide a numerical-derivative verification of the corrected expressions.
[Sec. II-C, Table IV] The Wasserstein-scaling factor μ is fitted per robot to ground-truth average error (Table II: μY=260, μZ=500), and Sec. IV-C then reports Pearson correlations between the Wasserstein distance and the relative position error on the same data used to fit/select the scaling and bounds. The correlations range from 0.221 (Indoor #1) to 0.807 (Outdoor #1), so the claim that the Wasserstein distance 'highly correlates with the real-world localization error' (Sec. IV-E) is only partially supported and is in-sample. No ablation is reported with a static VIO covariance or with μ chosen on a training split. Please add a held-out or cross-validation analysis, and quantify the ATE benefit of the Wasserstein weighting compared with a fixed-covariance baseline.
[Table III] Each experimental condition appears to be a single run with no error bars, repeated trials, or statistical tests. The ATE reductions are large and qualitatively convincing, but the word 'significant' in the abstract and conclusion is not supported by repeated measurements. Please add multiple runs for the key degradation experiments (or at least state explicitly that the reported numbers are single-run examples), and report mean/standard deviation or per-run values.

minor comments (5)

[Sec. IV-B, Outdoor #4] The text says the yaw error was reduced 'from 1.668 rad to 0.067 m'; the unit for a rotational ATE should be radians, not meters.
[Table II] The caption says 'all the presented results were obtained with the same set of parameters,' but the paper later states that OpenVINS parameters were not the same across datasets. Please clarify which parameters are fixed and which are dataset-dependent.
[Sec. IV-C] The statement that 'utilizing a static covariance matrix ... resulted in loss of the estimate' is anecdotal. If this comparison exists, please report the quantitative ATE/association failure for the static-covariance baseline.
[Sec. II-D, Eq. (23)] The notation Aint in Eq. (23a) is used without a formal definition of A. Define it explicitly (e.g., A ∈ {X,Y}) to avoid confusion with the skew-symmetric matrix notation.
[Sec. II-C, Eq. (11)] The phrase 'assumes maximal correlation between the two covariance matrices' is imprecise for the Wasserstein distance. The optimal transport coupling is not the same as maximal correlation; consider rephrasing or citing the closed form more carefully.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity; the central factor-graph derivation and observability analysis are self-contained, with only in-sample calibration caveats.

full rationale

The central derivation chain is not circular. The cooperative localization cost (Eq. 1), the relative pose factors (Eqs. 3, 8), the interpolation-based detection factor (Eqs. 18-19), and the observability rank/nullspace analysis (Sec. III, Eqs. 29-39) are self-contained computations from stated measurement models and the right-perturbation convention; they do not import the experimental ATE numbers as inputs. The Wasserstein weighting is explicitly introduced as an assumption ("We assume that the error of the relative pose measurement is proportional to the Wasserstein distance", Sec. II-C), with the scale factor μ fitted to real-world ground truth. The subsequent Pearson correlations (Table IV) are computed on the same underlying data, so this is an in-sample validation and a generalization/overfitting caveat, not a by-construction reduction of a prediction to a fitted parameter. Self-citations ([17], [30], [42], [44]) support components such as the detector, platform, and related work, and are not load-bearing for the central claim. Separately, Eq. (22a) appears inconsistent with the stated right-perturbation convention and with the error in Eq. (18); this is a correctness risk that should be corrected, but an incorrect Jacobian is not circularity. Overall, the paper does not derive its claimed improvements from the same quantities it fits.

