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REVIEW 5 major objections 5 minor 68 references

DRO: Doppler-Aware Direct Radar Odometry

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

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Pith's one-line read Radar odometry can run directly on the full intensity image, with Doppler and motion distortion corrected inside the optimization, and this beats point-based radar odometry.

desk verdict Genuine advance in direct radar odometry with a clean Doppler-aware formulation; the main claims hold, but the headline tunnel/skyway result rests on a Doppler bias correction that needs more evidence. read the letter →

arxiv 2504.20339 v1 pith:XXJY76SX submitted 2025-04-29 cs.RO cs.CV

Cedric Le Gentil , Leonardo Brizi , Daniil Lisus , Xinyuan Qiao , Giorgio Grisetti , Timothy D. Barfoot This is my paper · ORCID
classification cs.ROcs.CV
keywords radarodometrydirectregistrationDopplerdistortionmotioncontinuous-timetrajectoryFMCWGaussianprocessinfillgyroscopepreintegration
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 proposes a new way to estimate a robot's motion from a spinning millimetre-wave radar. Instead of extracting sparse points or features, the method registers the full radar intensity image directly against a local map, while correcting Doppler and motion distortion inside a continuous-time trajectory estimate. With a triangular frequency modulation that makes radial Doppler velocities observable, an additional Doppler objective lets the system estimate velocity even in geometrically featureless tunnels. The reported evidence is over 250 km of on-road data plus off-road runs, with an average relative translation error of 0.26% on the Boreas leaderboard when a gyroscope is used, falling to 0.18% when the Doppler constraint is active. If correct, the direct use of all radar information is not only simpler than point-based radar odometry but more accurate in exactly the geometries where points run out.

What carries the argument

The load-bearing object is a pair of objective functions optimized by gradient ascent. The first, $\mathcal{O}_\psi$, is the summed product of measured intensity $\psi_{nm}$ and local-map intensity queried at the motion- and Doppler-corrected position of each range cell. The second, $\mathcal{O}_d$, applies the range shift $\Delta r_n = \beta\,[\cos\alpha_n,\ \sin\alpha_n]^\top R^\top v$ to the Gaussian-process-infilled up-chirp image and correlates it with the down-chirp image, so that maximizing it recovers the body-centric velocity. The continuous trajectory connects the two objectives: orientation comes from gyroscope preintegration (or a constant angular rate), and translation comes from a constant body-centric velocity model integrated over the scan. The Gaussian process infill for interleaved chirp directions is what makes the Doppler comparison valid without assuming consecutive beams see the same object.

What would settle it

Drive a car with centimetre-level ground truth suddenly from an open road into a long featureless tunnel using Doppler-only estimation. If the estimated sideways velocity jumps or drifts faster than the paper's low-pass filter can follow it, and the odometry error grows sharply, the single-row/single-instant radar model is the weak link.

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Extended reading notes

Core claim

The central claim is that radar odometry can be formulated as a continuous, gradient-based optimization over the full radar intensity image, with both motion distortion and Doppler-induced range shifts corrected inside the cost rather than in a pre-processing step. The paper introduces two cross-correlation objectives: direct scan-to-local-map intensity registration, and, for triangular-chirp radars, a Doppler velocity constraint that compares Gaussian-process-infilled up-chirp and down-chirp images shifted by the range shift $\Delta r_n = \beta\,[\cos\alpha_n,\ \sin\alpha_n]^\top R^\top v$. This makes DRO, in the authors' words, the first direct method that accounts in a principled way for both distortions, and it is what lets the system estimate velocity purely from Doppler information in environments where geometry gives almost no constraint, such as a featureless tunnel. The supporting evidence is a set of benchmarks over more than 250 km of driving plus off-road runs, with headline numbers of 0.26% relative translation error on Boreas and 0.18% when the Doppler constraint is available.

Load-bearing premise

The load-bearing premise is that each row of radar data corresponds to one beam direction at one instant, and that the interpolated up-chirp and down-chirp views show the same scene; because a real spinning radar sweeps continuously, this mismatch shows up as a velocity bias that the online correction may not track quickly enough.

Editorial extensions

If this is right

  • On commercial sawtooth-pattern radars, direct registration alone reaches 0.26% average relative translation error on the Boreas odometry leaderboard, ahead of point-extraction methods.
  • When the radar emits triangular chirps, the Doppler objective keeps relative translation error at about 0.34% in a featureless tunnel where continuous-time point-based radar registration fails at roughly 35%.
  • Orientation from gyroscope integration stays accurate even where radar returns are weak, and the paper reports that adding radar constraints to inertial methods can actually worsen rotation estimates.
  • The full Doppler-aided pipeline runs in about 89 ms per 250 ms radar frame on a laptop GPU, so the extra processing does not prevent real-time use.
  • Because the method uses all intensity information, it avoids feature extraction, data association, and hand-tuned discretization of the search space.

