Pith. sign in

REVIEW 4 major objections 3 minor 47 references

CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments

T0 review · 4 major / 3 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read A chain of complementary pose estimators keeps robots localized in dark underground environments.

desk verdict CompSLAM is a field-proven systems paper whose real value is the public SubT dataset; the resilience claims outrun a single-run evaluation that never stresses the leg-odometry fallback. read the letter →

arxiv 2505.06483 v1 pith:DS5K7P52 submitted 2025-05-10 cs.RO cs.CV

Shehryar Khattak , Timon Homberger , Lukas Bernreiter , Julian Nubert , Olov Andersson , Roland Siegwart , Kostas Alexis , Marco Hutter This is my paper · ORCID
classification cs.ROcs.CV
keywords multi-modalSLAMundergroundroboticssensorfusionlidar-inertialodometryvisual-thermal-inertialleggedrobotsDARPASubTfinalsdatasetperceptiondegradation
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

This paper claims that an underground robot can be kept reliably localized and mapping by arranging several independent pose estimators in a hierarchy, so that each estimator uses the previous one's output and can be bypassed when its sensor inputs degrade. The authors present CompSLAM as that system and support the claim with its deployment on all Team Cerberus robots during the winning final run of the DARPA Subterranean Challenge and with a quantitative evaluation on a newly released 740-meter dataset collected by a quadruped in the same course. The load-bearing design premise is that environmental conditions rarely degrade every sensor modality at the same time, making redundancy the primary safety mechanism. If the claim is correct, the released code and dataset give the robotics community a reference implementation and a demanding benchmark for perception-degraded subterranean operation.

What carries the argument

The carrying mechanism is the fallback chain: a hierarchy of estimators in which each stage validates the quality of its input, produces a pose estimate, and passes that estimate to the next stage as a prior. The visual-thermal-inertial stage uses an iterated extended Kalman filter with visual and 16-bit thermal features and optional LiDAR-initialized inverse depth; the LiDAR-inertial stage performs feature-based scan-to-scan and scan-to-submap registration; and external kinematic estimates are inserted as prioritized priors. The degeneracy checks—image texture quality, D-optimality growth in the filter covariance, and eigenvalue thresholds on the registration Hessian—decide when a stage is bypassed, which is what converts redundant sensors into a graceful degradation chain.

What would settle it

Run CompSLAM in a controlled underground course engineered to degrade every perception channel simultaneously—unlit featureless walls, dense artificial dust, a geometrically self-similar tunnel, and rough terrain causing motion blur and IMU saturation—and compare the estimated trajectory against surveyed ground truth. If the pose error grows without bound or the map splits where the leg-odometry fallback drifts, the central resilience claim is falsified; a weaker version would blank out overlapping LiDAR and camera intervals in the released dataset and measure how long the reported 1.794% per 25 m accuracy is preserved.

Watch

Extended reading notes

Core claim

The paper's core claim is that resilience in underground SLAM comes from complementary redundancy rather than from a single tightly coupled estimator. CompSLAM links a visual-thermal-inertial odometry module, a LiDAR-inertial odometry module with scan-to-scan and scan-to-submap stages, and external kinematic estimates from the robot's legs into a coarse-to-fine chain, where each stage hands its pose estimate downstream as an initial guess and can be skipped if it detects degeneration. Degeneration is detected through image-quality gating, D-optimality covariance monitoring, and eigenvalue analysis of the registration Hessian. On the released DARPA SubT finals dataset, the paper reports an Average Wasserstein Distance of 0.221 m at 1 m voxel resolution, a Spatial Consistency Score of 0.491, and a relative translation error of 1.794% over 25 m trajectory segments, while the robot traversed 740 m in 35 minutes through self-similar tunnels, caves, and warehouse sections with missing LiDAR frames, motion blur, IMU saturation, and moving people present.

Load-bearing premise

The framework's resilience rests on the premise that environmental conditions will not degrade all sensor inputs at the same time; if darkness, dust, self-similar geometry, and motion blur arrive together, the final fallback is leg odometry, whose behavior under that combined stress is assumed rather than demonstrated by the evaluation.