Assumptions & free parameters 10 free parameters · 8 assumptions · 0 invented entities

The method introduces no new physical entities, forces, or conserved quantities. All tunable scalars (covariance bounds, Wasserstein scales, degeneracy threshold) are fitted to the authors' real-world data, and the central modeling assumptions are the gravity-aligned-frame prior, the Kalman-filter covariance interpretation, and the constant-velocity interpolation used by the detection factor.

free parameters (10)

λ_thr (LIO degeneracy threshold) = 430
Empirically tuned for the Ouster OS0-128 LiDAR; used in Eq. (4) to classify LIO outputs as reliable/degraded.
σ_det (detection noise) = 0.13 m
Empirically tuned to reflect LiDAR detection noise and constant-velocity interpolation error; used in Eq. (25).
µY, µZ (Wasserstein scaling factors) = µY=260, µZ=500
Selected by analyzing average VIO error vs ground truth; maps Wasserstein distance to relative-pose standard deviation in Eq. (13).
loσpos, hiσpos, loσγ, hiσγ = 0.01 m, 5.0 m, 0.001 rad, 1.0 rad
Covariance bounds for LIO relative factors in Eqs. (5)-(6); selected based on average LIO error w.r.t. ground truth.
vminσpos, vmaxσpos = 0.1 m, 5.0 m
Bounds for VIO positional standard deviation in Eq. (14); empirically selected.
ν (yaw inflation factor) = 20
Predefined scaling factor inflating VIO yaw sigma when positional sigma exceeds vmaxσpos; Eq. (16).
vσγ (VIO yaw sigma parameter) = 0.01 rad
Empirically selected parameter for VIO yaw standard deviation in Eq. (16).
LIO-SAM degeneracy internal threshold = not reported
Internal threshold of LIO-SAM for scan matching degeneracy 'empirically tuned' (Sec IV).
OpenVINS parameters = different per dataset
Tuned per camera/platform; exact values not reported (Sec IV).
prior covariance for T^W_Y0 = not reported
Empirically selected to be larger than the anchor prior; Sec II-E.

assumptions (8)

domain assumption All reference frames are gravity-aligned; roll and pitch are constrained to zero via prior factors.
Based on IMUs on each robot; required for the tilt prior in Figs. 3-4 and Eq. (1).
domain assumption Robot clocks are synchronized over the network (NTP/chrony).
Required for the asynchronous factor timestamps in Sec I-A and II-D.
domain assumption LIO scan-matching degeneracy is detectable by thresholding the minimum eigenvalue of the approximate Hessian A^T A.
Relies on [14]; used in Eq. (4) to switch between LIO covariance matrices.
ad hoc to paper The VIO is a Kalman filter whose output covariance matrices are meaningful, and the error of a VIO relative pose is proportional to the 2-Wasserstein distance between consecutive covariance matrices.
This proportional model is the paper's main modeling assumption (Sec II-C); it is validated only by correlation with ground-truth error on the same datasets.
domain assumption Constant velocity between consecutive estimated poses for detection-factor interpolation.
Used in Eqs. (18)-(19); error is folded into the constant detection noise σ_det.
domain assumption Inter-robot detections are Gaussian 3D positions, and nearest-neighbor association with a fixed threshold resolves anonymity and false positives.
Sec II-D; real detector uses reflective markers and LiDAR clustering.
domain assumption The simplified observability graph (two poses per robot, synchronized detections, zero roll/pitch) represents the real problem's observability.
Sec III states the simplification; results are experimentally spot-checked but not proven for the full asynchronous, interpolation-based graph.
domain assumption Trajectory alignment to ground truth via Kabsch-Umeyama is an appropriate evaluation for GNSS-denied localization accuracy.
Sec IV uses this standard evaluation; it removes global yaw/translation error, so ATE is relative trajectory error.

