Vibration-aware Lidar-Inertial Odometry based on Point-wise Post-Undistortion Uncertainty

Bin Han; Enci Xu; Shaoqiang Qiu; Wenxuan Li; Yan Dong; Yang Liu

REVIEW 4 major objections 7 minor 24 references

Vibration-aware Lidar-Inertial Odometry based on Point-wise Post-Undistortion Uncertainty

T0 review · 4 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper introduces point-wise post-undistortion uncertainty, a per-point covariance that guides matching and Kalman updates, and shows it improves odometry on vibrating robots.

desk verdict A useful, engineering-solid LIO paper that assigns post-undistortion uncertainty from vibration intensity and leverages it in matching and Kalman weighting; the main weakness is a hand-set gamma that is only asserted to be insensitive, not demonstrated. read the letter →

arxiv 2507.04311 v1 pith:AHMNZIOY submitted 2025-07-06 cs.RO

Yan Dong , Enci Xu , Shaoqiang Qiu , Wenxuan Li , Yang Liu , Bin Han This is my paper · ORCID

classification cs.RO

keywords LiDAR-inertialodometryvibrationpointcloudundistortionuncertaintyestimationiteratedKalmanfilterscan-to-mapmatchinggroundrobotsroughterrain

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 for LiDAR-inertial odometry running on vibrating ground robots, perfect undistortion of LiDAR scans is unnecessary as long as each point's remaining error can be quantified. It introduces point-wise post-undistortion uncertainty, a per-point covariance derived from measured angular and linear vibration intensity, and shows how to feed that covariance into an iterated Kalman filter through uncertainty-guided point-to-map matching and uncertainty-weighted residuals. If the claim holds, robots on rough terrain can get more accurate trajectories without upgrading IMU hardware, since the estimator learns to trust each scan point proportionally to its estimated reliability. The paper supports the claim with vibration-platform tests, public dataset experiments, and own rough-terrain recordings where the method outperforms Fast-LIO and other baselines under intense vibration.

What carries the argument

The central object is the point-wise post-undistortion uncertainty, a 3x3 covariance attached to each undistorted LiDAR point after IMU-based motion compensation. It is built from three components: (i) rotational uncertainty from angular vibration, expressed as $\lfloor p \rfloor_\times \mathrm{diag}\{\sigma_{rx}^2, \sigma_{ry}^2, \sigma_{rz}^2\} \lfloor p \rfloor_\times^T$, where $\lfloor p \rfloor_\times$ is the skew-symmetric matrix of the point's coordinates; (ii) translational uncertainty $\mathrm{diag}\{\sigma_{Tx}^2, \sigma_{Ty}^2, \sigma_{Tz}^2\}$; and (iii) the LiDAR's own measurement noise, modeled from range and bearing noise and rotated into the global frame. The vibration-dependent scales are set by mean absolute deviation of angular and linear velocity, scaled by the point's time offset from scan start. This covariance is used in two places: to pick the nearest planes by Mahalanobis distance rather than Euclidean distance, and to weigh the point-to-plane residual $R_j$ in the iterated Kalman filter update.

What would settle it

Run a sensitivity sweep over gamma across different IMU sampling rates (for example, 100 Hz versus 200 Hz) and different vibration amplitudes: if the optimal gamma changes materially across conditions, or if a single gamma fails to improve or degrades odometry relative to Fast-LIO on some intense-vibration sequence, the core claim of a robust uncertainty scale is undermined. Concretely, running the method with gamma from 0.01 to 1.0 on the 04 all-challenges sequence and checking whether the mean absolute pose error stays below Fast-LIO's 0.062 m would directly test the insensitivity assertion.