Editorial extensions

If this is right

  • If one sensor modality fails, the framework can continue delivering pose and map updates by using the previous valid estimate as a prior for the next stage, instead of stopping.
  • Because the modules are replaceable, learning-based components such as a learned scan-to-scan registration and a localizability classifier can be swapped into the LiDAR odometry stage without rebuilding the system.
  • The released dataset, with synchronized LiDAR, grayscale, RGB, IMU, and leg-odometry data spanning tunnels, caves, and warehouse areas, gives the community a common testbed for individual degradations including motion blur and dynamic people.
  • The same architecture has been extended for inter-robot map sharing, marsupial ground-air deployments, and collaborative multi-robot mapping, indicating the fallback chain is reusable beyond the competition course.

Reading between the lines

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

  • Beyond the paper, the ordered fallback design could be applied generally to autonomy stacks: rank independent estimators by cost and reliability, pass priors downstream, and let each stage validate its own input, rather than building one monolithic estimator.
  • Beyond the paper, because CompSLAM does not filter moving people, the released dataset can be used to measure how much dynamic-object filtering would improve map accuracy by comparing the published map against one rebuilt from static structures only.
  • Beyond the paper, the fact that the D-optimality reinitialization was never triggered during the finals suggests the quality thresholds are conservative; a systematic sweep of those thresholds on the released dataset could reveal earlier detection of sensor degradation.
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 / 3 minor

Summary. CompSLAM is a hierarchical multi-modal SLAM framework that fuses visual, thermal, LiDAR, inertial, and leg-kinematic measurements through three stages: an IEKF-based Visual-Thermal-Inertial Odometry module (Section III.B), a LiDAR-Inertial Odometry module with a LOAM-style scan-to-scan front-end and scan-to-submap mapping (Section III.C), and external fallback pose handling. The paper claims real-time, robust onboard pose estimation in degraded underground environments, supported by deployment on all Team Cerberus robots in the DARPA SubT winning final run and by an evaluation on a new 740 m ANYmal dataset from the finals course. The dataset release and open-source code are presented as contributions.

Significance. Verified hardware deployment in a DARPA-winning team is meaningful evidence of practical utility. If the robustness claims held, the work would be a useful reference architecture for complementary multi-modal sensor fusion in subterranean robotics, and the dataset would be a valuable benchmark for degraded underground perception. The manuscript is unusually open about its limitations (e.g., no dynamic-object filtering, D-optimality re-initialization not triggered in the final event), and it ships machine-checkable artifacts: public code and public dataset. However, the quantitative evaluation is too thin to sustain the full resilience claim.

major comments (4)
  1. [Section IV.B, Tables II–IV] The trajectory reference is an offline ICP registration of LiDAR point clouds with the dense ground-truth map. Because CompSLAM is itself LiDAR-based, this is a same-modality comparison that can mask drift, especially in the self-similar tunnel sections where LiDAR degeneracy is claimed. The map metrics in Tables II–III are also computed after dense ICP alignment of S_C to S_GT, which absorbs global misalignment. Please provide an independent reference (e.g., surveyed markers, visual/thermal structure-from-motion, or at least a comparison against a non-LiDAR trajectory estimate) and report absolute trajectory error without the alignment step.
  2. [Section IV.B and IV.A] The evaluation is a single teleoperated run with no baseline SLAM system, no ablation of the modules, and no repeated trials. The core claim (Section III.A) is that resilience holds when sensor inputs degrade, but Tables II–IV give one aggregate snapshot. Please add controlled ablations: disabling VTIO, disabling the leg-odometry fallback, disabling degeneracy detection; and report per-segment statistics on the sections where LiDAR degeneracy, darkness, IMU saturation, and motion blur actually occur. The competition deployment is not a substitute for this controlled evidence.
  3. [Section III.C.a and III.C.d] The quality checks on the leg-odometry fallback only verify that relative motion lies within controller bounds and that the LM's consistency check passes; neither detects slow drift or slip-induced bias in kinematic odometry, the likely failure mode on uneven or slippery terrain. Since the D-optimality re-initialization in III.B.e re-anchors VTIO to the broader CompSLAM pose, any drift entering through the fallback propagates. The paper never reports a segment in which LiDAR degeneracy actually fired and leg odometry was the active source, so the redundancy premise of Section III.A remains untested under exactly the combined degradation it is meant to handle.
  4. [Section III.B.e and IV] The paper notes that the D-optimality pose quality check 'was not triggered during the actual DARPA SubT Finals event' and the released dataset evaluation reports no activation statistics. As a safety mechanism central to the resilience story, its behavior should be demonstrated on the released sequence (or another) where the filter is actually degraded.
minor comments (3)
  1. [Figure 2 caption] The caption appears garbled: 'the different modules and passing of the estimated odometry as the initial guess of the next module.' Please rephrase.
  2. [Table I] The entries '3× Camera Grayscale 20' and '4× Camera RGB 0.5' are ambiguous; specify the number of cameras, resolution, and whether the listed frequency is per camera.
  3. [Section IV.B] The sentence 'segmenting the first tunnel, including the three-way junction from S_C and S_GT' is missing a comma or connecting word; please clarify whether both point clouds are segmented.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; same-modality LiDAR evaluation is a benchmark limitation, not a construction of the result.

