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HeRCULES: Heterogeneous Radar Dataset in Complex Urban Environment for Multi-session Radar SLAM

T0 review · 3 major / 6 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read This paper presents HeRCULES, the first public dataset to combine a 4D phased-array radar, a 360-degree spinning radar, and an FMCW LiDAR with per-sensor ground truth, intended to support multi-session radar SLAM and cross-sensor place…

desk verdict Useful first-of-kind sensor combination for radar fusion research, but the ground-truth poses need independent validation before trusting the benchmark numbers. read the letter →

arxiv 2502.01946 v3 pith:Z5336773 submitted 2025-02-04 cs.RO cs.CV

Hanjun Kim , Minwoo Jung , Chiyun Noh , Sangwoo Jung , Hyunho Song , Wooseong Yang , Hyesu Jang , Ayoung Kim This is my paper · ORCID
classification cs.ROcs.CV
keywords radarSLAM4DspinningFMCWLiDARmulti-sessionplacerecognitionurbandatasetsensorcalibration
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

Radar SLAM methods are usually built around one radar type: either a 360-degree spinning radar that excels at mapping and place recognition, or a lightweight phased-array 4D radar that returns range, azimuth, elevation, and Doppler velocity. This paper argues that the two types are complementary, yet no public dataset contains both, so algorithms never have to cope with the combination. HeRCULES is introduced as the first public dataset to pair a 4D radar, a spinning radar, and an FMCW LiDAR, with stereo cameras, IMU, and RTK-GPS, across eight urban environments and varied weather, lighting, and traffic. The routes repeatedly revisit the same places, and ground-truth poses are provided for every sensor individually, which makes multi-session SLAM, place recognition, and radar-LiDAR fusion directly testable. Benchmark results on the dataset show LiDAR odometry far outperforming radar-only odometry and cross-modal place recognition far weaker than same-modal recognition, framing the open problems the dataset is meant to serve.

What carries the argument

The load-bearing mechanism is the recording and calibration pipeline that makes heterogeneous sensors commensurable. The FMCW LiDAR and 4D radar are operated with a shared relative-velocity convention; the LiDAR and spinning radar are aligned through correlative scan matching on polar and Cartesian images; the LiDAR, 4D radar, and cameras are calibrated together with a reflector-based tool that uses the 4D radar's direct elevation measurement; and the IMU-LiDAR transform is initialized with a targetless method. The per-sensor ground truth is produced by taking the RTK-GPS/INS trajectory and interpolating it to each sensor's timestamps with B-Splines, so that every point cloud and image has a pose that accounts for both lever-arm and time offset. The benchmark protocols, Fast-LIO, 4DRadarSLAM, ORORA, and Scan Context, then convert the raw recordings into the quantitative claims above.

What would settle it

Pick a sequence with a deliberate revisit, such as Mountain 01 or Stream 01, and compute the gap between the provided ground-truth poses at the two visits to the same place; a large gap, or a disagreement larger than the reported ATE when the revisited segment is matched by a high-accuracy LiDAR map, would show the ground truth is not accurate enough to support the benchmark conclusions.

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Extended reading notes

Core claim

The paper's central claim is that a heterogeneous radar dataset is a missing resource for radar SLAM, and that HeRCULES fills that gap: it is the first dataset to integrate a 4D radar and a spinning radar alongside an FMCW LiDAR, giving every sensor its own ground-truth pose so that trajectories and recognition results can be compared honestly. The evaluation underlines the point by showing what currently fails. On the Sports Complex and Library sequences, Fast-LIO on the FMCW LiDAR reaches an absolute trajectory error of about 10 m, ORORA on the spinning radar is less accurate, and 4DRadarSLAM on the 4D radar is far less accurate, partly because the Continental sensor returns fewer points than the Oculii radar used when 4DRadarSLAM was introduced. In place recognition with Scan Context, same-modal LiDAR queries achieve AUC near 0.97, same-modal 4D radar queries are lower, and cross-modal radar-query-on-LiDAR-database recognition falls to roughly 0.28-0.40. The paper presents these gaps as the motivation for the dataset: single-radar SLAM and direct cross-sensor matching are both open, and the data needed to work on them now exists.