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Extended reading notes

Core claim

The central discovery is that undistortion error from intense vibration can be modeled per point as a Gaussian covariance, computed before matching rather than after, and that this covariance contains usable signal for state estimation. The paper defines the post-undistortion uncertainty as the sum of a rotational covariance $\lfloor p \rfloor_\times \Sigma_r \lfloor p \rfloor_\times^T$, a translational covariance $\Sigma_T$, and the LiDAR measurement noise rotated by the undistortion rotation: $L\Sigma_p = \Sigma_{\mathrm{rot}} + \Sigma_{\mathrm{trans}} + R \Sigma_{\mathrm{meas}} R^T$. The rotational and translational error scales are tied to the angular and linear vibration intensity via $\sigma_r = \gamma \, \delta t \, k_\omega$ and $\sigma_T = \gamma \, \delta t \, k_v$, where $k_\omega$ and $k_v$ are mean absolute deviations of LiDAR-frame angular and linear velocity over the scan. This per-point covariance then drives a Mahalanobis-distance selection of matching map points and a residual weight $R_j = u^T {}^G\Sigma_{p_j} u$ inside an iterated Kalman filter. The paper reports that this pipeline yields lower absolute pose error than the Fast-LIO, Point-LIO, LIO-SAM, LOAM, and LIO-Mapping baselines on intense-vibration sequences, and that it runs in about 36 ms per scan.

Load-bearing premise

The entire uncertainty model depends on one hand-set scalar gamma = 0.1 that converts a mean-absolute-deviation measure of vibration into per-point covariance; if that scaling is wrong for a given IMU frequency, terrain, or LiDAR model, the claimed accuracy gain could shrink or reverse.

Editorial extensions

If this is right

Odometry on rough-terrain robots can improve without hardware changes, since the method only adds per-point covariance computation and a Mahalanobis matching step.
The uncertainty estimate makes the matching step robust to scan distortion: points with large predicted error are less influential in the state update.
The method keeps real-time performance (about 36 ms per scan with roughly 2,500 points), so it can run on embedded computers.
The formulation is a drop-in modification to Fast-LIO-style iterated Kalman filters, meaning existing LIO systems could adopt the uncertainty weighting with limited changes.

Reading between the lines

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

The scalar gamma at 0.1 is a global calibration; if gamma varies with IMU sampling rate or LiDAR model, the method may need re-tuning per platform, and the paper's insensitivity claim is based on limited ablations that only test different vibration-intensity measures, not gamma values.
The same post-undistortion covariance could be propagated into loop-closure or map-update stages, not only the current scan's matching, potentially reducing drift accumulation further beyond what the paper reports.
The vibration intensity estimate via mean absolute deviation is heuristic; a learned or model-based predictor of undistortion error could replace it and might remove the need to hand-set gamma.
The approach could be tested on legged or tracked robots, whose vibration spectra differ from wheeled robots, to see whether the Gaussian covariance model generalizes across platforms.

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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

Colleague,

The core idea here is genuinely new: instead of trying to make undistortion more accurate under intense vibration, the authors estimate how wrong each point might be after undistortion and feed that uncertainty into matching and Kalman residual weighting. That is a sensible reframing, and the combination of vibration-derived per-point covariance (rotational, translational, plus measurement noise) with an iterated Kalman filter is, to my knowledge, not in the cited literature. The paper does well at the mechanics: the error-propagation derivation (Eq. 5-14) is coherent under the stated small-angle and Gaussian assumptions, and the experiments are consistent and reasonably broad — vibration platform, public datasets, and their own rough-terrain recordings with cm-level RTK truth. The ablations actually separate the contributions of the uncertainty model and the guided matching, which is better than most papers at this level. The one public sequence where they place second (Botanic Garden 1008-13) is reported honestly.