Reading between the lines

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

  • Editorial inference: the Gaussian-process infill trick that makes interleaved up-chirp and down-chirp rows comparable is a general mechanism; any sensor with two interleaved measurement patterns could use it to recover a velocity-dependent shift without explicit data association.
  • Editorial inference: the observation that radar-based orientation constraints degrade rotation estimates relative to pure gyroscope integration suggests that, for low-resolution spinning radars, orientation is best left to the inertial sensor while radar constrains translation and velocity only.
  • Editorial inference: a testable extension is applying the direct Doppler objective to 3D FMCW radars with dense beams; if the single-azimuth-per-row assumption is the main source of the residual velocity bias, per-beam timing in 3D data should reduce that bias.
  • Editorial inference: the environment-dependent lateral velocity bias, only partially corrected by a low-pass filter, is the most likely bottleneck; replacing it with a model-based or consistency-based correction could push the reported error below the 0.18% level.
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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

5 major / 5 minor

Summary. The paper proposes DRO, a direct SE(2) radar odometry method for spinning FMCW radars. Instead of extracting features or point clouds, DRO registers the full radar intensity image against an on-the-fly local map, using a locally continuous trajectory model that corrects both motion distortion and Doppler-induced range shifts. A second objective function (Od, Eq. 13) exploits triangular frequency modulation by GP-infilling alternating up- and down-chirp azimuths and cross-correlating the two images after a velocity-dependent Doppler shift. Orientation is obtained either from a constant angular velocity model or from gyroscope preintegration. The method is evaluated on a new automotive dataset (Suburbs, Highway, Tunnel, Skyway), on the Boreas and MulRan public benchmarks, and on an off-road dataset, with ablations and timing results. The paper reports state-of-the-art performance, including 0.26% average translation error on the Boreas leaderboard when aided by a gyroscope, and claims that the Doppler constraint enables odometry in feature-deprived tunnels and skyways.

Significance. If the reported results hold, this is a significant contribution to radar odometry. The direct, feature-free formulation with continuous-time motion and Doppler distortion correction is a principled departure from point-cloud-based methods, and the Doppler velocity objective (Eqs. 5-13) is derived clearly and without assuming that consecutive radar beams observe identical objects. The paper also provides substantial experimental evidence: more than 250 km of on-road data, public benchmarks, off-road validation, ablation studies, sensitivity analysis, and a real-time GPU implementation with public code. These strengths are real: the work addresses a known weakness of radar odometry in geometrically degenerate environments, and the evaluation is broader than in most related papers. However, several load-bearing points need attention before the claims can be fully accepted, in particular the consistency of the headline 0.18% number, the correctness of the printed gyroscope preintegration formula, and the validation of the online Doppler-bias correction in exactly the Tunnel/Skyway regimes where the Doppler objective carries the solution.

major comments (5)
  1. [Abstract and Table I] The abstract states that when using data with the Doppler-enabling frequency modulation, the translation error is reduced to 0.18% in similar environments. This number does not appear in Table I for any DRO-GD configuration: the DRO-GD entries are 0.19% (Suburbs), 0.24% (Highway), 0.34% (Tunnel), and 0.40% (Skyway). The only 0.18% entry in Table I is DRO-G in Suburbs, which does not use the Doppler objective Od. Please reconcile the abstract with the reported results, or state explicitly which sequences and configuration produce the 0.18% value.
  2. [Eq. (15), Section IV-D2] The gyroscope preintegration formula appears incorrect as written. For a constant angular accel? Actually linearly interpolated angular rate ω(s) between ω_i and ω_{i+1}, the exact integral from t_i to t is θ_i + ω_i (t-t_i) + (ω_{i+1}-ω_i)(t-t_i)^2/(2(t_{i+1}-t_i)). The printed expression θ(t_i) + (ω_{i+1}+ω_i)(t-t_i)/(2(t_{i+1}-t_i)) + ω_i(t-t_i) is dimensionally inconsistent: the first term has units of angular rate, not angle, and it does not evaluate to the trapezoidal integral at t=t_{i+1}. Since orientation integration is a central component of the gyro-aided results, please correct Eq. (15) and confirm that the implementation matches the corrected formula.
  3. [Section V-A4 and Appendix B] The claim that DRO-GD enables odometry in feature-deprived Tunnel and Skyway scenarios rests on the Doppler objective Od, because DRO-G alone fails there (Table I: 7.12% and 45.76%). The paper acknowledges a residual Doppler velocity bias 'partly explained by the disparity between the actual measurement process and the simple measurement model that assumes the information contained in a row of the radar data corresponds to a single azimuth/timestamp' and shows in Appendix B that the bias is environment-dependent. Only the lateral component is corrected, by a low-pass filter with no stated time constant or convergence guarantee, and the ablation study in Table II excludes Skyway and reports velocity RMSE rather than bias residual. Please provide evidence that the bias correction converges and tracks the environment-dependent bias in Tunnel/Skyway (e.g., bias residual against ground-truth velocity, sensitivity to the filter time constant, or a bound on the resulting error in Eq. (13)), or discuss why the remaining bias cannot corrupt the registration that supports the headline claim.
  4. [Section IV-C] The Doppler objective assumes that, after GP infilling, the up-chirp and down-chirp images observe exactly the same geometry. This is a modeling simplification: the infilled rows are constructed from measurements taken at different azimuths and timestamps, and the mismatch will grow with radar rotation rate, range, and vehicle speed. The paper acknowledges a related limitation in Section V-A4 but does not analyze how the interpolation error affects Od or the estimated velocity. A simulation or synthetic-shift experiment varying the infill neighborhood (U x V), rotation rate, and velocity would make the validity domain of this assumption explicit.
  5. [Section V-D2, MulRan] The MulRan evaluation applies post-hoc corrections: a 50 ms IMU timestamp offset removed from all sequences and a 0.172 deg radar heading adjustment. The text reports that without the heading correction DRO-G gives 1.46% and 0.38 deg/100m, while with the correction the table shows 1.45% and 0.38 deg/100m. It is not stated whether these corrections were selected on the test sequences themselves or on held-out calibration data. Please state the selection protocol explicitly; if the corrections were tuned on the evaluation sequences, the comparison to CFEAR is not fully apples-to-apples and the interpretation of the MulRan results should be softened accordingly.
minor comments (5)
  1. [Table I caption] The caption says the best radar-based method is in bold, but no bold entries appear in the table as rendered; please add the emphasis or remove the statement.
  2. [Section V-C4 and Fig. 8] The ablation study states that Skyway sequences were left out due to their extreme challenge, yet Fig. 8 (sensitivity to gamma) includes a Skyway curve. Please clarify whether Skyway was included in the sensitivity analysis or adjust the text.
  3. [Eq. (18)] The robust weighting formula rho = (|psi - Gamma| - 1)^6 gives weight 1 for exact agreement, zero near absolute difference 1, and large weights again for differences greater than 1 (which can occur if intensities are not strictly normalized). Please clarify the intended behavior and, if intensities are normalized to [0,1], state this explicitly or correct the formula.
  4. [Table II] The 'No local map' ablation shows a lower velocity RMSE (0.107 m/s) than the full DRO-GD (0.119 m/s), which is counterintuitive; a one-sentence explanation would help the reader interpret the ablation.
  5. [Section V-A4] The low-pass filter used for lateral velocity bias estimation is described qualitatively; please report its time constant or update rule and the velocity threshold value used in the release.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the two objective functions are cross-correlations against raw sensor data, the Doppler scale comes from sensor parameters, and the online bias filters are disclosed calibration aids rather than fitted predictions.

