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REVIEW 3 major objections 5 minor 39 references

Robust Localization, Mapping, and Navigation for Quadruped Robots

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A low-cost quadruped robot can localize, map, and navigate accurately by fusing contact-aided leg odometry, visual-inertial odometry, and IMU-stabilized depth scans.

desk verdict Useful system integration for low-cost quadrupeds, but the central ablation table is internally inconsistent and contradicts the paper's own causal story. read the letter →

arxiv 2505.02272 v2 pith:6SZTGVVX submitted 2025-05-04 cs.RO cs.AI

Dyuman Aditya , Junning Huang , Nico Bohlinger , Piotr Kicki , Krzysztof Walas , Jan Peters , Matteo Luperto , Davide Tateo This is my paper · ORCID
classification cs.ROcs.AI
keywords quadrupedrobots2DSLAMlegodometryvisual-inertialscanstabilizationcontactestimationautonomousnavigationlow-costsensors
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 sets out to show that a cheap quadruped robot—equipped only with a depth camera, an IMU, and joint encoders—can map a room, know where it is, and navigate to goals, tasks usually reserved for expensive platforms with long-range lidars. It argues that the obstacle is not the SLAM algorithms themselves but the instability of depth-derived scans when a legged robot pitches and rolls with every step. The proposed fix selects a slanting row of depth pixels determined by the IMU's roll and pitch, producing a stabilized 2D scan, and adds leg odometry as a velocity constraint in the pose graph. In the paper's experiments, the full system outperforms every ablated variant, reaches 100 percent navigation success in the simulated house and warehouse scenarios tested, and the real-robot experiments confirm the same ordering of ablations. The takeaway is that one small sensor-fusion detail, not a new SLAM paradigm, unlocks robust autonomy for low-cost legged robots.

What carries the argument

The load-bearing object is an IMU-stabilized scan: from a depth image, choose the row whose slope is set by the roll angle and whose intercept is $-f_y\tan(p)$, where $p$ is pitch and $f_y$ is the focal length, and combine pixels above and below that row into a lidar-like 2D scan. Around this, the paper stacks a least-squares leg odometry that solves for the body twist $\hat{V}_b$ from foot-contact constraints plus IMU angular velocity measurements, and a contact-state observer based on generalized momentum that estimates foot contacts from joint torques alone. The leg twist is used to reinitialize visual-inertial odometry after tracking loss and to impose velocity constraints between consecutive poses in the 2D SLAM factor graph, preventing scan mismatches from corrupting the map.

What would settle it

Tilt a mounted depth camera by a known pitch angle, point it at a flat wall at a known distance, and read off the image row where the wall appears. Compare that row with the prediction of Eq. 5 using the camera's focal length; a discrepancy of more than a few pixels across the usable width would show that the single-point pinhole derivation does not describe the full row, and because the paper's ablation identifies scan stabilization as the most critical component, that failure would undermine the central claim.

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Extended reading notes

Core claim

The central claim, stated on the paper's own terms, is that contact-aided kinematic leg odometry, visual-inertial odometry, and depth-stabilized vision can be fused into a single 2D localization, mapping, and navigation stack that works on low-cost quadruped platforms. The discovery is that scan stabilization is the load-bearing component: without it, localization error grows by roughly an order of magnitude in several simulated and real configurations and navigation often fails; with it, the robot localizes on a pre-built map and reaches every commanded pose in the tested scenarios. Leg odometry alone does not rescue the baseline, and in some cases degrades it when scans are badly aligned; it becomes valuable only in combination with stable scans, where it reinitializes lost visual odometry and adds velocity edges to the factor graph.

Load-bearing premise

The system stands on the assumption that one slanted row of depth pixels always samples the same horizontal plane in the world, no matter how the robot is tilted; camera distortion or a tilted mounting would break the stabilized scans, and the paper's own ablation says those scans are the most critical part.