The soft spots are real but not fatal. The biggest is gamma: Eq. 17 sets sigma = gamma * dt * k, with gamma fixed at 0.1 and only a one-line assertion that the method is insensitive to it. No sensitivity sweep, no calibration against actual undistortion error. The stress-test concern about linear vs sqrt(dt) scaling is worth taking seriously — if the dominant error were random-walk IMU noise, sigma should grow as sqrt(dt), and a fixed gamma cannot fix a misspecified functional form. That said, the experiments still show improvements on multiple independent datasets, which would be a strange accident if the whole effect came from a lucky gamma. I also note the vibration intensity k_v uses the filter's own velocity estimate, creating a feedback path that is not analyzed; in practice this is likely minor because odometry drift is small over a 0.1 s scan. The published results are good enough that I would not block on these concerns, but a revision should show a gamma sweep and ideally compare the dt-linear model against a dt-sqrt alternative.

Is it a new result? Yes — the point-wise post-undistortion covariance propagated from vibration intensity and used to re-rank matches and weight residuals is a combination I have not seen. It is not a paradigm shift, but it is a practical, reusable technique. Who is this for? Anyone doing LIO on ground robots or handheld devices with modest IMUs; the code would be the real deliverable, and the paper does not ship it (only the dataset link is mentioned). I would be comfortable sending this to a serious referee; I would probably ask for the gamma robustness data and a note on the dt dependence, and I would note that the public dataset gains are small but consistent.

Recommendation: accept for peer review, with revision expected.

Referee Report

4 major / 7 minor

Summary. The paper proposes a vibration-aware lidar-inertial odometry (LIO) system that assigns a point-wise "post-undistortion uncertainty" to each LiDAR point after motion undistortion. The uncertainty is modeled from angular and linear vibration intensities, computed as the mean absolute deviation of gyroscope measurements and of the filter-estimated velocity, scaled by a hand-chosen hyperparameter gamma and the time offset within the scan. This uncertainty is propagated through the point-to-plane observation model and used both to guide scan-to-map matching via Mahalanobis distances and to weight residuals in an iterated Kalman filter. The method is evaluated on a 3DoF vibration platform, several public datasets (NCD, M2DGR, Botanic Garden), and four self-recorded uneven-terrain sequences, reporting improved mean APE over Fast-LIO, Point-LIO, and other baselines in most tested scenarios.

Significance. If the uncertainty model is valid, the proposed method is a practical enhancement to Fast-LIO-style estimators in high-vibration conditions, with the advantage of real-time performance and a released dataset. The core idea of modeling post-undistortion uncertainty rather than seeking perfectly accurate undistortion is novel and well motivated, and the error-propagation chain from rotation/translation perturbations to per-point covariance (Eqs. 5-14) is clearly presented under the stated Gaussian and small-angle assumptions. The experiments cover a useful spread of platforms and environments, and the ablation study demonstrates the contribution of the uncertainty-aware components. However, the link between the heuristic vibration-intensity definition and the resulting covariance scale is not derived or externally validated, which currently limits the strength of the empirical claims.

major comments (4)

[Sec. III.B.3, Eq. (17)] The entire uncertainty model is scaled by a single hand-chosen hyperparameter gamma (set to 0.1), and the paper only asserts that "the proposed method is not sensitive to gamma" without showing a sensitivity sweep or calibrating sigma against measured undistortion errors. Since the claimed benefit of uncertainty-aware matching and residual weighting depends directly on the absolute scale of sigma, please provide experiments varying gamma (e.g., 0.01, 0.02, 0.05, 0.1, 0.2, 0.5) on at least the vibration platform and the 01-circle sequence, or alternatively calibrate gamma from ground-truth undistortion error in simulation.
[Sec. III.B.3, Eqs. (15)-(16)] The linear vibration intensity k_v is computed from the filter's own estimated velocity GvI, creating a feedback path between the state being estimated and the uncertainty used to estimate that state. The paper does not analyze this loop; a drift or bias in the velocity estimate would change k_v, hence change the point weights, and this interaction could either amplify or mask errors. Please provide either an analysis of the loop's stability or an experiment that uses a vibration-intensity estimate independent of the state estimate (e.g., from raw IMU integration alone) to break the feedback.
[Sec. III.B.3, Eq. (17)] The assumed linear growth of sigma with scan time offset delta_t is not derived from the error-propagation model of Sec. III.B.2. If the dominant source of undistortion error is integrated random-walk IMU noise, the standard deviation of the position error should grow as sqrt(delta_t), not delta_t; a scalar gamma cannot fix this misspecification because it rescales all points uniformly and cannot change the relative weighting between early and late points in a scan. Please justify the linear functional form from the IMU noise model or provide empirical evidence from simulation that the undistortion error magnitude scales linearly with the time offset.
[Abstract and Table III] The abstract and conclusion claim that the method "achieves better performance than other methods when LiDAR undergoes intense vibration," but on the Botanic Garden 1008-13 sequence the proposed method (mean/RMSE APE 0.064/0.075 m) is worse than Fast-LIO (0.054/0.059 m). Please qualify the claim to reflect that the improvement holds on most, but not all, tested high-vibration sequences, and discuss possible reasons for this counterexample (e.g., sensor characteristics, vibration frequency content, or the effect of a sparse 16-beam scanner on Mahalanobis matching).