full rationale

The paper's derivation chain is self-contained and does not reduce to its own inputs. The direct intensity objective Oψ (Eq. 11) is a cross-correlation between Doppler- and motion-corrected radar intensities and a local map built from past radar data; no fitted constant is renamed as a prediction. The Doppler objective Od (Eq. 13) uses the range shift Δr_n from Eq. 10, whose scale β is derived in Eq. 7 from the physical FMCW parameters (chirp slope, wavelength, speed of light), not estimated from the data. The GP infill step is a preprocessing interpolation, and the assumption that I↑ and I↓ observe the same geometry is an explicit modeling choice, not an equation that presupposes the velocity output. The reported lateral-velocity bias filter and gyroscope-bias heuristic (Sections V-A4 and IV-D2) are disclosed online calibration mechanisms, ablation-tested, and not presented as derived predictions. Comparisons on Boreas and MulRan are external benchmarks, and the central claim does not rest on a self-citation chain; the citations to [49] for row filtering and for empirically observing a velocity bias are non-load-bearing. No equation in the paper is equivalent by construction to a fitted parameter or to the claimed result.

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

The method rests on standard GP regression, a simplified radar Doppler model, a constant body-centric velocity motion model over each 250 ms scan, gyroscope integration for orientation, and two ad-hoc mechanisms: the non-slip-based online lateral velocity bias filter and the rare-frame robust reweighting. Free parameters are limited to user-chosen gains and thresholds; no new physical entities are introduced.

free parameters (5)
  • Local map update gain gamma = 0.1
    User-defined in Eq. (12); sensitivity analysis (Fig. 8) shows low sensitivity, but it is still a hand-chosen parameter affecting the map update.
  • Gradient ascent step c = 0.1 initial, halved on non-ascent
    Eq. (17) optimization hyperparameter; no automatic schedule other than halving.
  • Gyro bias velocity threshold = 5 cm/s
    Threshold under which gyro measurements are averaged and low-pass filtered to estimate bias (Section IV-D2).
  • Robust weighting parameters = rho = (|psi - Gamma| - 1)^6
    Ad-hoc weighting function in Eq. (18) used only on rare outlier frames; the exponent and offset are chosen by hand.
  • GP inference neighborhood (U x V) and kernel hyperparameters = not specified
    Section V-A1 mentions a UxV neighborhood for efficient GP regression but does not state U, V, or the kernel length scale and signal variance; these affect the infilled images.
assumptions (6)
  • standard math Gaussian process regression formulas (Eqs. 1-2) from Rasmussen and Williams
    Background interpolation method used to infill up-chirp and down-chirp radar rows.
  • domain assumption Doppler range-shift model (Eq. 7) with constant beta for triangular FMCW
    Assumes ideal linear up and down chirps; the velocity bias section shows real-world deviations from this model.
  • domain assumption Constant body-centric velocity during a radar scan (Section IV-D3)
    Motion model used to compute position from velocity; reasonable for 250 ms scans but violated in rapid maneuvers.
  • domain assumption Gyroscope integration provides orientation; bias estimation via static periods (Section IV-D2)
    The method's orientation relies on gyro integration; the limitations section states catastrophic failure if gyro degrades.
  • ad hoc to paper Up-chirp and down-chirp images after GP infill observe the same geometry (Section IV-C)
    This enables the Doppler objective; not guaranteed in environments with high-frequency reflectivity variation.
  • ad hoc to paper Online lateral velocity bias filter uses non-slip kinematics (Section V-A4)
    The non-slip assumption is used to estimate and correct bias; authors note it is not strictly enforced due to the low-pass filter.