full rationale

CompSLAM is an engineering architecture paper, not a mathematical derivation: its central claims are empirical performance claims backed by a competition deployment and a released dataset. I found no step in which a prediction is equivalent to an input by construction. The VTIO module builds on ROVIO's IEKF formulation, the LIO module builds on LOAM-style feature registration and on Zhang et al.'s degeneracy analysis, and GTSAM provides the factor graph machinery; these are external, independently developed methods rather than self-citations doing load-bearing work. No parameter is fitted to the released benchmark, and no reported metric is fed back into the estimator. The closest candidate is the evaluation design: the map error is computed after dense ICP alignment of the estimated map to the DARPA ground-truth map, and the trajectory reference is generated by offline LiDAR-ICP registration against that ground truth. This shares the LiDAR modality with CompSLAM's LIO and could mask common-mode LiDAR degeneracy, but it is not circular in the derivation-chain sense. The rigid ICP transform is an evaluation alignment, not an estimated state or fitted parameter, and nothing in the paper forces the reported RPE, AWD, or RMSE values to be small; a genuinely bad map or trajectory would still produce large errors after alignment. The paper's stated redundancy assumption that environmental conditions are unlikely to degrade all sensor inputs simultaneously is an openly declared design premise, not a conclusion smuggled in through a self-citation, and the lack of a combined-degradation stress test is a completeness or evidence concern rather than circularity. Prior Team Cerberus publications are cited for deployment context and extensions, but the architecture's content is described in this paper on its own terms. I therefore find no significant circularity and assign score 0.

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

The central claim rests on a redundancy assumption about sensor degradation, on the reliability of external fallback odometry, and on evaluation procedures that use LiDAR-derived references for a LiDAR-based system. The system itself introduces no new physical entities and only a handful of heuristic thresholds.

free parameters (5)
  • Degeneracy eigenvalue threshold = not reported
    Used in LO and LM modules to declare scan-to-scan or scan-to-submap registration degenerate; chosen heuristically, not derived or reported numerically (Sec. III.C.b).
  • Static period duration = 0.5 s
    Period of no motion required before adding zero-velocity and roll-pitch factors to the factor graph (Sec. III.C.c).
  • Submap range = 50 m
    Range from which map voxels are gathered into the KD-tree for scan-to-submap registration during DARPA SubT (Sec. III.C.d).
  • Global map voxel size = 10 x 10 x 10 m
    Voxel structure of the LiDAR map used by the LM module (Sec. III.C.d).
  • Temporal consistency block size and entropy threshold = not reported
    Image quality assessment in VTIO uses a 4x4 grid, median entropy, and a predefined minimum threshold to select features; exact values are not given (Sec. III.B.a).
assumptions (5)
  • domain assumption Environmental conditions are unlikely to degrade all sensor inputs simultaneously
    Stated in Sec. III.A as the core design assumption for complementary redundancy; if darkness, dust, geometric self-similarity, and motion blur coincide, no complementary modality remains.
  • domain assumption Leg-kinematic odometry remains drift-bounded and robust throughout a 740 m underground run
    Used as the external fallback pose estimate in the evaluation (Sec. IV.B); the reliability of this fallback is assumed rather than measured independently.
  • domain assumption Dense ICP alignment of the estimated map to the ground truth map yields a valid global alignment without absorbing significant drift
    Section IV.B applies T_ICP from segment alignment to the whole map before computing AWD, SCS, and RMSE; if the alignment absorbs loop-closure error, reported accuracy is optimistic.
  • domain assumption Offline ICP registration of LiDAR scans against the ground truth map produces an accurate reference trajectory
    Used to compute RPE in Table IV; because the reference is built from the same modality CompSLAM consumes, it is not an independent ground truth.
  • domain assumption Local planarity and sufficient LiDAR point distribution in a pixel window imply reliable inverse depth initialization
    In Sec. III.B.d, depth verification requires at least two points per quadrant and low depth standard deviation; this assumes the scene is locally planar.