minor comments (7)

[Sec. III.B.3] There is a typo "Equatoin (1)" and the phrase "from Equation (1)" is ambiguous because Eq. (1) defines the state vector, not the velocity estimates; please specify that GvI(i) are obtained from forward propagation of the current state estimate.
[Table II] In Table II, "Descirption" is a typo, and the standard deviations of the proposed method are larger than those of Fast-LIO in most rows (e.g., translation error for 1 Hz z-axis: 1.72 +/- 0.81 vs. 2.19 +/- 0.28); please comment on whether the reported improvements are statistically significant given the overlap.
[Table III and Sec. IV.B] The sequence names in Table III are "1018-00" and "1018-13," while the text refers to "1008-00" and "1008-13"; please unify the notation.
[Sec. IV.E, Table V] The ablation setting "w/o uncertainty" with GM enabled is unclear: if the post-undistortion uncertainty is not modeled, what covariance is used in the Mahalanobis-distance matching of Sec. III.C.2? Please clarify how the baseline with only guided matching is implemented.
[Fig. 9] The axis labels in Fig. 9 appear duplicated ("Y[m]" and "X[m]" on both subplots); please adjust the layout so each axis is labeled once.
[Sec. IV.D] The sentence "uncertainty-guided matching (needing 2-3 iterations per scan and taking a total of 22 ms on average)" is ambiguous; please state whether 22 ms is the total matching time per scan, per iteration, or averaged over all iterations.
[Footnote 1] The dataset link is a Google Drive folder; a permanent DOI or a repository identifier would be more stable and citable for a journal publication.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the odometry claim is validated against external ground truth; the unvalidated gamma scale is a robustness concern, not a circular reduction.

full rationale

The paper's load-bearing claim is that vibration-aware point-wise post-undistortion uncertainty improves LIO accuracy under intense vibration (Abstract). That claim is tested against external references: vibration-platform end-time translation/rotation errors (Table II), public-dataset APE against ground truth (Table III), and self-recorded RTK-referenced trajectories (Table IV). The uncertainty model itself is constructed from an assumed Gaussian error propagation (Eqs. 5-14) and heuristic vibration-intensity measures (Eqs. 16-17); it is not fitted to those ground-truth outcomes. The hand-set scale gamma=0.1 and the assertion of gamma-insensitivity without a sensitivity sweep are legitimate robustness concerns, as is the feedback path in Eq. (15) where the linear vibration intensity k_v uses the filter's own velocity estimate. But this is a modeling/validation weakness, not circularity: no equation makes the predicted trajectory equal to the uncertainty input by construction, and no parameter is calibrated to the evaluation metric. The only self-citations ([2], hardware design; [23], dataset) are not load-bearing for the method's derivation. Accordingly, no circular step can be exhibited under the required quote-and-reduction standard.

