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REVIEW 3 major objections 6 minor 45 references

Do We Still Need to Work on Odometry for Autonomous Driving?

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper argues that a wheel encoder plus a yaw gyroscope, integrated with plain dead-reckoning equations, can outperform state-of-the-art radar-inertial odometry on an autonomous-driving benchmark at negligible computational cost, and…

desk verdict Wheel-encoder odometry is a surprisingly strong baseline, but the paper overstates the leaderboard win and its own independent data undercut the abstract's claim. read the letter →

arxiv 2505.04438 v2 pith:BHONLP7I submitted 2025-05-07 cs.RO

Cedric Le Gentil , Daniil Lisus , Timothy D. Barfoot This is my paper · ORCID
classification cs.RO
keywords odometrywheelencoderyawgyroscopedeadreckoningautonomousdrivingradarslipsnow
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 asks whether the autonomous-driving community should keep investing in complex odometry systems. It shows that "Odometer-Gyroscope (OG) odometry"—direct integration of wheel-encoder ticks and gyroscope yaw rate—achieves a 0.20% relative translation error on a public multi-season driving benchmark, edging out state-of-the-art radar-inertial methods at 0.26% while using under one millisecond of computation per frame. The authors argue that nominal driving odometry is effectively solved by this simple dead reckoning. To stress-test the approach, they collected snowstorm data with deliberate handbrake-induced slippage and found that only substantial slip (relative translation errors near 1%) defeats the method. The paper's conclusion is that research effort should move from generic odometry to slip detection and localization within prior maps.

What carries the argument

The load-bearing object is the OG dead-reckoning update. Distance traveled is computed as $d_i = 2\pi r (c_{i+1} - c_i)/N$ from encoder tick counts, and heading is updated by trapezoidal integration of the gyro: $\Delta\theta = (\omega_i + \omega_{i+1})(t_{i+1} - t_i)/2$. When the vehicle turns, the wheel's displacement is treated as a circular arc: $\Delta p = (d_i/\Delta\theta)[\sin(\Delta\theta),\, 1-\cos(\Delta\theta)]^\top$; when $\Delta\theta \approx 0$, the displacement degenerates to $(d_i, 0)^\top$. Two supporting mechanisms make this work: a gyro-bias estimate obtained by averaging measurements while the vehicle is static and updated with a low-pass filter, and an offline calibration that fits the wheel radius and the encoder-to-GNSS/INS extrinsic parameters by least squares against ground-truth velocities. The identity underlying the argument is that all of the benchmark's sophisticated radar pipelines are, in nominal conditions, recovering the same planar motion that a wheel encoder measures directly.

What would settle it

Re-run OG odometry on the same benchmark sequences using a reference trajectory produced without any wheel-encoder input, for example GNSS/INS only; if the 0.20% relative translation error no longer beats the 0.26% radar-inertial baseline, the headline outperformance claim fails. Independently, on the paper's snowstorm sequences with encoder-free ground truth, check whether no-slip snowy-road segments keep OG error below about 0.5%; if they exceed that, the claim that nominal conditions are handled is weakened.

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Extended reading notes

Core claim

The central claim is that the simplest possible dead-reckoning pipeline—counting wheel-encoder ticks for forward distance and integrating a yaw gyroscope for heading—is enough to match or beat the best radar-inertial odometry on nominal road driving. On the public benchmark's leaderboard the OG estimate posts a relative translation error of 0.20% versus 0.26% for the best radar-inertial method, with a per-frame execution time under one millisecond against hundreds of milliseconds. Lidar-inertial odometry can be more accurate, but at roughly three orders of magnitude more computation. The paper's own snowstorm experiments show the method degrades only when wheel slip is deliberately induced: no-slip snow-road sequences stay under 0.50% error, while drift sequences with up to 0.65 m/s RMS lateral slip reach approximately 1% to 2% error. The authors therefore claim that for autonomous driving in nominal conditions, the no-slip assumption is not the limiting factor it is often taken to be.

Load-bearing premise

The benchmark's ground-truth trajectory is generated with a solution that also uses the wheel encoder, so the comparison may be biased in the wheel-based method's favor; the paper calls this bias slight but never quantifies it.