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Cite this review

Pith. "Pith review of DRO: Doppler-Aware Direct Radar Odometry." pith.science (2026). https://pith.science/paper/XXJY76SX

@misc{pith2026250420339,
  author       = {Pith},
  title        = {Pith review of: DRO: Doppler-Aware Direct Radar Odometry},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XXJY76SX}},
  note         = {Machine review of arXiv:2504.20339}
}
read the original abstract

A renaissance in radar-based sensing for mobile robotic applications is underway. Compared to cameras or lidars, millimetre-wave radars have the ability to `see' through thin walls, vegetation, and adversarial weather conditions such as heavy rain, fog, snow, and dust. In this paper, we propose a novel SE(2) odometry approach for spinning frequency-modulated continuous-wave radars. Our method performs scan-to-local-map registration of the incoming radar data in a direct manner using all the radar intensity information without the need for feature or point cloud extraction. The method performs locally continuous trajectory estimation and accounts for both motion and Doppler distortion of the radar scans. If the radar possesses a specific frequency modulation pattern that makes radial Doppler velocities observable, an additional Doppler-based constraint is formulated to improve the velocity estimate and enable odometry in geometrically feature-deprived scenarios (e.g., featureless tunnels). Our method has been validated on over 250km of on-road data sourced from public datasets (Boreas and MulRan) and collected using our automotive platform. With the aid of a gyroscope, it outperforms state-of-the-art methods and achieves an average relative translation error of 0.26% on the Boreas leaderboard. When using data with the appropriate Doppler-enabling frequency modulation pattern, the translation error is reduced to 0.18% in similar environments. We also benchmarked our algorithm using 1.5 hours of data collected with a mobile robot in off-road environments with various levels of structure to demonstrate its versatility. Our real-time implementation is publicly available: https://github.com/utiasASRL/dro.

Figures

Figures reproduced from arXiv: 2504.20339 by the authors.

Figure 1
Figure 1. This paper presents a Direct Radar Odometry (DRO) method [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Impact of different frequency modulation patterns (top) and Doppler [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Overview diagram of the proposed direct radar odometry method. To leverage the Doppler-based velocity constraint, the radar must use a triangular [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Illustration of the proposed infilling. The raw Doppler distorted radar data (left) is split between up and down-chirp azimuths. Gaussian Process (GP) [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Illustration of the GP-based infill highlighting the Doppler effect on [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Illustration of a challenging situation with a line of vehicles coming [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Trajectory estimate samples for the different sequence types. [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Sensitivity analysis of DRO-GD with respect to the local map update [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: Visualization of trajectory estimates in the off-road scenarios (aligned with the ground-truth for visualization using the first 20% of the trajectory). [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: Lateral velocity bias estimate obtained by running DRO-GD on [PITH_FULL_IMAGE:figures/full_fig_p016_10.png]

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Reference graph

Works this paper leans on

68 extracted references · 37 canonical work pages

  1. [1]

    Abu-Alrub and Nathir A

    Nader J. Abu-Alrub and Nathir A. Rawashdeh. Radar odometry for autonomous ground vehicles: A survey of methods and datasets. IEEE Trans. on Intelligent Vehicles (T-IV), 9(3):4275–4291, 2024. doi: 10.1109/TIV .2023. 3340513

    doi:10.1109/tiv 2024

  2. [2]

    Lilienthal, and Henrik Andreasson

    Daniel Adolfsson, Martin Magnusson, Anas Alhashimi, Achim J. Lilienthal, and Henrik Andreasson. Cfear radarodometry - conservative filtering for efficient and accurate radar odometry. In Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) , pages 5462–5469, 2021. doi: 10.1109/IROS51168.2021. 9636253

    arXiv 2021

  3. [3]

    Lilienthal, and Henrik Andreasson

    Daniel Adolfsson, Martin Magnusson, Anas Alhashimi, Achim J. Lilienthal, and Henrik Andreasson. Lidar-level localization with radar? the cfear approach to accurate, fast, and robust large-scale radar odometry in diverse environments. IEEE Trans. on Robotics (TRO) , 39(2): 1476–1495, 2023. doi: 10.1109/TRO.2022.3221302. −150 −100 −50 0 50 100 x [m] −80 −60...

    arXiv 2023

  4. [4]

    Fast radar motion estimation with a learnt focus of attention using weak supervision

    Roberto Aldera, Daniele De Martini, Matthew Gadd, and Paul Newman. Fast radar motion estimation with a learnt focus of attention using weak supervision. In Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), pages 1190–1196, 2019. doi: 10.1109/ICRA. 2019.8794014

    arXiv 2019

  5. [5]