how reviews work

0 comments
Cite this review

Pith. "Pith review of CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments." pith.science (2026). https://pith.science/paper/DS5K7P52

@misc{pith2026250506483,
  author       = {Pith},
  title        = {Pith review of: CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DS5K7P52}},
  note         = {Machine review of arXiv:2505.06483}
}
read the original abstract

Robot autonomy in unknown, GPS-denied, and complex underground environments requires real-time, robust, and accurate onboard pose estimation and mapping for reliable operations. This becomes particularly challenging in perception-degraded subterranean conditions under harsh environmental factors, including darkness, dust, and geometrically self-similar structures. This paper details CompSLAM, a highly resilient and hierarchical multi-modal localization and mapping framework designed to address these challenges. Its flexible architecture achieves resilience through redundancy by leveraging the complementary nature of pose estimates derived from diverse sensor modalities. Developed during the DARPA Subterranean Challenge, CompSLAM was successfully deployed on all aerial, legged, and wheeled robots of Team Cerberus during their competition-winning final run. Furthermore, it has proven to be a reliable odometry and mapping solution in various subsequent projects, with extensions enabling multi-robot map sharing for marsupial robotic deployments and collaborative mapping. This paper also introduces a comprehensive dataset acquired by a manually teleoperated quadrupedal robot, covering a significant portion of the DARPA Subterranean Challenge finals course. This dataset evaluates CompSLAM's robustness to sensor degradations as the robot traverses 740 meters in an environment characterized by highly variable geometries and demanding lighting conditions. The CompSLAM code and the DARPA SubT Finals dataset are made publicly available for the benefit of the robotics community

Figures

Figures reproduced from arXiv: 2505.06483 by the authors.

Figure 1
Figure 1. Narrow passage, traversed by the legged robot (location 4 in Figure [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview figure of the proposed complementary multi-modal SLAM [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Tracked CompSLAM-VTIO image features and overlay of LiDAR [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Highlighting different ranges of map used for scan-to-submap regis [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Sequence captured in the DARPA SubT final circuit environment, comprising tunnel, urban, and cave domains and featuring various geometries, such as [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: The ANYmal quadruped moving through the DARPA Subterranean [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: CompSLAM’s estimated point cloud map. Colors show the euclidean [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 9
Figure 9. Figure 9: Moving persons, captured by the onboard RGB cameras. [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

47 extracted references · 41 canonical work pages

  1. [1]

    Into the robotic depths: Analy- sis and insights from the darpa subterranean challenge,

    T. H. Chung, V . Orekhov, and A. Maio, “Into the robotic depths: Analy- sis and insights from the darpa subterranean challenge,”Annual Review of Control, Robotics, and Autonomous Systems, 2023

    work page 2023

  2. [2]

    Scientific exploration of challenging planetary analog environments with a team of legged robots,

    P. Arm,et al., “Scientific exploration of challenging planetary analog environments with a team of legged robots,”Science robotics, 2023

    work page 2023

  3. [3]

    Marsupial walking-and-flying robotic deployment for collaborative exploration of unknown environments,

    P. De Petriset al., “Marsupial walking-and-flying robotic deployment for collaborative exploration of unknown environments,” inIEEE Inter- national Symposium on Safety, Security, and Rescue Robotics, 2022

    work page 2022

  4. [4]

    A framework for collaborative multi-robot mapping using spec- tral graph wavelets,

    L. Bernreiter, S. Khattak, L. Ott, R. Siegwart, M. Hutter, and C. Ca- dena, “A framework for collaborative multi-robot mapping using spec- tral graph wavelets,”The International Journal of Robotics Research, vol. 43, no. 13, pp. 2070–2088, 2024

    work page 2024

  5. [5]

    Graph-based multi-sensor fusion for consistent localization of autonomous construction robots,