Assumptions & free parameters 6 free parameters · 7 assumptions · 0 invented entities

The central covariance model is assembled from a standard error-propagation skeleton plus several unverified statistical assumptions: zero-mean Gaussian vibration errors, independence of rotation/translation/measurement errors, and a heuristic mapping from velocity MAD to sigma. The only tuned numbers are gamma and matching/map hyperparameters. The method introduces no new physical entities, but the reliability of the uncertainty values is not independently validated.

free parameters (6)

gamma (uncertainty scale) = 0.1
Converts vibration intensity to per-point covariance in Eq. (17); hand-set for 100-200 Hz IMU, no sensitivity sweep reported.
K (KNN search count) = 5
Neighborhood size for plane fitting in Sec. III.C.2 and Sec. IV.D; chosen empirically and used across all experiments.
Kc (candidate count) = 10 (2K)
Number of candidates before Mahalanobis re-ranking; set to 2K in Sec. III.C.2.
ikdtree resolution = 0.5 m
Map voxel resolution for ikdtree in Sec. IV.D; affects plane-fit quality and runtime.
max iKF iterations = 4
Maximum iterated Kalman filter iterations; set for all experiments in Sec. IV.D.
scan downsampling ratio = 1/4 (~2500 points/scan)
Runtime versus accuracy trade-off in Sec. IV.D; chosen empirically.

assumptions (7)

domain assumption Vibration-induced undistortion errors are zero-mean Gaussian: delta_r ~ N(0, Sigma_r), delta_T ~ N(0, Sigma_T).
Invoked after Eq. (8) and Eq. (11); supports the covariance sum in Eq. (14), with no empirical check that residual undistortion errors are unbiased or Gaussian.
domain assumption Small-angle, first-order approximation of rotational errors: delta_R linearized, rotation order ignored, and the cross term in Eq. (13) is neglected.
Used in Eq. (6)-(7) and Eq. (13); may break under the most violent vibrations where higher-order terms are not small.
domain assumption Rotational, translational, and measurement errors are independent, so covariances add without cross terms in Eq. (14).
No statistical independence justification is provided before the covariance addition.
ad hoc to paper MAD of angular and estimated linear velocities is a valid proxy for undistortion error magnitude, with the same scale gamma for all axes.
Eq. (16)-(17) define sigma_r and sigma_T from k_omega and k_v; this is heuristic, not derived from sensor physics.
domain assumption The LiDAR measurement noise model of [18] (range and bearing Gaussian noise with sigma_d, sigma_phi) applies and the sigma values are available.
Eq. (12) imports this model; no calibration constants are reported in the paper.
domain assumption The point-to-plane residual is exactly Gaussian with scalar covariance R_j = u_j^T G Sigma_pj u_j, and the map plane is error-free.
Eq. (19)-(23) treat data association and map plane uncertainty as absent, which is optimistic during intense vibration.
ad hoc to paper The estimated linear velocity GvI from the state is accurate enough to estimate linear vibration intensity k_v.
Eq. (15) uses GvI from Eq. (1) to compute k_v, but the paper does not discuss that this velocity is itself the quantity the filter is refining.

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Cite this review

Pith. "Pith review of Vibration-aware Lidar-Inertial Odometry based on Point-wise Post-Undistortion Uncertainty." pith.science (2026). https://pith.science/paper/AHMNZIOY

@misc{pith2026250704311,
  author       = {Pith},
  title        = {Pith review of: Vibration-aware Lidar-Inertial Odometry based on Point-wise Post-Undistortion Uncertainty},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AHMNZIOY}},
  note         = {Machine review of arXiv:2507.04311}
}

read the original abstract

High-speed ground robots moving on unstructured terrains generate intense high-frequency vibrations, leading to LiDAR scan distortions in Lidar-inertial odometry (LIO). Accurate and efficient undistortion is extremely challenging due to (1) rapid and non-smooth state changes during intense vibrations and (2) unpredictable IMU noise coupled with a limited IMU sampling frequency. To address this issue, this paper introduces post-undistortion uncertainty. First, we model the undistortion errors caused by linear and angular vibrations and assign post-undistortion uncertainty to each point. We then leverage this uncertainty to guide point-to-map matching, compute uncertainty-aware residuals, and update the odometry states using an iterated Kalman filter. We conduct vibration-platform and mobile-platform experiments on multiple public datasets as well as our own recordings, demonstrating that our method achieves better performance than other methods when LiDAR undergoes intense vibration.