    Ransac for motion-distorted 3d visual sensors

    Sean Anderson and Timothy D Barfoot. Ransac for motion-distorted 3d visual sensors. In Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pages 2093–2099. IEEE, 2013

    work page 2013

  6. [6]

    Sean Anderson and Timothy D. Barfoot. Full steam ahead: Exactly sparse gaussian process regression for batch continuous-time trajectory estimation on se(3). In Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) , pages 157–164, 2015. doi: 10.1109/IROS.2015.7353368

    work page arXiv 2015

  7. [7]

    Barfoot, Chi Hay Tong, and Simo S¨arkk¨a

    Sean Anderson, Timothy D. Barfoot, Chi Hay Tong, and Simo S¨arkk¨a. Batch nonlinear continuous-time trajectory estimation as exactly sparse gaussian process regression. Autonomous Robots, 39(3):221–238, 2015. ISSN 0929-

    work page 2015

  8. [8]

    Exactly sparse gaussian variational inference with appli- cation to derivative-free batch nonlinear state estimation

    Timothy D Barfoot, James R Forbes, and David J Yoon. Exactly sparse gaussian variational inference with appli- cation to derivative-free batch nonlinear state estimation. Intl. Journal of Robotics Research (IJRR) , 39(13):1473– 1502, 2020. doi: 10.1177/0278364920937608

    doi:10.1177/0278364920937608 2020

Show all 68 references
  1. [9]

    Under the radar: Learn- ing to predict robust keypoints for odometry estimation and metric localisation in radar

    Dan Barnes and Ingmar Posner. Under the radar: Learn- ing to predict robust keypoints for odometry estimation and metric localisation in radar. In Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , 2020

    2020

  2. [10]

    Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information

    Dan Barnes, Rob Weston, and Ingmar Posner. Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information. In Conference on Robot Learning (CoRL), 2019. URL https://arxiv.org/pdf/1909.03752

    2019 arXiv

  3. [11]

    S. Blake. Os-cfar theory for multiple targets and nonuniform clutter. IEEE Trans. on Aerospace and Electronic Systems (T-AES) , 24(6):785–790, 1988. doi: 10.1109/7.18645

    1988 doi

  4. [12]

    Map matching and data association for large-scale two-dimensional laser scan- based slam

    Michael Bosse and Robert Zlot. Map matching and data association for large-scale two-dimensional laser scan- based slam. Intl. Journal of Robotics Research (IJRR) , 27:667–691, 06 2008. doi: 10.1177/0278364908091366

    2008 doi

  5. [13]

    Burnett, Angela P

    K. Burnett, Angela P. Schoellig, and Timothy D. Barfoot. Do we need to compensate for motion distortion and doppler effects in spinning radar navigation? IEEE Robotics and Automation Letters (RA-L) , 6(2):771–778,

  6. [14]

    Radar Odometry Combining Prob- abilistic Estimation and Unsupervised Feature Learning

    Keenan Burnett, David J Yoon, Angela P Schoellig, and Timothy D Barfoot. Radar Odometry Combining Prob- abilistic Estimation and Unsupervised Feature Learning. In Proc. of Robotics: Science and Systems (RSS) , 2021

    2021

  7. [15]

    Are We Ready for Radar to Replace Lidar in All-weather Mapping and Localization? IEEE Robotics and Automation Letters (RA-L), 7(4):10328–10335, 2022

    Keenan Burnett, Yuchen Wu, David J Yoon, Angela P Schoellig, and Timothy D Barfoot. Are We Ready for Radar to Replace Lidar in All-weather Mapping and Localization? IEEE Robotics and Automation Letters (RA-L), 7(4):10328–10335, 2022

    2022

  8. [16]

    Boreas: A multi- season autonomous driving dataset

    Keenan Burnett, David J Yoon, Yuchen Wu, Andrew Z Li, Haowei Zhang, Shichen Lu, Jingxing Qian, Wei-Kang Tseng, Andrew Lambert, Keith YK Leung, Angela P Schoellig, and Timothy D Barfoot. Boreas: A multi- season autonomous driving dataset. Intl. Journal of Robotics Research (IJR...

    2023 doi

  9. [17]

    Schoellig, and Timothy D

    Keenan Burnett, Angela P. Schoellig, and Timothy D. Barfoot. Continuous-time radar-inertial and lidar-inertial odometry using a gaussian process motion prior. IEEE Trans. on Robotics (TRO) , 2024

    2024

  10. [18]

    Cadena, L

    C. Cadena, L. Carlone, H. Carrillo, Y . Latif, D. Scara- muzza, J. Neira, I. Reid, and J.J. Leonard. Past, present, and future of simultaneous localization and mapping: Towards the robust-perception age. IEEE Trans. on Robotics (TRO), 32(6):1309–1332, 2016

    2016

  11. [19]

    Radar slam us- ing visual features

    Jonas Callmer, David T ¨ornqvist, Fredrik Gustafsson, Henrik Svensson, and Pelle Carlbom. Radar slam us- ing visual features. EURASIP Journal on Advances in Signal Processing , 2011, 12 2011. doi: 10.1186/ 1687-6180-2011-71

    2011

  12. [20]