Editorial extensions

If this is right

  • With the full pipeline, navigation on a pre-built map succeeds 100 percent of the time in the tested house and warehouse scenarios for both robots, compared with 0 to 80 percent for the baseline.
  • The ablation shows scan stabilization is the single most critical module; removing it inflates absolute trajectory error by roughly an order of magnitude in several configurations.
  • Leg odometry contributes by reinitializing visual odometry after tracking loss and by adding velocity constraints to the 2D factor graph, but only when scans are already stabilized.
  • The same pipeline, including torque-based contact estimation, works on a real quadruped with an actuated spine and in cluttered indoor environments, not just in simulation.
  • Because the system uses 2D grid maps, it plugs into standard 2D navigation stacks rather than requiring expensive 3D mapping hardware or algorithms.

Reading between the lines

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

  • The IMU-row selection trick is not inherently tied to legged platforms; the same formula should stabilize depth slices on any tilt-prone base, so a direct test on a wheeled rover with suspension or a handheld depth camera would check how far the mechanism generalizes.
  • Because the stabilized scan is a single horizontal slice of the world, an obvious extension is to sample several parallel slices and stack them into a 2.5D or multi-floor map; the paper only commits to 2D.
  • The strong ablation result suggests a practical rule of thumb the authors do not state: before improving hardware, check camera calibration and mounting stiffness, since a distorted or loose camera would break the exact row-to-plane correspondence the method relies on.
  • A testable refinement of the contact estimator would be to compare its torque-based contact states against a platform with real foot contact sensors during the same gaits; the paper validates the estimator mainly through downstream localization accuracy.
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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

3 major / 5 minor

Summary. The manuscript presents an integrated localization, mapping, and navigation system for low-cost quadruped robots equipped with an RGB-D camera and an IMU. It augments a standard RTAB-Map/SLAM Toolbox 2D pipeline with three components: contact estimation from generalized momentum, legged odometry via least-squares twist estimation, and IMU-stabilized scan extraction from depth images. The authors claim that the full system produces accurate 2D maps, robust localization, and autonomous navigation, and they identify scan stabilization as the most critical component. The evaluation uses Gazebo simulation (AWS small house and warehouse), real-world Silver Badger experiments with OptiTrack ground truth, navigation success tests, and autonomous exploration, with five runs per condition and ablations of leg odometry and scan stabilization. Code, videos, and additional material are released on a project website.

Significance. If the claims survive the required corrections, the paper makes a useful practical contribution: it targets low-cost sensors, evaluates against external ground truth, reports real-hardware results, and releases code and supplementary material. The ablation design is methodologically sound in principle, and the real-world full-system results are encouraging. However, the printed simulation ablation in Table I contains internally inconsistent values that undermine the stated causal attribution of the improvements, the scan-stabilization derivation is not fully justified for the implemented slice extraction, and the contact-estimation module is not isolated in any controlled ablation. These issues must be fixed before the central claims can be accepted.