Load-bearing premise

The benchmark's reliability rests on the assumption that the RTK-GPS/INS poses, interpolated to each sensor's timestamp, remain accurate in urban canyons, under bridges, on mountain roads, and in dense traffic, so that every reported error is really a sensor or algorithm error rather than a ground-truth error.

Editorial extensions

If this is right

  • Radar-only odometry is the current weak link: in the provided sequences, 4DRadarSLAM's ATE is tens of meters while LiDAR odometry is near ten meters, so heterogeneous or fused SLAM is the obvious next target.
  • Cross-modal place recognition has a concrete baseline to beat: matching a 4D radar query against an FMCW LiDAR database yields AUC around 0.28-0.40 on the tested sequences, far below same-modal recognition.
  • Multi-session SLAM can be evaluated directly because the routes revisit locations and every sensor has its own pose, which the Mountain, River Island, and Stream sequences are designed to support.
  • The shared Doppler-velocity structure of FMCW LiDAR and 4D radar makes the dataset suitable for comparing velocity estimation across the two sensing principles.

Reading between the lines

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

  • If the per-sensor ground truth is as accurate as claimed, the dataset can double as a testbed for temporal synchronization: residual time offsets between sensors should appear as consistent per-sensor pose shifts on the same trajectory.
  • The large radar-versus-LiDAR performance gap suggests that 4D radar SLAM might benefit substantially from point densification or temporal accumulation before matching; because the raw Continental point clouds are shipped, this hypothesis is testable without new hardware.
  • The weak cross-modal place recognition numbers are likely a property of the geometric Scan Context descriptor rather than of the sensors themselves, so the dataset's revisits make it well suited for training or evaluating learned cross-modal descriptors.
  • Because the same places are scanned by three active range sensors with different fields of view and densities, the dataset can serve as a benchmark for radar point upsampling and cross-sensor completion, an application the paper mentions only briefly.
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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 introduces HeRCULES, a multi-modal urban dataset combining a Continental ARS548 4D radar, a Navtech RAS6 spinning radar, an Aeva Aeries II FMCW LiDAR, stereo cameras, an IMU, and an RTK-GPS/INS. The authors claim it is the first public dataset to include both 4D radar and spinning radar alongside FMCW LiDAR. The dataset comprises 21 sequences across eight environments (Mountain, Library, Sports Complex, Parking Lot, River Island, Bridge, Street, Stream) under diverse weather and lighting conditions, with repeated visits to support place recognition and multi-session SLAM. They provide per-sensor ground truth poses derived from RTK-GPS/INS with B-Spline interpolation, and they report SLAM benchmarks (Fast-LIO, 4DRadarSLAM, ORORA) and place recognition benchmarks (Scan Context) on a subset of sequences. The paper also states that ROS tools and radar format conversion software are released.

Significance. If the data and tools are publicly available as stated, HeRCULES fills a genuine gap: no existing public dataset combines a 4D phased-array radar, a 360-degree spinning radar, and an FMCW LiDAR with Doppler velocity. This enables research on heterogeneous radar fusion, cross-sensor place recognition, and radar-LiDAR SLAM. The diversity of conditions, intentional revisits, and per-sensor ground truth are useful features. The authors also provide baseline evaluations, which are valuable for dataset adoption. However, the significance of the benchmark claims depends on the reliability of the ground truth poses and calibration, which are not quantitatively validated in the manuscript.