    Cen and Paul Newman

    Sarah H. Cen and Paul Newman. Precise ego-motion estimation with millimeter-wave radar under diverse and challenging conditions. In Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , pages 6045–6052, 2018. doi: 10.1109/ICRA.2018.8460687

    2018

  13. [21]

    Cen and Paul Newman

    Sarah H. Cen and Paul Newman. Radar-only ego-motion estimation in difficult settings via graph matching. In Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), pages 298–304, 2019. doi: 10.1109/ICRA.2019. 8793990

    2019 doi

  14. [22]

    Radar scan matching slam using the fourier-mellin transform

    Paul Checchin, Franck G ´erossier, Christophe Blanc, Roland Chapuis, and Laurent Trassoudaine. Radar scan matching slam using the fourier-mellin transform. In Springer Tracts in Advanced Robotics, volume 62, pages 151–161, 01 2009. ISBN 978-3-642-13407-4. doi: 10.1007/978-3-64...

    2009 doi

  15. [23]

    Stachniss, and Giorgio Grisetti

    Bartolomeo Della Corte, Igor Bogoslavskyi, C. Stachniss, and Giorgio Grisetti. A general framework for flexible multi-cue photometric point cloud registration. Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , pages 1–8, 2017. URL https://api.semanticscholar.org/...

    2017

  16. [24]

    Photometric bundle adjustment for dense multi-view 3d modeling

    Ama ¨el Delaunoy and Marc Pollefeys. Photometric bundle adjustment for dense multi-view 3d modeling. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , pages 1486–1493, 2014. doi: 10.1109/CVPR.2014.193

    2014 doi

  17. [25]

    Md-slam: Multi-cue direct slam

    Luca Di Giammarino, Leonardo Brizi, Tiziano Guadagnino, Cyrill Stachniss, and Giorgio Grisetti. Md-slam: Multi-cue direct slam. In Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) , pages 11047–11054, 2022. doi: 10.1109/IROS47612.2022.9981147

    2022

  18. [26]

    Dissanayake, P

    M.W.M.G. Dissanayake, P. Newman, S. Clark, H.F. Durrant-Whyte, and M. Csorba. A solution to the simul- taneous localization and map building (slam) problem. IEEE Trans. on Robotics and Automation , 17(3):229– 241, 2001. doi: 10.1109/70.938381

    2001 doi

  19. [27]

    Direct sparse odometry

    Jakob Engel, Vladlen Koltun, and Daniel Cremers. Direct sparse odometry. IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) , PP, 2016. doi: 10.1109/ TPAMI.2017.2658577

    2016

  20. [28]

    Engel, Thomas Sch ¨ops, and Daniel Cre- mers

    Jakob J. Engel, Thomas Sch ¨ops, and Daniel Cre- mers. Lsd-slam: Large-scale direct monocular slam. In Proc. of the Europ. Conf. on Computer Vision (ECCV) ,

  21. [29]

    Harold M. Finn. Adaptive detection mode with threshold control as a function of spatially sampled clutter level estimates. Rca Rev., 29:414–465, 1968. URL https://api. semanticscholar.org/CorpusID:208092087

    1968

  22. [30]

    Svo: Fast semi-direct monocular visual odom- etry

    Christian Forster, Matia Pizzoli, and Davide Scara- muzza. Svo: Fast semi-direct monocular visual odom- etry. Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , pages 15–22, 2014. URL https: //api.semanticscholar.org/CorpusID:206850490

    2014

  23. [31]

    Barfoot, and Gabe Sibley

    Paul Furgale, Chi Hay Tong, Timothy D. Barfoot, and Gabe Sibley. Continuous-time batch trajectory estimation using temporal basis functions. Intl. Journal of Robotics Research (IJRR) , 34(14):1688–1710, 2015. doi: 10. 1177/0278364915585860. URL https://doi.org/10.1177/ 0278364...

    2015

  24. [33]

    Gandhi and S.A

    P.P. Gandhi and S.A. Kassam. Analysis of cfar proces- sors in nonhomogeneous background. IEEE Trans. on Aerospace and Electronic Systems (T-AES) , 24(4):427– 445, 1988. doi: 10.1109/7.7185

    1988 doi

  25. [34]

    Complete odometry estimation of a vehicle using single automotive radar and a gyroscope

    Reza Ghabcheloo and Shadman Siddiqui. Complete odometry estimation of a vehicle using single automotive radar and a gyroscope. In Mediterranean Conference on Control and Automation (MED) , pages 855–860, 2018. doi: 10.1109/MED.2018.8442474

    2018

  26. [35]

    Photometric lidar and rgb-d bundle adjustment

    Luca Giammarino, Emanuele Giacomini, Leonardo Brizi, Omar Salem, and Giorgio Grisetti. Photometric lidar and rgb-d bundle adjustment. IEEE Robotics and Automation Letters (RA-L) , PP:1–8, 07 2023. doi: 10.1109/LRA. 2023.3281907

    2023

  27. [36]

    Towards direct lo- calization for visual teach and repeat

    Mona Gridseth and Timothy Barfoot. Towards direct lo- calization for visual teach and repeat. In 16th Conference on Computer and Robot Vision (CRV) , pages 97–104,

  28. [37]