Editorial extensions

If this is right

  • In nominal road conditions, a wheel encoder and a yaw gyroscope are enough for competitive odometry, so the marginal value of radar or lidar odometry for ordinary passenger-vehicle autonomy drops sharply.
  • The practical bottleneck becomes slip: the paper's measurements tie OG error to RMS wheel slip, making slip detection and compensation the natural next problem, with direct applications to stability and driver-assistance systems.
  • Public benchmarks should include off-nominal trajectories—snow, handbrake turns, sustained drifting—because existing large-scale driving datasets never stress the no-slip assumption.
  • For autonomous driving, research priority should move from unknown-environment odometry to localization within a prior map and map updating, where odometry serves only as a local prior.

Reading between the lines

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

  • The headline lead may be partly an artifact of the benchmark's own ground truth: the wheel encoder is weakly used in the reference trajectory, and on the paper's independent snowstorm data with encoder-free ground truth the wheel method no longer beats the radar baselines. The outperform claim is therefore safest read as benchmark-specific.
  • The calibration procedure still leans on a high-accuracy GNSS/INS solution; the paper's suggestion that consumer GPS could replace it is plausible but untested and would require continuous radius and bias estimation to hold over months of driving.
  • A direct extension would be to run the same encoder-gyro integration on any other dataset with wheel encoders and independent ground truth, to see whether the 0.20% result transfers across vehicles, roads, and traffic conditions; the paper currently tests one platform and one benchmark.
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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 / 6 minor

Summary. The paper proposes Odometer-Gyroscope (OG) odometry, the direct integration of wheel-encoder distance and gyro yaw rate (Section III-A), with a stationary-heuristic gyro bias estimate (Section III-B) and a least-squares calibration of wheel radius and wheel-to-GNSS/INS extrinsics (Section III-C, Eq. 7). The authors benchmark OG against radar-based and lidar-based baselines in two settings: the Boreas public leaderboard (Tables I-II) and a new 15-sequence dataset collected in February 2025, which includes snowstorm episodes with deliberately induced slip (Tables III-IV, Figs. 3-5). Importantly, the independent dataset's ground-truth trajectory is generated without the wheel encoder (Section IV-B.1), removing the sensor-to-reference coupling that exists in Boreas. The paper's central claim is that OG odometry outperforms current state-of-the-art radar-inertial SE(2) odometry in most scenarios at a fraction of the computational cost (Table V), with the Boreas result (0.20%% versus 0.26%% translation error) as the headline example. It concludes that the automotive community should redirect effort from nominal-condition odometry to slip handling and localization.

Significance. The paper asks a well-posed and timely question, and it has genuine strengths. The OG formulation is minimal and transparent: all free parameters (wheel radius, extrinsics, gyro bias) are explicitly identified, and the integration equations are simple enough to be reproduced and checked by hand. The independent dataset in Section IV-B.1 is exactly the right experimental control for the question, since it removes the wheel encoder from the ground-truth generation. The slippage data (Table IV, Figs. 3-5) are granular and falsifiable, the computation-time comparison (Table V) is quantified, and the OG21-versus-OG24 comparison makes a practical point about calibration longevity. The stress-test concern lands on a careful reading: the headline 'outperform' claim rests entirely on the Boreas leaderboard margin, which is confounded by an unquantified encoder-aided reference trajectory, and the paper's own bias-free experiments show the opposite ordering. If the abstract were reframed to 'competitive at negligible cost under no-slip assumptions', the contribution would be a valuable negative/positive result for the odometry community; as written, the headline overstates the evidence.