Figures

Figures reproduced from arXiv: 2507.04311 by the authors.

**Figure 2.** Overview of the proposed LIO system. The core of this system is that each point in a scan is assigned a point-wise post-undistortion uncertainty. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Post-undistortion uncertainty caused by rotational vibration. During a [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (6 more)

**Figure 5.** Figure 5: Demonstration of the uncertainty-guided matching process. When [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]

**Figure 6.** Figure 6: 3DoF vibration platform experiments. (a) The 3DoF vibration platform. (b) Different vibrations of the platform. (c) and [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

**Figure 7.** Figure 7: The estimated motion of Fast-LIO and our method in the vibration [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]

**Figure 8.** Figure 8: The platform and scene in our uneven terrain experiment. (a) Our wheeled platform. (b) A bird-eye view of the whole scene. (c) The elevation map [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

**Figure 9.** Figure 9: Trajectories of different methods on our [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]

**Figure 10.** Figure 10: The execution process of the proposed method. (a) The trajectory [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]

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Reference graph

Works this paper leans on

24 extracted references · 20 canonical work pages

[1]

Into the robotic depths: analysis and insights from the darpa subterranean challenge,

T. H. Chung, V . Orekhov, and A. Maio, “Into the robotic depths: analysis and insights from the darpa subterranean challenge,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 6, no. 1, pp. 477–502, 2023

work page 2023
[2]

Design of a variable wheel-propeller integrated mechanism for amphibious robots,

L. Lu, X. Gao, M. Xiang, Z. Yan, and B. Han, “Design of a variable wheel-propeller integrated mechanism for amphibious robots,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024, pp. 3519–3525

work page 2024
[3]

Cerberus in the darpa subterranean challenge,

M. Tranzatto, T. Miki, M. Dharmadhikari, L. Bernreiter, M. Kulkarni, F. Mascarich, O. Andersson, S. Khattak, M. Hutter, R. Siegwart et al., “Cerberus in the darpa subterranean challenge,” Science Robotics, vol. 7, no. 66, 2022

work page 2022
[4]

Loam: Lidar odometry and mapping in real- time,

J. Zhang and S. Singh, “Loam: Lidar odometry and mapping in real- time,” in Proceedings of Robotics: Science and Systems (RSS ’14), vol. 2, no. 9, July 2014, pp. 1–9

work page 2014
[5]

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 , vol. 38, no. 4, pp. 2053–2073, 2022

2022
[6]

Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping,

T. Shan, B. Englot, D. Meyers, W. Wang, C. Ratti, and D. Rus, “Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 5135–5142

2020
[7]

Coco-lic: Continuous-time tightly-coupled lidar-inertial-camera odometry using non-uniform b-spline,

X. Lang, C. Chen, K. Tang, Y . Ma, J. Lv, Y . Liu, and X. Zuo, “Coco-lic: Continuous-time tightly-coupled lidar-inertial-camera odometry using non-uniform b-spline,” IEEE Robotics and Automation Letters , vol. 8, no. 11, pp. 7074–7081, 2023

work page 2023
[8]

Motion distortion elimination for lidar-inertial odometry under rapid motion conditions,

J. Shi, W. Wang, X. Li, Y . Yan, and E. Yin, “Motion distortion elimination for lidar-inertial odometry under rapid motion conditions,” IEEE Transactions on Instrumentation and Measurement , vol. 72, pp. 1–16, 2023

work page 2023

Show all 24 references

[9]