    Barfoot, Ayoung Kim, and Christoffer Heckman

    Kyle Harlow, Hyesu Jang, Timothy D. Barfoot, Ayoung Kim, and Christoffer Heckman. A new wave in robotics: Survey on recent mmwave radar applications in robotics. IEEE Trans. on Robotics (TRO) , 40:4544–4560, 2024. doi: 10.1109/TRO.2024.3463504

    2024

  29. [38]

    Rolling shutter bundle adjustment

    Johan Hedborg, Per-Erik Forss ´en, Michael Felsberg, and Erik Ringaby. Rolling shutter bundle adjustment. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , pages 1434–1441, 2012. URL https://api.semanticscholar.org/CorpusID:15591674

    2012

  30. [39]

    Instantaneous ego-motion estimation using doppler radar

    Dominik Kellner, Michael Barjenbruch, Jens Klappstein, J¨urgen Dickmann, and Klaus Dietmayer. Instantaneous ego-motion estimation using doppler radar. In Intl. IEEE Intelligent Transportation Systems Conference (ITSC) , pages 869–874, 2013. doi: 10.1109/ITSC.2013.6728341

    2013

  31. [41]

    Do we need scan-matching in radar odometry? In Proc

    Vladim ´ir Kubelka, Emil Fritz, and Martin Magnusson. Do we need scan-matching in radar odometry? In Proc. of the IEEE Intl. Conf. on Robotics & Automa- tion (ICRA) , pages 13710–13716, 2024. doi: 10.1109/ ICRA57147.2024.10610666

    2024

  32. [42]

    Wang, and Wen-Chieh Lin

    Pou-Chun Kung, C. Wang, and Wen-Chieh Lin. A normal distribution transform-based radar odometry de- signed for scanning and automotive radars. Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), pages 14417–14423, 2021. URL https://api. semanticscholar.org/CorpusID:...

    2021

  33. [43]

    Continu- ous latent state preintegration for inertial-aided systems

    Cedric Le Gentil and Teresa Vidal-Calleja. Continu- ous latent state preintegration for inertial-aided systems. Intl. Journal of Robotics Research (IJRR) , 42(10):874– 900, 2023. doi: 10.1177/02783649231199537. URL https://doi.org/10.1177/02783649231199537

    2023 doi

  34. [44]

    Gaussian process preintegration for inertial-aided state estimation

    Cedric Le Gentil, Teresa Vidal-Calleja, and Shoudong Huang. Gaussian process preintegration for inertial-aided state estimation. IEEE Robotics and Automation Letters (RA-L), 5(2):2108–2114, 2020. doi: 10.1109/LRA.2020. 2970940

    2020 doi

  35. [45]

    Real-time truly-coupled lidar-inertial motion correction and spatiotemporal dynamic object detection

    Cedric Le Gentil, Raphael Falque, and Teresa Vidal- Calleja. Real-time truly-coupled lidar-inertial motion correction and spatiotemporal dynamic object detection. In Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) , pages 12565–12572, 2024. doi: 10.1...

    2024

  36. [46]

    Cfear++: An efficient radar-inertial semantic odometry

    Siru Li, Yushuai Chen, Ziyang Hong, and Liang Hu. Cfear++: An efficient radar-inertial semantic odometry. ICRA Workshop, Radar in Robotics: Resilience from Signal to Navigation , 2024

    2024

  37. [47]

    ORORA: Outlier-robust Radar Odometry

    Hyungtae Lim, Kawon Han, Gunhee Shin, Giseop Kim, Songcheol Hong, and Hyun Myung. ORORA: Outlier-robust Radar Odometry. In Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , pages 2046–2053, 2023. doi: 10.1109/ICRA48891.2023. 10160997

    2023

  38. [48]

    Daniil Lisus, Johann Laconte, Keenan Burnett, and Tim D. Barfoot. Pointing the way: Refining radar- lidar localization using learned icp weights. ArXiv, abs/2309.08731, 2023. URL https://api.semanticscholar. org/CorpusID:262043844

    2023 arXiv

  39. [49]

    Yoon, Richard Poulton, John Marshall, and Timothy D

    Daniil Lisus, Keenan Burnett, David J. Yoon, Richard Poulton, John Marshall, and Timothy D. Barfoot. Are doppler velocity measurements useful for spinning radar odometry? IEEE Robotics and Automation Letters (RA-L), 10(1):224–231, 2025. doi: 10.1109/LRA.2024. 3505821

    2025 doi

  40. [50]

    David G. Lowe. Distinctive image features from scale- invariant keypoints. Intl. Journal of Computer Vi- sion (IJCV) , 60:91–110, 2004. doi: 10.1023/B:VISI. 0000029664.99615.94. URL http://dx.doi.org/10.1023/B: VISI.0000029664.99615.94

    2004

  41. [51]

    Evaluation of cfar detectors performance

    Jos ´e Machado Fern ´andez, Norelys Mojena-Hern ´andez, and Jes ´us Bacallao Vidal. Evaluation of cfar detectors performance. ITECKNE Innovaci ´on e Investigaci ´on en Ingenier´ia, 14:170, 11 2017. doi: 10.15332/iteckne. v14i2.1772

    2017 doi

  42. [52]