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Cite this review

Pith. "Pith review of Degradation-Aware Cooperative Multi-Modal GNSS-Denied Localization Leveraging LiDAR-Based Robot Detections." pith.science (2026). https://pith.science/paper/QYLITOTN

@misc{pith2026251020480,
  author       = {Pith},
  title        = {Pith review of: Degradation-Aware Cooperative Multi-Modal GNSS-Denied Localization Leveraging LiDAR-Based Robot Detections},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QYLITOTN}},
  note         = {Machine review of arXiv:2510.20480}
}

read the original abstract

Accurate long-term localization using onboard sensors is crucial for robots operating in Global Navigation Satellite System (GNSS)-denied environments. While complementary sensors mitigate individual degradations, carrying all the available sensor types on a single robot significantly increases the size, weight, and power demands. Distributing sensors across multiple robots enhances the deployability but introduces challenges in fusing asynchronous, multi-modal data from independently moving platforms. We propose a novel adaptive multi-modal multi-robot cooperative localization approach using a factor-graph formulation to fuse asynchronous Visual-Inertial Odometry (VIO), LiDAR-Inertial Odometry (LIO), and 3D inter-robot detections from distinct robots in a loosely-coupled fashion. The approach adapts to changing conditions, leveraging reliable data to assist robots affected by sensory degradations. A novel interpolation-based factor enables fusion of the unsynchronized measurements. LIO degradations are evaluated based on the approximate scan-matching Hessian. A novel approach of weighting odometry data proportionally to the Wasserstein distance between the consecutive VIO outputs is proposed. A theoretical analysis is provided, investigating the cooperative localization problem under various conditions, mainly in the presence of sensory degradations. The proposed method has been extensively evaluated on real-world data gathered with heterogeneous teams of an Unmanned Ground Vehicle (UGV) and Unmanned Aerial Vehicles (UAVs), showing that the approach provides significant improvements in localization accuracy in the presence of various sensory degradations.

Figures

Figures reproduced from arXiv: 2510.20480 by the authors.

**Figure 2.** Robot X is localized in local frame L using a LIO algorithm. Robot Y is localized in a local frame V using a VIO algorithm. Robot X detects the relative 3D position of robot Y . Robot Z represents another VIOutilizing robot detected by robot X. W denotes the world reference frame of the cooperative localization algorithm. All the reference frames are gravityaligned. Blue dashed lines represent the estimated variab… view at source ↗

**Figure 3.** Factor graph representation of the cooperative localization problem [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (8 more)

**Figure 4.** Figure 4: Simplified factor graph used in the observability analysis of the [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: xy-plots of odometry data (LIO, VIO), the proposed cooperative localization method (COOP), and ground truth (GT) from the experimental evaluation. All estimated trajectories were aligned to the ground-truth data for evaluation. based on the DJI F330 frame, equipped wit…

**Figure 6.** Figure 6: Altitude and yaw plots of odometry data (LIO, VIO), the proposed cooperative localization method (COOP), and ground-truth data (GT). [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

**Figure 7.** Figure 7: Continuation of the altitude and yaw plots of odometry data (LIO, VIO), the proposed cooperative localization method (COOP), and ground truth [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]

**Figure 8.** Figure 8: Ground-truth trajectories from the Outdoor #1 experiment, where the two UAVs flew around an open field. Table IV PEARSON CORRELATION COEFFICIENTS OF WASSERSTEIN DISTANCES AND THE NORMS OF RELATIVE 3D POSITION ERROR OF THE VIO. THE VALUES FOR OUTDOOR #4 ARE EQUAL TO THE…

**Figure 9.** Figure 9: Comparison of the LIO relative position error, VIO relative position [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]

**Figure 10.** Figure 10: The effect of the unobservable direction during LIO degradation. The left plot shows the situation with one detected UAV and the unconstrained [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]

**Figure 11.** Figure 11: The effect of unobservable yaw of the detected UAV [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]

Discussion (0). Continue with ORCID to comment.