major comments (3)
  1. [Table I and Section IV-C] Table I contains internally inconsistent APE values for the Silver Badger rows. In the AWS Small Warehouse, B+SS is reported with ATE 5.19±2.24 and APE 0.45±0.11, while B+LO is reported with ATE 0.44±0.11 and APE 7.90±0.83. If APE is a combined pose error that includes translation, these entries cannot both be correct, and the B+SS and B+LO APE columns appear to be swapped. More importantly, reading the ATE/ARE columns literally, B+LO reduces the warehouse ATE from 5.00 to 0.44, whereas B+SS leaves it at 5.19; this contradicts the Section IV-C statement that "scan stabilization proves to be the most critical component" and instead credits leg odometry with the main translation improvement. The same pattern appears in the AWS Small House SB rows. Even after correcting the swap, the claim needs to be metric-specific: B+SS improves RPE locally (warehouse RPE 2m: 0.51 to 0.18) while B+LO improves ATE, so "most critical" is too broad as printed. Please correct Table I, re-analyze the attribution, and revise Sections IV-C and V accordingly.
  2. [Section III-C, Eq. (5)] The derivation of the scan-line equation is carried out for a single point on the optical axis, and Eq. (5) is then applied as a global row offset and slope to the entire depth slice. For an ideal pinhole camera the offset is exact for points on the horizontal plane through the camera center, but the manuscript does not state this assumption, and the implementation also ignores lens distortion and any camera-IMU extrinsic misalignment. Since Section IV-C identifies scan stabilization as the most critical component, this is not merely a presentation issue. Please either extend the derivation to the full projection model, quantify the approximation error on the actual D435i (for example, by comparing Eq. (5) against ray-plane intersection for the depth image), or otherwise validate that the fixed line and fixed slice width produce a scan that corresponds to a consistent world plane.
  3. [Section IV-A and contribution (i)(b)] Contact estimation is listed as a contribution, but it is never ablated. In the simulation experiments the authors state they "use the ground-truth contact instead" (Section IV-A), and the real-world experiments in Table III compare configurations with and without leg odometry and scan stabilization but do not isolate the contact estimator. Thus there is no controlled evidence that the generalized-momentum observer with feet-specific thresholds performs comparably to true contact sensing. Please add an evaluation that compares estimated contacts against ground-truth contacts (for example, B+LO with estimated versus true contacts in simulation, or a real-world sequence where contacts are also measured by an instrumented foot), or explicitly narrow the contribution claim to the integration rather than to the contact-estimation module itself.
minor comments (5)
  1. [Section I] The text contains a typo: "RBGD camera" should be "RGBD camera".
  2. [Tables I and III] The units of ARE are not stated. Please specify whether the reported angular errors are in degrees or radians, since this is necessary for interpreting the magnitudes.
  3. [Section IV-D / Table II] The navigation success results report 0% and 100% values but do not state the number of independent trials. Please clarify whether these are five navigation runs per condition or five goals within a single run, and report confidence intervals or per-goal counts.
  4. [Section III-C] The sign convention in Eq. (5) is not defined: it should be stated whether a positive roll/pitch corresponds to a clockwise or counterclockwise rotation in the image frame, and how the camera frame is oriented relative to the robot body frame.
  5. [General] Several tuning parameters that affect the results are not given numerically in the paper: the observer gains L1, L2 and L, the feet-specific contact thresholds, the slice width, and the factor-graph velocity constraint weights. The project website is a good resource, but a complete reproducibility table in the paper would be preferable.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the claimed results are validated against external ground truth and do not reduce to their inputs.

full rationale

The paper's central claim—that a low-cost quadruped can localize, map, and navigate by combining contact-aided kinematic odometry, VIO, and depth-stabilized scans—is not circular. Each component is an independent algorithm drawn from published work (RTAB-Map [11], 2D SLAM toolbox [38], GM observer [33], leg odometry [35]) rather than from assumptions that presuppose the target result. Scan stabilization is derived from a pinhole projection model (Eq. 5), not fitted to the mapping outcome. Accuracy claims are evaluated against external ground truth (Gazebo simulation and OptiTrack motion capture, Tables I and III), and navigation success is a downstream task metric, not an input. No fitted parameter is renamed as a prediction, and the paper's load-bearing citations are to external libraries and algorithms, not to the authors' own prior theorems. The Table I APE/ATE inconsistency noted by a skeptical reading is a correctness or data-integrity concern, not a circularity concern; it does not make the derivation assume its conclusion. The weakest assumption (Eq. 5 applied as a uniform pixel-shift slice) is a modeling approximation, not circular logic. Accordingly, the circularity score is 0.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The system rests on standard robotics assumptions: a calibrated pinhole camera, an IMU whose attitude is trusted, leg contacts that can be detected from joint torques, and an indoor horizontal 2D map assumption. The novel components add several hand-tuned parameters (observer gains, contact thresholds, slice width, factor-graph weights) whose values are not reported, which weakens reproducibility. No new physical entities are introduced.