    J. Nubert, S. Khattak, and M. Hutter, “Graph-based multi-sensor fusion for consistent localization of autonomous construction robots,” inIEEE International Conference on Robotics and Automation, 2022

    work page 2022

  6. [6]

    Holistic fusion: Task-and setup-agnostic robot localization and state estimation with factor graphs,

    J. Nubert, T. Tuna, J. Frey, C. Cadena, K. J. Kuchenbecker, S. Khat- tak, and M. Hutter, “Holistic fusion: Task-and setup-agnostic robot localization and state estimation with factor graphs,”arXiv preprint arXiv:2504.06479, 2025

    arXiv 2025

  7. [7]

    Self-supervised learning of lidar odometry for robotic applications,

    J. Nubert, S. Khattak, and M. Hutter, “Self-supervised learning of lidar odometry for robotic applications,” inIEEE international conference on robotics and automation, 2021

    work page 2021

  8. [8]

    Learning-based local- izability estimation for robust lidar localization,

    J. Nubert, E. Walther, S. Khattak, and M. Hutter, “Learning-based local- izability estimation for robust lidar localization,” inIEEE International Conference on Intelligent Robots and Systems (IROS), 2022

    work page 2022

Show all 47 references
  1. [9]

    Comple- mentary multi–modal sensor fusion for resilient robot pose estimation in subterranean environments,

    S. Khattak, H. Nguyen, F. Mascarich, T. Dang, and K. Alexis, “Comple- mentary multi–modal sensor fusion for resilient robot pose estimation in subterranean environments,” inIEEE International Conference on Unmanned Aircraft Systems (ICUAS), 2020

    2020

  2. [10]

    Cerberus: Autonomous legged and aerial robotic exploration in the tunnel and urban circuits of the darpa subterranean challenge,

    M. Tranzattoet al., “Cerberus: Autonomous legged and aerial robotic exploration in the tunnel and urban circuits of the darpa subterranean challenge,”Field Robotics, 2022

    2022

  3. [11]

    Cerberus in the darpa subterranean challenge,

    ——, “Cerberus in the darpa subterranean challenge,”Science Robotics, 2022

    2022

  4. [12]

    Team cerberus wins the darpa subterranean challenge: Technical overview and lessons learned,

    ——, “Team cerberus wins the darpa subterranean challenge: Technical overview and lessons learned,”IEEE Transactions of Field Robotics, 2024

    2024

  5. [13]

    Present and future of slam in extreme environments: The darpa subt challenge,

    K. Ebadiet al., “Present and future of slam in extreme environments: The darpa subt challenge,”IEEE Transactions on Robotics, 2023

    2023

  6. [14]

    A multi-state constraint kalman filter for vision-aided inertial navigation,

    A. I. Mourikis and S. I. Roumeliotis, “A multi-state constraint kalman filter for vision-aided inertial navigation,” inIEEE international confer- ence on robotics and automation, 2007

    2007

  7. [15]

    A robust and modular multi-sensor fusion approach applied to mav navigation,

    S. Lynen, M. W. Achtelik, S. Weiss, M. Chli, and R. Siegwart, “A robust and modular multi-sensor fusion approach applied to mav navigation,” in IEEE international conference on intelligent robots and systems, 2013

    2013

  8. [16]

    Iterated extended kalman filter based visual-inertial odometry using direct photometric feedback,

    M. Bloeschet al., “Iterated extended kalman filter based visual-inertial odometry using direct photometric feedback,”The International Journal of Robotics Research, 2017

    2017

  9. [17]

    State estimation for legged robots on unstable and slippery terrain,

    M. Bloesch, C. Gehring, P. Fankhauser, M. Hutter, M. A. Hoepflinger, and R. Siegwart, “State estimation for legged robots on unstable and slippery terrain,” inIEEE International Conference on Intelligent Robots and Systems, 2013

    2013

  10. [18]

    The two-state implicit filter recursive estimation for mobile robots,

    M. Bloesch, M. Burri, H. Sommer, R. Siegwart, and M. Hutter, “The two-state implicit filter recursive estimation for mobile robots,”IEEE Robotics and Automation Letters, 2017

    2017

  11. [19]

    Openvins: A research platform for visual-inertial estimation,

    P. Geneva, K. Eckenhoff, W. Lee, Y . Yang, and G. Huang, “Openvins: A research platform for visual-inertial estimation,” inIEEE International Conference on Robotics and Automation, 2020