major comments (3)
  1. [Abstract; Table I; Section IV-A.3] The headline claim that 'OG odometry can outperform current state-of-the-art radar-inertial SE(2) odometry ... in most scenarios' is supported only by the Boreas leaderboard margin (0.20%% versus 0.26%% translation error). Section IV-A.3 concedes that the Boreas reference trajectory was generated using an RTK-GNSS/INS/encoder solution and that the wheel encoder is 'weakly used' in the reference, 'slightly biasing the results in a positive manner', but the bias is never quantified. The margin is 0.06 percentage points, which is the same order of magnitude as the admitted bias, and the calibration in Eq. (7) is fitted against that same encoder-influenced reference on the 14 public sequences, so the coupling can enter both the estimated parameters and the evaluation. The authors should either quantify the encoder's contribution to the reference trajectory (for example, by recomputing the reference on the public sequences with an encoder-free GNSS/INS solution and re-running the leaderboard comparison, or by providing a sensitivity analysis of the 0.20%% versus 0.26%% margin) or remove the outperform claim from the abstract.
  2. [Table III; Section IV-B.3] The independent dataset, whose ground truth is generated without the wheel encoder (Section IV-B.1), directly contradicts the abstract's claim. In Table III, OG24's relative translation error is worse than DRO-G's in every scenario category: 0.24%% versus 0.18%% for Suburbs no-slip, 0.50%% versus 0.24%% for Suburbs slip, 0.18%% versus 0.17%% for Campus no-slip, and 1.00%% versus 0.16%% for Campus drift. OG24 beats only Radar-DG among the baselines, and only in the no-slip rows. The prose in Section IV-B.3 says that OG is 'on par' with state-of-the-art methods, which is more accurate, but the abstract and Table I narrative is never reconciled with this table. The paper should state plainly that on the unbiased benchmark, the state-of-the-art radar baseline is more accurate than OG in every category, and the outperform claim should be restricted to the Boreas leaderboard or dropped entirely.
  3. [Table III; Section IV-B.3] The comparative claim 'on par' is asserted without statistical support. Table III reports only means over 2-5 sequences with no standard deviations or per-sequence values for the no-slip categories, so a 0.01 percentage-point gap (Campus no-slip: 0.17%% versus 0.18%%) is indistinguishable from noise, while the 0.06 percentage-point gap (Suburbs no-slip: 0.18%% versus 0.24%%) is reported without any dispersion measure. The authors should provide per-sequence breakdowns and a measure of spread for the no-slip categories, or temper the comparative wording to match what the data can actually support.
minor comments (6)
  1. [Table II] The table caption contains a typo: 'BOERAS' should read 'Boreas'.
  2. [Section IV-B.3] There is a mismatched parenthesis in the sentence 'The car slippage of the Campus drift sequence shown in Fig. 3) has a strong, local impact'; the opening parenthesis before 'Fig. 3' is missing.
  3. [Section III-B] The first-order low-pass filter used to update the gyroscope bias estimate is described only in words; please provide the update equation or a citation so that the implementation is fully reproducible.
  4. [Section V] The sentence 'there is no rational explanation for why we should use much more complex, expensive, and sometimes less reliable solutions' overreaches the measured data, which show that the radar and lidar baselines retain their accuracy under slip (for example, 0.13-0.16%% on Campus drift versus 1.00%% for OG). A more measured statement would strengthen the discussion.
  5. [Figures 4-5; Section IV-B.3] The text in Section IV-B.3 refers to 'the first slip and no-slip sequences', while the caption of Fig. 5 refers to 'the best Suburbs no-slip and Suburbs slip sequences'; please make the choice consistent and ensure the reader knows which sequences are shown.
  6. [Section VI] The paper calls for new benchmark datasets, and its independent slip dataset is the only bias-free evidence for its comparative claims, yet Section VI defers releasing that dataset to future work. Please state the availability status of the dataset explicitly (including any planned release link) in the current version.

Circularity Check

1 steps flagged · score 4.0 of 10

Boreas leaderboard 'outperform' is self-referential: the reference trajectory uses the tested wheel encoder, and the authors' encoder-free dataset contradicts the headline.

  1. self definitional [Section IV-A.3 (Boreas leaderboard result, Table I); Section III.C (wheel-radius calibration, Eq. 7)]
    "the OG odometry results could benefit from the fact that the wheel encoder is also weakly used in the generation of the ground-truth trajectory, thus slightly biasing the results in a positive manner."

    The headline outperform claim rests solely on the Boreas margin (0.20% vs 0.26%), where the reference is generated with an RTK-GNSS/INS/encoder solution, the same wheel-encoder signal OG integrates (Eqs. 1-2). Section III.C fits the wheel radius to this same encoder-influenced ground truth (Eq. 7), aligning OG's scale to the benchmark's own scale. The admitted bias is never quantified and could produce a 0.06pp margin. On the encoder-free dataset (Section IV-B.1), Table III shows DRO-G beating OG24 in every condition (e.g., 0.16 vs 1.03). Thus the outperform claim is partly self-referential, not an independent finding.