free parameters (4)
  • Observer gains L1, L2 (and L, k1, k2) = not reported
    Gains for the generalized-momentum contact observer in Section III-A; values affect contact-state detections but are not specified.
  • Feet-specific contact thresholds = not reported
    Thresholds used to convert filtered estimated contact forces into binary contact states (Section III-A); chosen by hand and not reported.
  • Scan stabilization slice width = not reported
    Number of pixels above/below the reference line used to form the stabilized depth scan (Section III-C); not specified.
  • Factor-graph velocity constraint weights = not reported
    Weights applied to the leg-odometry velocity constraints between consecutive poses in the 2D SLAM factor graph (Section III-E); not reported.
assumptions (5)
  • domain assumption Pinhole camera model with known intrinsics K, fx, fy, cx, cy
    Used in the scan stabilization derivation in Section III-C; if the RealSense camera has significant distortion, the line-slice formula in Eq. 5 will be inaccurate.
  • domain assumption The contact force nonlinearities satisfy a global incremental affine bound, so the mixed sliding-mode/high-gain observer converges
    Invoked in Section III-A via reference [34]; not verified on the actual robot data in this paper.
  • domain assumption At least two legs are in contact and their contact constraints are linearly independent, so the leg-odometry least-squares problem is observable
    Required in Section III-B to solve for the 6-DoF body twist; during parts of the gait cycle (e.g., flight phases) this may not hold.
  • domain assumption The robot operates indoors on roughly horizontal surfaces, so a 2D grid map and a horizontal scan slice are sufficient
    Stated in Section I as a restriction to indoor settings; stairs or ramps would break the 2D map assumption.
  • domain assumption The IMU provides an accurate roll and pitch attitude estimate
    Scan stabilization depends on roll and pitch angles from the onboard IMU (Section III-C); if attitude drift is significant, the stabilized slice is wrong.

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Cite this review

Pith. "Pith review of Robust Localization, Mapping, and Navigation for Quadruped Robots." pith.science (2026). https://pith.science/paper/6SZTGVVX

@misc{pith2026250502272,
  author       = {Pith},
  title        = {Pith review of: Robust Localization, Mapping, and Navigation for Quadruped Robots},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6SZTGVVX}},
  note         = {Machine review of arXiv:2505.02272}
}
read the original abstract

Quadruped robots are currently a widespread platform for robotics research, thanks to powerful Reinforcement Learning controllers and the availability of cheap and robust commercial platforms. However, to broaden the adoption of the technology in the real world, we require robust navigation stacks relying only on low-cost sensors such as depth cameras. This paper presents a first step towards a robust localization, mapping, and navigation system for low-cost quadruped robots. In pursuit of this objective we combine contact-aided kinematic, visual-inertial odometry, and depth-stabilized vision, enhancing stability and accuracy of the system. Our results in simulation and two different real-world quadruped platforms show that our system can generate an accurate 2D map of the environment, robustly localize itself, and navigate autonomously. Furthermore, we present in-depth ablation studies of the important components of the system and their impact on localization accuracy. Videos, code, and additional experiments can be found on the project website: https://sites.google.com/view/low-cost-quadruped-slam

Figures

Figures reproduced from arXiv: 2505.02272 by the authors.