    Pharao: Direct radar odometry using phase correlation

    Yeong Sang Park, Young-Sik Shin, and Ayoung Kim. Pharao: Direct radar odometry using phase correlation. In Proc. of the IEEE Intl. Conf. on Robotics & Au- tomation (ICRA), pages 2617–2623, 2020. doi: 10.1109/ ICRA40945.2020.9197231

    2020

  43. [53]

    The finer points: A systematic comparison of point- cloud extractors for radar odometry

    Elliot Preston-Krebs, Daniil Lisus, and Timothy Barfoot. The finer points: A systematic comparison of point- cloud extractors for radar odometry. arXiv preprint arXiv:2409.10491, 09 2024. doi: 10.48550/arXiv.2409. 12256

    2024 doi arXiv

  44. [54]

    Radar teach and repeat: Architecture and initial field testing

    Xinyuan Qiao, Alexander Krawciw, Sven Lilge, and Timothy D Barfoot. Radar teach and repeat: Architecture and initial field testing. arXiv preprint arXiv:2409.10491, 2024

    2024 arXiv

  45. [55]

    Gaussian Processes for Machine Learning

    C E Rasmussen and C K I Williams. Gaussian Processes for Machine Learning . The MIT Press, 2006

    2006

  46. [56]

    Doppler-Aware Odometry from FMCW Scanning Radar

    Fraser Rennie, David Williams, Paul Newman, and Daniele De Martini. Doppler-Aware Odometry from FMCW Scanning Radar. In Intl. IEEE Intelligent Trans- portation Systems Conference (ITSC) , pages 5126–5132, 2023

    2023

  47. [57]

    Radar cfar thresholding in clutter and multiple target situations

    Hermann Rohling. Radar cfar thresholding in clutter and multiple target situations. IEEE Trans. on Aerospace and Electronic Systems (T-AES) , AES-19(4):608–621, 1983. doi: 10.1109/TAES.1983.309350

    1983

  48. [58]

    Orb: An efficient alternative to sift or surf

    Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. Orb: An efficient alternative to sift or surf. In Proc. of the IEEE Intl. Conf. on Computer Vision (ICCV), pages 2564–2571, 2011. doi: 10.1109/ICCV . 2011.6126544

    2011

  49. [59]

    Enhancing lidar performance: Robust de- skewing exclusively relying on range measurements

    Omar Ashraf Ahmed Khairy Salem, Emanuele Giaco- mini, Leonardo Brizi, Luca Di Giammarino, and Gior- gio Grisetti. Enhancing lidar performance: Robust de- skewing exclusively relying on range measurements. In AIxIA 2023 - Advances in Artificial Intelligence , Berlin, Heidelberg...

    2023 doi

  50. [60]

    Smith and P

    M.E. Smith and P. K. Varshney. Vi-cfar: a novel cfar algorithm based on data variability. Proceedings of the 1997 IEEE National Radar Conference , pages 263–268,

    1997

  51. [61]

    Millimeter wave fmcw radars for perception, recognition and localization in automotive applications: A survey

    Arthur Venon, Yohan Dupuis, Pascal Vasseur, and Pierre Merriaux. Millimeter wave fmcw radars for perception, recognition and localization in automotive applications: A survey. IEEE Trans. on Intelligent Vehicles (T-IV) , 7: 1–1, 09 2022. doi: 10.1109/TIV .2022.3167733

    2022

  52. [62]

    Fast-mbym: Leveraging translational invariance of the fourier transform for ef- ficient and accurate radar odometry

    Rob Weston, Matthew Gadd, Daniele De Martini, Paul Newman, and Ingmar Posner. Fast-mbym: Leveraging translational invariance of the fourier transform for ef- ficient and accurate radar odometry. In Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA) , pages 2186–2192...

    2022

  53. [63]

    Im- proved switching cfar detector for non-homogeneous environments

    Renli Zhang, Weixing Sheng, and Xiaofeng Ma. Im- proved switching cfar detector for non-homogeneous environments. Signal Processing, page 35–48, 2013. doi: 10.1016/j.sigpro.2012.06.015

    2013 doi

  54. [64]

    A tutorial on quantitative trajectory evaluation for visual(-inertial) odometry

    Zichao Zhang and Davide Scaramuzza. A tutorial on quantitative trajectory evaluation for visual(-inertial) odometry. In Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) , 2018

    2018

  55. [65]

    A novel approach for cfar processors design

    Lei Zhao, Weixian Liu, Xin Wu, and Jeffrey S Fu. A novel approach for cfar processors design. In Proceed- ings of the 2001 IEEE Radar Conference, pages 284–288. IEEE, 2001. APPENDIX A RESULTS DETAILS ON BOREAS DATASET This appendix presents the detailed results obtained on the...

    2001

  56. [1997]

    URL https://api.semanticscholar.org/CorpusID: 140690935

  57. [2014]

    URL https://api.semanticscholar.org/CorpusID: 14547347

  58. [2019]

    doi: 10.1109/CRV .2019.00021

    2019

  59. [2021]

    URL https://doi

    doi: 10.1109/lra.2021.3052439. URL https://doi. org/10.1109/lra.2021.3052439

    2021

  60. [5593]

    doi: 10.1007/s10514-015-9455-y

    doi

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.