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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 With- out Explicit Communication,”IEEE Robotics and Automation Letters, vol. 9, no. 6, pp. 5284–5291, Jun. 2024

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Characterizing the Uncertainty of Jointly Distributed Poses in the Lie Algebra,

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DCL-SLAM: A Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm,

S. Zhong, Y . Qi, Z. Chen, J. Wu, H. Chen, and M. Liu, “DCL-SLAM: A Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm,”IEEE Sensors Journal, vol. 24, no. 4, pp. 4786–4797, Feb. 2024

2024
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DiSCo-SLAM: Distributed Scan Context-Enabled Multi-Robot LiDAR SLAM With Two-Stage Global-Local Graph Optimization,

Y . Huang, T. Shan, F. Chen, and B. Englot, “DiSCo-SLAM: Distributed Scan Context-Enabled Multi-Robot LiDAR SLAM With Two-Stage Global-Local Graph Optimization,”IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 1150–1157, Apr. 2022

2022
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LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments,

Y . Chang, K. Ebadi, C. E. Denniston, M. F. Ginting, A. Rosinol, A. Reinke, M. Palieri, J. Shi, A. Chatterjee, B. Morrell, A.-a. Agha- mohammadi, and L. Carlone, “LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments,”IEEE...

2022
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CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation for Robotic Swarms,

S. Zhong, H. Chen, Y . Qi, D. Feng, Z. Chen, J. Wu, W. Wen, and M. Liu, “CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation for Robotic Swarms,” in2024 IEEE International Conference on Robotics and Automation (ICRA), May 2024, pp. 3920–3926

2024
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COVINS: Visual-Inertial SLAM for Centralized Collaboration,

P. Schmuck, T. Ziegler, M. Karrer, J. Perraudin, and M. Chli, “COVINS: Visual-Inertial SLAM for Centralized Collaboration,” in2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), Oct. 2021, pp. 171–176

2021
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Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems,

Y . Tian, Y . Chang, F. Herrera Arias, C. Nieto-Granda, J. P. How, and L. Carlone, “Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems,”IEEE Transactions on Robotics, vol. 38, no. 4, pp. 2022–2038, Aug. 2022

2022
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DVM-SLAM: Decentralized Visual Monocular Simultaneous Localization and Mapping for Multi- Agent Systems,

J. Bird, J. Blumenkamp, and A. Prorok, “DVM-SLAM: Decentralized Visual Monocular Simultaneous Localization and Mapping for Multi- Agent Systems,” Mar. 2025, arXiv:2503.04126 [cs]

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Omni-Swarm: A Decentralized Omnidirectional Visual–Inertial–UWB State Estimation System for Aerial Swarms,

H. Xu, Y . Zhang, B. Zhou, L. Wang, X. Yao, G. Meng, and S. Shen, “Omni-Swarm: A Decentralized Omnidirectional Visual–Inertial–UWB State Estimation System for Aerial Swarms,”IEEE Transactions on Robotics, vol. 38, no. 6, pp. 3374–3394, 2022

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Ground and Aerial Collaborative Mapping in Urban Environments,

J. He, Y . Zhou, L. Huang, Y . Kong, and H. Cheng, “Ground and Aerial Collaborative Mapping in Urban Environments,”IEEE Robotics and Automation Letters, vol. 6, no. 1, pp. 95–102, Jan. 2021

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Swarm-SLAM: Sparse Decentralized Collaborative Simultaneous Localization and Mapping Framework for Multi-Robot Systems,

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Swarm-LIO2: Decentralized Efficient LiDAR- Inertial Odometry for Aerial Swarm Systems,

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Fusion of Visual-Inertial Odometry with LiDAR Relative Localization for Cooperative Guidance of a Micro-Scale Aerial Vehicle,

V . Pritzl, M. Vrba, P. ˇStˇep´an, and M. Saska, “Fusion of Visual-Inertial Odometry with LiDAR Relative Localization for Cooperative Guidance of a Micro-Scale Aerial Vehicle,” 2024, arXiv:2306.17544 [cs]

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Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.

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LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping,

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R 33LIVE++: A Robust, Real-Time, Radiance Reconstruction Package With a Tightly-Coupled LiDAR-Inertial-Visual State Estimator,

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2025