Figure 1
Figure 1. Overall system design for robust navigation in low-cost quadruped [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Effects of scan stabilization in the map generation. Left: no scan [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Estimated trajectories of the Silver badger robot in the small [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: Estimated trajectories using our system and the baseline of the [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Maps generated on two real-world environments. Left: IAS lab. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

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Reference graph

Works this paper leans on

39 extracted references · 32 canonical work pages

  1. [1]

    Learning robust perceptive locomotion for quadrupedal robots in the wild,

    T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V . Koltun, and M. Hutter, “Learning robust perceptive locomotion for quadrupedal robots in the wild,”Science Robotics, vol. 7, no. 62, p. eabk2822, 2022

    2022

  2. [2]

    Learning to walk in minutes using massively parallel deep reinforcement learning,

    N. Rudin, D. Hoeller, P. Reist, and M. Hutter, “Learning to walk in minutes using massively parallel deep reinforcement learning,” in Conference on Robot Learning. PMLR, 2022, pp. 91–100

    2022

  3. [3]

    Rapid locomotion via reinforcement learning,

    G. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal, “Rapid locomotion via reinforcement learning,” inRobotics: Science and Systems, 2022

    work page 2022

  4. [4]

    Walk these ways: Tuning robot control for generalization with multiplicity of behavior,

    G. B. Margolis and P. Agrawal, “Walk these ways: Tuning robot control for generalization with multiplicity of behavior,” inProc. CORL, 2023

    work page 2023

  5. [5]

    Demonstrating a walk in the park: Learning to walk in 20 minutes with model-free reinforcement learning,

    L. Smith, I. Kostrikov, and S. Levine, “Demonstrating a walk in the park: Learning to walk in 20 minutes with model-free reinforcement learning,”Proc. RSS, vol. 2, no. 3, p. 4, 2023

    work page 2023

  6. [6]

    Grow your limits: Continuous improvement with real-world rl for robotic locomotion,

    L. Smith, Y . Cao, and S. Levine, “Grow your limits: Continuous improvement with real-world rl for robotic locomotion,” inProc. ICRA, 2024, pp. 10 829–10 836

    work page 2024

  7. [7]

    Learning a state representation and navigation in cluttered and dynamic environ- ments,

    D. Hoeller, L. Wellhausen, F. Farshidian, and M. Hutter, “Learning a state representation and navigation in cluttered and dynamic environ- ments,”IEEE RA-L, vol. 6, no. 3, pp. 5081–5088, 2021

    work page 2021

  8. [8]

    Deep reinforcement learning for robotics: A survey of real- world successes,

    C. Tang, B. Abbatematteo, J. Hu, R. Chandra, R. Mart ´ın-Mart´ın, and P. Stone, “Deep reinforcement learning for robotics: A survey of real- world successes,” inProc. AAAI, 2025

    work page 2025

Show all 39 references
  1. [9]

    A survey on active simultaneous localization and mapping: State of the art and new frontiers,

    J. A. Placed, J. Strader, H. Carrillo, N. Atanasovet al., “A survey on active simultaneous localization and mapping: State of the art and new frontiers,”IEEE T-RO, 2023

    2023

  2. [10]

    Motion planning and control for mobile robot navigation using machine learning: a survey,

    X. Xiao, B. Liu, G. Warnell, and P. Stone, “Motion planning and control for mobile robot navigation using machine learning: a survey,” Autonomous Robots, vol. 46, no. 5, pp. 569–597, 2022

    2022

  3. [11]

    Rtab-map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation,

    M. Labb ´e and F. Michaud, “Rtab-map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation,”Journal of field robotics, vol. 36, no. 2, pp. 416–446, 2019

    2019

  4. [12]

    A robust visual slam system for small-scale quadruped robots in dynamic environments,

    C. Li, Y . Zhang, Z. Yu, X. Liu, and Q. Shi, “A robust visual slam system for small-scale quadruped robots in dynamic environments,” inProc. IROS. IEEE, 2024, pp. 321–326

    2024

  5. [13]

    A tightly- coupled lidar-imu slam method for quadruped robots,

    Z. Zhou, C. Zhang, C. Li, Y . Zhang, Y . Shi, and W. Zhang, “A tightly- coupled lidar-imu slam method for quadruped robots,”Measurement and Control, vol. 57, no. 7, pp. 1004–1013, 2024

    2024

  6. [14]