Ensemble kalman filter based lidar odometry for skewed point clouds using scan slicing,

Y . Choe, J. H. Jung, and C. G. Park, “Ensemble kalman filter based lidar odometry for skewed point clouds using scan slicing,” in 2022 International Conference on Robotics and Automation (ICRA) , 2022, pp. 3677–3683

2022
[10]

Clins: Continuous- time trajectory estimation for lidar-inertial system,

J. Lv, K. Hu, J. Xu, Y . Liu, X. Ma, and X. Zuo, “Clins: Continuous- time trajectory estimation for lidar-inertial system,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021, pp. 6657–6663

2021
[11]

Ct-icp: Real-time elastic lidar odometry with loop closure,

P. Dellenbach, J.-E. Deschaud, B. Jacquet, and F. Goulette, “Ct-icp: Real-time elastic lidar odometry with loop closure,” in 2022 Interna- tional Conference on Robotics and Automation (ICRA), 2022, pp. 5580– 5586

2022
[12]

Developing and validating a predictive model of measurement uncertainty for multi-beam lidars: Application to the velodyne vlp-16,

Q. P ´entek, T. Allouis, O. Strauss, and C. Fiorio, “Developing and validating a predictive model of measurement uncertainty for multi-beam lidars: Application to the velodyne vlp-16,” in 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (...

2018
[13]

Sensitivity of tree volume measurement to trajectory errors from a terrestrial lidar scanner,

T. Palleja, M. Tresanchez, M. Teixido, R. Sanz, J. Rosell, and J. Palacin, “Sensitivity of tree volume measurement to trajectory errors from a terrestrial lidar scanner,” Agricultural and Forest Meteorology, vol. 150, no. 11, pp. 1420–1427, 2010

2010
[14]

Cta-lo: Accurate and robust lidar odometry using continuous-time adaptive estimation,

Y . Lv, Y . Zhang, X. Zhao, W. Li, J. Ning, and Y . Jin, “Cta-lo: Accurate and robust lidar odometry using continuous-time adaptive estimation,” in 2024 IEEE International Conference on Robotics and Automation (ICRA), 2024, pp. 12 034–12 040

2024
[15]

Point-lio: Robust high-bandwidth light detection and ranging inertial odometry,

D. He, W. Xu, N. Chen, F. Kong, C. Yuan, and F. Zhang, “Point-lio: Robust high-bandwidth light detection and ranging inertial odometry,” Advanced Intelligent Systems , vol. 5, no. 7, 2023

2023
[16]

Analyzing lidar scan skewing and its impact on scan matching,

A. Al-Nuaimi, W. Lopes, P. Zeller, A. Garcea, C. Lopes, and E. Stein- bach, “Analyzing lidar scan skewing and its impact on scan matching,” in 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2016, pp. 1–8

2016
[17]

Lidar scan registration robust to extreme motions,

S.-P. Desch ˆenes, D. Baril, V . Kubelka, P. Gigu `ere, and F. Pomerleau, “Lidar scan registration robust to extreme motions,” in 2021 18th Conference on Robots and Vision (CRV) , 2021, pp. 17–24

2021
[18]

Efficient and probabilistic adaptive voxel mapping for accurate online lidar odometry,

C. Yuan, W. Xu, X. Liu, X. Hong, and F. Zhang, “Efficient and probabilistic adaptive voxel mapping for accurate online lidar odometry,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 8518–8525, 2022

2022
[19]

i-octree: A fast, lightweight, and dynamic octree for proximity search,

J. Zhu, H. Li, Z. Wang, S. Wang, and T. Zhang, “i-octree: A fast, lightweight, and dynamic octree for proximity search,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024, pp. 12 290–12 296