full rationale

The paper's central quantitative assertion, that simple OG odometry outperforms state-of-the-art radar-inertial SE(2) odometry, is supported only by the Boreas leaderboard result (Abstract and Table I). That result is weakened by a genuine self-reference: the ground-truth trajectory is produced by an RTK-GNSS/INS/encoder solution, and the wheel encoder is the very sensor OG integrates; moreover, OG's wheel radius is calibrated against that same encoder-influenced ground-truth velocity field. The authors themselves flag the resulting positive bias as 'slight' but do not quantify it, and the winning margin (0.06 percentage points) is small enough that even a weak encoder contribution could manufacture the win. The paper's own independent dataset, explicitly designed to remove this coupling (ground truth generated without the wheel encoder), contradicts the headline: in Table III, DRO-G beats OG24 in every condition class, with large gaps in slip/drift scenarios. The defensible, non-circular finding is that dead-reckoning is competitive and far cheaper in nominal conditions; the specific 'outperform' claim, however, reduces to a self-referential benchmark result. No other circularity is present: the OG algorithm is a straightforward integration, the paper explicitly disclaims novelty, and the calibration on held-out sequences would be standard if the ground truth were sensor-independent. The concern here is benchmark contamination rather than a hidden equation-level identity, so the score is moderate rather than extreme.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The method introduces no new physical entities and only standard calibrated quantities: wheel radius, wheel-to-INS extrinsics, and gyroscope bias. The main unstated cost is the no-slip assumption and the accuracy of the benchmark reference, both of which the paper partially stress-tests but does not fully quantify.

free parameters (3)
  • Wheel radius r = Estimated via nonlinear least squares from ground-truth velocities (value not reported)
    Distance per encoder tick in Eq. (1) scales linearly with r, so the whole trajectory accuracy depends on this calibrated value estimated in Eq. (7).
  • Wheel-to-INS extrinsics p_w^GT and R_w^GT = Estimated alongside r in Eq. (7)
    These map the wheel-center velocity into the GNSS/INS frame and are needed both for calibration and for comparing OG estimates against the reference trajectory.
  • Gyroscope bias estimate b = Initialized from 2 seconds of static data, updated by low-pass filter (value not reported)
    Heading integration in Eqs. (3)-(5) is sensitive to yaw-rate bias, so the estimated bias is a parameter the method must provide.
assumptions (4)
  • domain assumption Wheel encoder ticks are linearly proportional to distance traveled (no-slip assumption)
    Eq. (1) defines distance from tick counts; the paper's entire stress-test section is built around violating this assumption, so it is load-bearing.
  • domain assumption Yaw gyroscope measurement equals vehicle yaw rate plus a slowly varying bias
    Section III-B assumes this sensor model and uses it for heading integration and bias estimation.
  • domain assumption Boreas RTK-GNSS/INS/encoder ground truth is accurate and only weakly biased by encoder usage
    Section IV-A uses this leaderboard as the primary benchmark; the authors acknowledge the encoder is weakly used in ground-truth generation but do not quantify the bias.
  • standard math Planar SE(2) kinematics with a non-steering wheel describe the vehicle motion
    Eqs. (2)-(4) use the standard planar rigid-body rotation model; this is a standard kinematic assumption for the Boreas vehicle.

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Cite this review

Pith. "Pith review of Do We Still Need to Work on Odometry for Autonomous Driving?." pith.science (2026). https://pith.science/paper/BHONLP7I

@misc{pith2026250504438,
  author       = {Pith},
  title        = {Pith review of: Do We Still Need to Work on Odometry for Autonomous Driving?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BHONLP7I}},
  note         = {Machine review of arXiv:2505.04438}
}
read the original abstract

Over the past decades, a tremendous amount of work has addressed the topic of ego-motion estimation of moving platforms based on various proprioceptive and exteroceptive sensors. At the cost of ever-increasing computational load and sensor complexity, odometry algorithms have reached impressive levels of accuracy with minimal drift in various conditions. In this paper, we question the need for more research on odometry for autonomous driving by assessing the accuracy of one of the simplest algorithms: the direct integration of wheel encoder data and yaw rate measurements from a gyroscope. We denote this algorithm as Odometer-Gyroscope (OG) odometry. This work shows that OG odometry can outperform current state-of-the-art radar-inertial SE(2) odometry for a fraction of the computational cost in most scenarios. For example, the OG odometry is on top of the Boreas leaderboard with a relative translation error of 0.20%, while the second-best method displays an error of 0.26%. Lidar-inertial approaches can provide more accurate estimates, but the computational load is three orders of magnitude higher than the OG odometry. To further the analysis, we have pushed the limits of the OG odometry by purposely violating its fundamental no-slip assumption using data collected during a heavy snowstorm with different driving behaviours. Our conclusion shows that a significant amount of slippage is required to result in non-satisfactory pose estimates from the OG odometry.

Figures

Figures reproduced from arXiv: 2505.04438 by the authors.

Figure 1
Figure 1. This paper benchmarks a simple wheel-gyroscope odometry [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Sample images from the front-facing camera during collection of [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Visualization of the slippage in one of the [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Visualization of the velocity measured by the wheel encoder and the ground-truth for two [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Magnitude of lateral and longitudinal slippage for the best [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

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Pith tools

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