    Learning robust autonomous navigation and locomotion for wheeled- legged robots,

    J. Lee, M. Bjelonic, A. Reske, L. Wellhausen, T. Miki, and M. Hutter, “Learning robust autonomous navigation and locomotion for wheeled- legged robots,”Science Robotics, vol. 9, no. 89, p. eadi9641, 2024

    2024

  7. [15]

    Autonomous exploration and mapping payload integrated on a quadruped robot,

    J. Y . Raheema, M. R. H. R. C. Provost, M. Bilinski, and H. I. Chris- tensen, “Autonomous exploration and mapping payload integrated on a quadruped robot,” inProc. ISRR, 2024

    2024

  8. [16]

    Feature assessment and enhanced vertical constraint lidar odometry and map- ping on quadruped robot,

    J. Li, D. Zeng, Q. Luo, X. Luo, C. P. Chen, and C. Yang, “Feature assessment and enhanced vertical constraint lidar odometry and map- ping on quadruped robot,”IEEE Transactions on Instrumentation and Measurement, 2025

    2025

  9. [17]

    Fast lidar informed visual search in unseen indoor environments,

    R. Gupta, K. Morgenstein, S. Ortega, and L. Sentis, “Fast lidar informed visual search in unseen indoor environments,” in2025 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2025, pp. 1170–1176

    2025

  10. [18]

    Experimental evalu- ation of autonomous map-based spot navigation in confined environ- ments,

    A. Koval, S. Karlsson, and G. Nikolakopoulos, “Experimental evalu- ation of autonomous map-based spot navigation in confined environ- ments,”Biomimetic Intelligence and Robotics, vol. 2, no. 1, p. 100035, 2022

    2022

  11. [19]

    Periodic slam: Using cyclic constraints to improve the performance of visual-inertial slam on legged robots,

    H. Kumar, J. J. Payne, M. Travers, A. M. Johnson, and H. Choset, “Periodic slam: Using cyclic constraints to improve the performance of visual-inertial slam on legged robots,” inProc. ICRA, 2022

    2022

  12. [20]

    A hybrid-dimensional laser slam framework for indoor quadruped inspection robots,

    J. Cheng, M. Zhang, L. Ma, H. Chen, Y . Gan, and D. Huang, “A hybrid-dimensional laser slam framework for indoor quadruped inspection robots,”IEEE Sensors Journal, 2024

    2024

  13. [21]

    Autonomous spot: Long-range autonomous exploration of extreme environments with legged locomotion,

    A. Bouman, M. F. Ginting, N. Alatur, M. Palieri, D. D. Fan, T. Touma, T. Pailevanian, S.-K. Kim, K. Otsu, J. Burdicket al., “Autonomous spot: Long-range autonomous exploration of extreme environments with legged locomotion,” inProc. IROS, 2020

    2020

  14. [22]

    Automated indoor 3d scene reconstruction with decoupled mapping using quadruped robot and lidar sensor,

    V . J. Gan, D. Hu, Y . Wang, and R. Zhai, “Automated indoor 3d scene reconstruction with decoupled mapping using quadruped robot and lidar sensor,”Computer-Aided Civil and Infrastructure Engineering, 2025

    2025

  15. [23]

    Mine tunnel exploration using multiple quadrupedal robots,

    I. D. Miller, F. Cladera, A. Cowley, S. S. Shivakumar, E. S. Lee, L. Jarin-Lipschitz, A. Bhat, N. Rodrigues, A. Zhou, A. Cohenet al., “Mine tunnel exploration using multiple quadrupedal robots,”IEEE RA-L, 2020

    2020

  16. [24]

    Vital: Vision-based terrain-aware locomotion for legged robots,

    S. Fahmi, V . Barasuol, D. Esteban, O. Villarreal, and C. Semini, “Vital: Vision-based terrain-aware locomotion for legged robots,” IEEE Transactions on Robotics, vol. 39, no. 2, pp. 885–904, 2022