    2020

  12. [20]

    Super odometry: Imu-centric lidar-visual-inertial esti- mator for challenging environments,

    S. Zhaoet al., “Super odometry: Imu-centric lidar-visual-inertial esti- mator for challenging environments,” inIEEE International Conference on Intelligent Robots and Systems (IROS), 2021

    2021

  13. [21]

    Vilens: Visual, inertial, lidar, and leg odometry for all-terrain legged robots,

    D. Wisth, M. Camurri, and M. Fallon, “Vilens: Visual, inertial, lidar, and leg odometry for all-terrain legged robots,”IEEE Transactions on Robotics, 2022

    2022

  14. [22]

    Loam: Lidar odometry and mapping in real- time

    J. Zhang, S. Singhet al., “Loam: Lidar odometry and mapping in real- time.” inRobotics: Science and systems, 2014

    2014

  15. [23]

    Open3d slam: Point cloud based mapping and localization for education,

    E. Jelavic, J. Nubert, and M. Hutter, “Open3d slam: Point cloud based mapping and localization for education,” inRobotic Perception and Mapping: Emerging Techniques, ICRA 2022 Workshop, 2022

    2022

  16. [24]

    Wildcat: Online continuous-time 3d lidar-inertial slam,

    M. Ramezani, K. Khosoussi, G. Catt, P. Moghadam, J. Williams, P. Borges, F. Pauling, and N. Kottege, “Wildcat: Online continuous-time 3d lidar-inertial slam,”arXiv preprint arXiv:2205.12595, 2022

    2022 arXiv

  17. [25]

    Direct lidar-inertial odome- try: Lightweight lio with continuous-time motion correction,

    K. Chen, R. Nemiroff, and B. T. Lopez, “Direct lidar-inertial odome- try: Lightweight lio with continuous-time motion correction,” inIEEE international conference on robotics and automation, 2023

    2023

  18. [26]

    Fast-lio2: Fast direct lidar- inertial odometry,

    W. Xu, Y . Cai, D. He, J. Lin, and F. Zhang, “Fast-lio2: Fast direct lidar- inertial odometry,”IEEE Transactions on Robotics, 2022

    2022

  19. [27]

    Keyframe-based direct thermal–inertial odometry,

    S. Khattak, C. Papachristos, and K. Alexis, “Keyframe-based direct thermal–inertial odometry,” in2019 International conference on robotics and automation. IEEE, 2019, pp. 3563–3569

    2019

  20. [28]

    Airborne particle classification in lidar point clouds using deep learning,

    L. Stanislas, J. Nubert, D. Dugas, J. Nitsch, N. S ¨underhauf, R. Siegwart, C. Cadena, and T. Peynot, “Airborne particle classification in lidar point clouds using deep learning,” inField and Service Robotics: Results of the 12th International Conference, 2021

    2021

  21. [29]

    Cello-3d: Estimating the covariance of icp in the real world,

    D. Landry, F. Pomerleau, and P. Giguere, “Cello-3d: Estimating the covariance of icp in the real world,” in2019 International Conference on Robotics and Automation. IEEE, 2019, pp. 8190–8196

    2019

  22. [30]

    X-icp: Localizability-aware lidar registration for robust localization in extreme environments,

    T. Tuna, J. Nubert, Y . Nava, S. Khattak, and M. Hutter, “X-icp: Localizability-aware lidar registration for robust localization in extreme environments,”IEEE Transactions on Robotics, 2023

    2023

  23. [31]

    Informed, constrained, aligned: A field analysis on degeneracy-aware point cloud registration in the wild,

    T. Tuna, J. Nubert, P. Pfreundschuh, C. Cadena, S. Khattak, and M. Hutter, “Informed, constrained, aligned: A field analysis on degeneracy-aware point cloud registration in the wild,”arXiv preprint arXiv:2408.11809, 2024

    2024 arXiv

  24. [32]

    Dare-slam: Degeneracy-aware and resilient loop closing in perceptually-degraded environments,

    K. Ebadi, M. Palieri, S. Wood, C. Padgett, and A.-a. Agha- mohammadi, “Dare-slam: Degeneracy-aware and resilient loop closing in perceptually-degraded environments,”Journal of Intelligent & Robotic Systems, 2021

    2021

  25. [33]