2024
[20]

ikd-tree: An incremental k-d tree for robotic applications,

Y . Cai, W. Xu, and F. Zhang, “ikd-tree: An incremental k-d tree for robotic applications,” 2021. [Online]. Available: https: //arxiv.org/abs/2102.10808

2021 arXiv
[21]

The newer college dataset: Handheld lidar, inertial and vision with ground truth,

M. Ramezani, Y . Wang, M. Camurri, D. Wisth, M. Mattamala, and M. Fallon, “The newer college dataset: Handheld lidar, inertial and vision with ground truth,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 4353–4360

2020
[22]

M2dgr: A multi-sensor and multi-scenario slam dataset for ground robots,

J. Yin, A. Li, T. Li, W. Yu, and D. Zou, “M2dgr: A multi-sensor and multi-scenario slam dataset for ground robots,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 2266–2273, 2022

2022
[23]

Botanicgarden: A high-quality dataset for robot navigation in unstructured natural environments,

Y . Liu, Y . Fu, M. Qin, Y . Xu, B. Xu, F. Chen, B. Goossens, P. Z. Sun, H. Yu, C. Liu, L. Chen, W. Tao, and H. Zhao, “Botanicgarden: A high-quality dataset for robot navigation in unstructured natural environments,” IEEE Robotics and Automation Letters , vol. 9, no. 3, pp. 27...

2024
[24]

Tightly coupled 3d lidar inertial odometry and mapping,

H. Ye, Y . Chen, and M. Liu, “Tightly coupled 3d lidar inertial odometry and mapping,” in 2019 International Conference on Robotics and Automation (ICRA), 2019, p. 3144–3150

2019

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Into the robotic depths: analysis and insights from the darpa subterranean challenge,

T. H. Chung, V . Orekhov, and A. Maio, “Into the robotic depths: analysis and insights from the darpa subterranean challenge,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 6, no. 1, pp. 477–502, 2023

work page 2023

[2] [2]

Design of a variable wheel-propeller integrated mechanism for amphibious robots,

L. Lu, X. Gao, M. Xiang, Z. Yan, and B. Han, “Design of a variable wheel-propeller integrated mechanism for amphibious robots,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2024, pp. 3519–3525

work page 2024

[3] [3]

Cerberus in the darpa subterranean challenge,

M. Tranzatto, T. Miki, M. Dharmadhikari, L. Bernreiter, M. Kulkarni, F. Mascarich, O. Andersson, S. Khattak, M. Hutter, R. Siegwart et al., “Cerberus in the darpa subterranean challenge,” Science Robotics, vol. 7, no. 66, 2022

work page 2022

[4] [4]

Loam: Lidar odometry and mapping in real- time,

J. Zhang and S. Singh, “Loam: Lidar odometry and mapping in real- time,” in Proceedings of Robotics: Science and Systems (RSS ’14), vol. 2, no. 9, July 2014, pp. 1–9

work page 2014

[5] [5]

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 , vol. 38, no. 4, pp. 2053–2073, 2022

2022

[6] [6]

Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping,

T. Shan, B. Englot, D. Meyers, W. Wang, C. Ratti, and D. Rus, “Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 5135–5142

2020

[7] [7]

Coco-lic: Continuous-time tightly-coupled lidar-inertial-camera odometry using non-uniform b-spline,

X. Lang, C. Chen, K. Tang, Y . Ma, J. Lv, Y . Liu, and X. Zuo, “Coco-lic: Continuous-time tightly-coupled lidar-inertial-camera odometry using non-uniform b-spline,” IEEE Robotics and Automation Letters , vol. 8, no. 11, pp. 7074–7081, 2023

work page 2023

[8] [8]

Motion distortion elimination for lidar-inertial odometry under rapid motion conditions,

J. Shi, W. Wang, X. Li, Y . Yan, and E. Yin, “Motion distortion elimination for lidar-inertial odometry under rapid motion conditions,” IEEE Transactions on Instrumentation and Measurement , vol. 72, pp. 1–16, 2023

work page 2023