    2022

  17. [25]

    Learning agile locomotion on risky terrains,

    C. Zhang, N. Rudin, D. Hoeller, and M. Hutter, “Learning agile locomotion on risky terrains,” inProc. IROS. IEEE, 2024

    2024

  18. [26]

    Resilient legged local navigation: Learning to traverse with compromised perception end-to-end,

    C. Zhang, J. Jin, J. Frey, N. Rudin, M. Mattamala, C. Cadena, and M. Hutter, “Resilient legged local navigation: Learning to traverse with compromised perception end-to-end,” inProc. ICRA. IEEE, 2024, pp. 34–41

    2024

  19. [27]

    Legolas: Deep leg-inertial odometry,

    J. Wasserman, A. Agarwal, R. Jangir, G. Chowdharyet al., “Legolas: Deep leg-inertial odometry,” inProc. CORL, 2024

    2024

  20. [28]

    Legged robot state estimation with dynamic contact event information,

    J.-H. Kim, S. Hong, G. Ji, S. Jeon, J. Hwangbo, J.-H. Oh, and H.- W. Park, “Legged robot state estimation with dynamic contact event information,”IEEE RA-L, 2021

    2021

  21. [29]

    Step: State estimator for legged robots using a preintegrated foot velocity factor,

    Y . Kim, B. Yu, E. M. Lee, J.-h. Kim, H.-w. Park, and H. Myung, “Step: State estimator for legged robots using a preintegrated foot velocity factor,”IEEE RA-L, 2022

    2022

  22. [30]

    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, vol. 39, no. 1, pp. 309–326, 2022

    2022

  23. [31]

    Cerberus: Low-drift visual-inertial-leg odometry for agile locomotion,

    S. Yang, Z. Zhang, Z. Fu, and Z. Manchester, “Cerberus: Low-drift visual-inertial-leg odometry for agile locomotion,” inProc. ICRA, 2023

    2023

  24. [32]

    Realant: An open-source low-cost quadruped for education and research in real-world reinforcement learning,

    R. Boney, J. Sainio, M. Kaivola, A. Solin, and J. Kannala, “Realant: An open-source low-cost quadruped for education and research in real-world reinforcement learning,”arXiv preprint arXiv:2011.03085, 2020

    2011 arXiv

  25. [33]

    Contact model fusion for event-based locomotion in unstructured terrains,

    G. Bledt, P. M. Wensing, S. Ingersoll, and S. Kim, “Contact model fusion for event-based locomotion in unstructured terrains,” in Proc.ICRA, 2018

    2018

  26. [34]

    Observer design via intercon- nections of second-order mixed sliding-mode/linear differentiators,

    V . Andrieu, D. Astolfi, and P. Bernard, “Observer design via intercon- nections of second-order mixed sliding-mode/linear differentiators,” International Journal of Robust and Nonlinear Control, vol. 31, no. 9, pp. 3631–3657, 2021

    2021

  27. [35]

    Leg odometry for slam,

    M. Camurri and M. Mattamala, “Leg odometry for slam,” inSLAM Handbook — From Localization and Mapping to Spatial Intelligence, L. Carlone, A. Kim, F. Dellaert, T. Barfoot, and D. Cremers, Eds. Cambridge University Press, 2025

    2025

  28. [36]

    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

  29. [37]

    A benchmark for the evaluation of rgb-d slam systems,

    J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, “A benchmark for the evaluation of rgb-d slam systems,” inProc. IROS, 2012

    2012

  30. [38]

    Slam toolbox: Slam for the dynamic world,

    S. Macenski and I. Jambrecic, “Slam toolbox: Slam for the dynamic world,”Journal of Open Source Software, 2021

    2021

  31. [39]

    A frontier-based approach for autonomous exploration,

    B. Yamauchi, “A frontier-based approach for autonomous exploration,” inProc. CIRA, 1997, pp. 146–151

    1997

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