    Lamp: Large-scale autonomous mapping and posi- tioning for exploration of perceptually-degraded subterranean environ- ments,

    K. Ebadiet al., “Lamp: Large-scale autonomous mapping and posi- tioning for exploration of perceptually-degraded subterranean environ- ments,” inIEEE International Conference on Robotics and Automation, 2020

    2020

  26. [34]

    On degeneracy of optimization-based state estimation problems,

    J. Zhang, M. Kaess, and S. Singh, “On degeneracy of optimization-based state estimation problems,” in2016 IEEE international conference on robotics and automation. IEEE, 2016, pp. 809–816

    2016

  27. [35]

    Visual-thermal landmarks and inertial fusion for navigation in degraded visual environments,

    S. Khattak, C. Papachristos, and K. Alexis, “Visual-thermal landmarks and inertial fusion for navigation in degraded visual environments,” in 2019 IEEE Aerospace Conference. IEEE, 2019, pp. 1–9

    2019

  28. [36]

    Robust thermal-inertial localization for aerial robots: A case for direct methods,

    S. Khattaket al., “Robust thermal-inertial localization for aerial robots: A case for direct methods,” inIEEE International Conference on Un- manned Aircraft Systems (ICUAS), 2019

    2019

  29. [37]

    Keyframe-based thermal– inertial odometry,

    S. Khattak, C. Papachristos, and K. Alexis, “Keyframe-based thermal– inertial odometry,”Journal of Field Robotics, 2020

    2020

  30. [38]

    A unified framework for nearby and distant landmarks in bearing-only slam,

    N. Trawny and S. I. Roumeliotis, “A unified framework for nearby and distant landmarks in bearing-only slam,” inIEEE International Confer- ence on Robotics and Automation, 2006

    2006

  31. [39]

    Inverse depth parametriza- tion for monocular slam,

    J. Civera, A. J. Davison, and J. M. Montiel, “Inverse depth parametriza- tion for monocular slam,”IEEE Transactions on Robotics, 2008

    2008

  32. [40]

    On the comparison of uncer- tainty criteria for active slam,

    H. Carrillo, I. Reid, and J. A. Castellanos, “On the comparison of uncer- tainty criteria for active slam,” in2012 IEEE International Conference on Robotics and Automation. IEEE, 2012, pp. 2080–2087

    2012

  33. [41]

    Anymal - a highly mobile and dynamic quadrupedal robot,

    M. Hutteret al., “Anymal - a highly mobile and dynamic quadrupedal robot,” inIEEE International Conference on Intelligent Robots and Systems (IROS), 2016

    2016

  34. [42]

    On-manifold preintegration for real-time visual–inertial odometry,

    C. Forster, L. Carlone, F. Dellaert, and D. Scaramuzza, “On-manifold preintegration for real-time visual–inertial odometry,”IEEE Transactions on Robotics, vol. 33, no. 1, pp. 1–21, 2016

    2016

  35. [43]

    Factor graphs and gtsam: A hands-on introduction,

    F. Dellaert, “Factor graphs and gtsam: A hands-on introduction,”Geor- gia Institute of Technology, Tech. Rep, vol. 2, no. 4, 2012

    2012

  36. [44]

    Collaborative robot mapping using spectral graph analysis,

    L. Bernreiter, S. Khattak, L. Ott, R. Siegwart, M. Hutter, and C. Cadena, “Collaborative robot mapping using spectral graph analysis,” inIEEE international conference on robotics and automation, 2022

    2022

  37. [45]

    Unified temporal and spatial calibration for multi-sensor systems,

    P. Furgale, J. Rehder, and R. Siegwart, “Unified temporal and spatial calibration for multi-sensor systems,” in2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, pp. 1280–1286

    2013

  38. [46]

    Mapeval: Towards unified, robust and efficient slam map evaluation framework,

    X. Hu, J. Wu, M. Jia, H. Yan, Y . Jiang, B. Jiang, W. Zhang, W. He, and P. Tan, “Mapeval: Towards unified, robust and efficient slam map evaluation framework,” 2024. [Online]. Available: https://arxiv.org/abs/2411.17928

    2024 arXiv

  39. [47]

    evo: Python package for the evaluation of odometry and slam

    M. Grupp, “evo: Python package for the evaluation of odometry and slam.” https://github.com/MichaelGrupp/evo, 2017

    2017

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.