major comments (3)
  1. [Sec. IV-C and Table IV] The ground truth poses are load-bearing for all benchmark conclusions, but their accuracy is not validated. The manuscript states only that the GNSS solution was fixed and the INS converged before each sequence, and that B-Spline interpolation is used to re-time poses to each sensor. No failure flags, covariance estimates, or independent checks (e.g., loop-closure residuals or comparison with an offline LiDAR SLAM) are provided for the challenging sequences such as Mountain, Bridge, Street, and urban areas where RTK-GPS can degrade. Since Table IV reports ATE/RPE values against these poses (e.g., 4DRadarSLAM ATE of 64.884 m vs. Fast-LIO 0.358 m on Sports Complex 01), any systematic GT error would directly change the numerical results and potentially the ranking. Please add per-sequence GT quality metrics, interpolation error bounds, and at least one independent validation of the GT poses.
  2. [Sec. V-A, Table IV, Table V, and Sec. VI] The benchmark evaluation is too limited to support the paper's broad conclusions. Only Sports Complex 01 and Library 01 (both day, clear conditions) are used for SLAM evaluation in Table IV, and only these two are used for place recognition in Table V. The abstract, introduction, and conclusion claim that the evaluations identify limitations of single-radar SLAM in "various environments" and support the need for heterogeneous radar SLAM, but no night, rain, snow, bridge, mountain, or stream sequences are benchmarked. Single-run results are reported without error bars or repeated trials. Please expand the evaluation to a representative subset of adverse-weather and challenging-terrain sequences, or reframe the conclusions as preliminary case-study results rather than general findings.
  3. [Sec. III-B and Sec. IV-C] Quantitative extrinsic calibration accuracy is not reported. Section III-B4 presents only qualitative overlays in Fig. 4(c)-(e), and Section IV-C relies on extrinsics to produce per-sensor ground truth poses. Without quantitative metrics (e.g., reprojection error, point-to-plane residuals, or cross-sensor consistency checks), the accuracy of the per-sensor trajectories is unknown. Please report calibration residuals or an independent consistency evaluation for all sensor pairs.
minor comments (6)
  1. [Table I] The header structure of Table I is confusing, with sub-columns for "4D Radar" and "Scanning Radar" interleaved with general columns. Please restructure the table so that each sensor type is clearly separated.
  2. [Table II] The header row of Table II is garbled ("FrequencyRange Azimuth Elevation Range Azimuth Elevation") and contains the typo "elavation". Please reformat the table and clarify the units for each column.
  3. [Sec. V-B] The sentence "The ablation study conducted for the Library shows the results for thresholds of 10 m, 15 m, and 20 min Fig. 10" has a typo ("20 min" should be "20 m") and a missing "in" before "Fig. 10".
  4. [Sec. IV-C] The statement "Before logging each sequence, we ensure that the GNSS solution is fixed and the INS solution has converged" is vague. Please specify the RTK-GPS fix status criteria and whether the status is monitored during each sequence, not only at start-up.
  5. [Sec. V-A] The explanation for 4DRadarSLAM's poor performance ("the point cloud contains fewer points than the Oculii radar") is informal. Please report quantitative point counts per scan and mention whether any preprocessing was applied.
  6. [Sec. II-B] The sentence "All the above datasets are limited to 2D radar" at the end of Sec. II-B is ambiguous because Table I includes 4D radar datasets. Please clarify that the sentence refers only to the spinning-radar datasets discussed in that subsection.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the dataset, firstness claim, and benchmark evaluations do not reduce to their own inputs by construction.

full rationale

The paper's central claims are an artifact claim, a firstness claim, and benchmark evaluations. The firstness claim—'This is the first dataset to integrate 4D radar and spinning radar alongside FMCW LiDAR'—is supported by the external comparison in Table I and by the stated absence of prior heterogeneous-radar datasets; it is not derived from any fitted parameter or internal equation. The per-sensor ground truth (Sec. IV-C) is produced from a fixed RTK-GPS/INS solution with B-Spline interpolation for temporal alignment; this is an independent measurement pipeline, not a prediction fitted to the evaluated SLAM outputs. The SLAM and place recognition evaluations use published baselines (Fast-LIO, 4DRadarSLAM, ORORA, Scan Context); Scan Context (Ref. [37]) is a self-citation overlapping with author A. Kim, but it is used only as an evaluation baseline, not as load-bearing support for the dataset's novelty or for the ground-truth derivation. No equation in the manuscript reduces to another equation by construction, and no fitted input is renamed as a prediction. The skeptical concern—that RTK-GPS/INS ground truth may be inaccurate under bridges, in urban canyons, or during dynamic maneuvering—is a correctness and validation risk, not a circularity, because the benchmark conclusions would be falsifiable if the released data do not support them.

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

The dataset itself is the contribution; the paper introduces no new physical entities or fitted parameters. The axioms are the unverified background assumptions about data quality, calibration, and ground truth.

assumptions (4)
  • domain assumption RTK-GPS/INS ground truth remains fixed and accurate in all recorded sequences
    The per-sensor ground truth poses rely on GNSS/INS convergence and B-Spline interpolation (Section IV.C).
  • domain assumption Extrinsic calibration methods from Boreas, Domhof, and Zhu are appropriate for this sensor suite
    Calibration is adopted from prior work without independent validation on this platform (Section III.B).
  • domain assumption Sensor specifications in Table II are accurate
    The paper lists resolutions and FOVs but provides no independent verification.
  • domain assumption The dataset files are complete and correctly synchronized over UTC timestamps
    The dataset is hosted externally; the manuscript does not demonstrate data integrity.

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Cite this review

Pith. "Pith review of HeRCULES: Heterogeneous Radar Dataset in Complex Urban Environment for Multi-session Radar SLAM." pith.science (2026). https://pith.science/paper/Z5336773

@misc{pith2026250201946,
  author       = {Pith},
  title        = {Pith review of: HeRCULES: Heterogeneous Radar Dataset in Complex Urban Environment for Multi-session Radar SLAM},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z5336773}},
  note         = {Machine review of arXiv:2502.01946}
}
read the original abstract

Recently, radars have been widely featured in robotics for their robustness in challenging weather conditions. Two commonly used radar types are spinning radars and phased-array radars, each offering distinct sensor characteristics. Existing datasets typically feature only a single type of radar, leading to the development of algorithms limited to that specific kind. In this work, we highlight that combining different radar types offers complementary advantages, which can be leveraged through a heterogeneous radar dataset. Moreover, this new dataset fosters research in multi-session and multi-robot scenarios where robots are equipped with different types of radars. In this context, we introduce the HeRCULES dataset, a comprehensive, multi-modal dataset with heterogeneous radars, FMCW LiDAR, IMU, GPS, and cameras. This is the first dataset to integrate 4D radar and spinning radar alongside FMCW LiDAR, offering unparalleled localization, mapping, and place recognition capabilities. The dataset covers diverse weather and lighting conditions and a range of urban traffic scenarios, enabling a comprehensive analysis across various environments. The sequence paths with multiple revisits and ground truth pose for each sensor enhance its suitability for place recognition research. We expect the HeRCULES dataset to facilitate odometry, mapping, place recognition, and sensor fusion research. The dataset and development tools are available at https://sites.google.com/view/herculesdataset.

Figures

Figures reproduced from arXiv: 2502.01946 by the authors.

Figure 1
Figure 1. Overview of the HeRCULES Dataset. The FMCW LiDAR and 4D radar point colors represent relative velocities, with red [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Sensor overview of HeRCULES and coordinate of sensors. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Day, dusk, and night conditions of the HeRCULES dataset. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: (a) LiDAR - spinning radar extrinsic calibration pipeline. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 6
Figure 6. Figure 6: File structure of the HeRCULES dataset, illustrating the [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Ground truth pose for each sensor is overlaid on the path of [PITH_FULL_IMAGE:figures/full_fig_p005_7.png]
Figure 9
Figure 9. Figure 9: Place recognition result for Sports Complex. 0.0 0.2 0.4 0.6 0.8 1.0 Recall 0.0 0.2 0.4 0.6 0.8 1.0 Precision PR Curve (Library) Aeva (10m) Aeva (15m) Aeva (20m) Continental (10m) Continental (15m) Continental (20m) Aeva-Continental (10m) Aeva-Continental (15m) Aeva-Co…
Figure 10
Figure 10. Figure 10: Place recognition result for Library. VI. CONCLUSION The HeRCULES dataset is a comprehensive benchmark for SLAM and sensor fusion research in autonomous driv￾ing, uniquely integrating a diverse sensor suite including 4D radar, spinning radar, FMCW LiDAR, IMU, GPS, and…

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Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.