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GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation

T0 review · 3 major / 4 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read GrandTour is the largest public legged-robot dataset to date, pairing 49 missions with survey-grade ground truth and a 52-method benchmark.

desk verdict GrandTour is the legged-SLAM dataset the community needs, but the ground-truth validation on GNSS-denied missions is self-consistency, not absolute accuracy, so the benchmark rankings on ARC-2/ARC-7 inherit unquantified drift. read the letter →

arxiv 2602.18164 v3 pith:EDLAMBM7 submitted 2026-02-20 cs.RO

Turcan Tuna , Jonas Frey , Frank Fu , Katharine Patterson , Tianao Xu , Maurice Fallon , Cesar Cadena , Marco Hutter This is my paper · ORCID
classification cs.RO
keywords leggedroboticsmulti-modaldatasetstateestimationSLAMbenchmarkmulti-sensorfusionLiDAR-inertialodometryvisual-inertialgroundtruth
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

GrandTour sets out to give legged-robotics research what large earlier datasets gave driving and aerial vehicles: a shared, open collection of real-world, time-synchronized sensor data plus reference trajectories accurate enough to benchmark state-estimation methods. The paper claims it is the largest legged-robot dataset to date—49 missions, more than ten kilometers of walking, and over five hours of data, spanning alpine, forest, urban, industrial, indoor, and underground environments. The dataset pairs multiple LiDARs, ten cameras, depth cameras, eight IMUs, and full joint/proprioceptive feedback with ground truth from a robotic total station and RTK-GNSS, fused to millimeter-level accuracy. On six representative missions the authors evaluate 52 open-source odometry and SLAM pipelines, reporting per-mission ATE/RTE, ranks, and failures. If the claim holds, this becomes a reference benchmark for legged state estimation, multi-modal fusion, and perception in hard outdoor and indoor conditions.

What carries the argument

The load-bearing mechanism is the sensor payload plus its calibration and time-synchronization chain: all sensors share a common time source (sub-millisecond for most streams), extrinsics are calibrated to 0.05 mm mechanical tolerance and validated by point-cloud-to-image overlays, and ground-truth poses are generated by factor-graph fusion of total-station 20 Hz positions, post-processed GNSS/INS poses, and IMU measurements—producing a 20 Hz reference trajectory that follows the total station when line of sight exists and bridges occlusions with inertial/GNSS propagation.

What would settle it

Take the missions with no GNSS and less than half total-station coverage, re-derive the reference trajectory using only the raw total-station fixes with no inertial propagation, and recompute ATE/RTE for the top-ranked methods; if the rank order changes materially beyond a few centimeters, the ground-truth accuracy and the resulting rankings on those missions are falsified.

Watch

Extended reading notes

Core claim

The central claim is that GrandTour is the largest, most comprehensively instrumented public legged-robot dataset to date, and that its survey-grade reference trajectories make it a trustworthy benchmark. The supporting discovery is that centimeter-to-millimeter-level ground truth can be produced under real-field legged locomotion by combining total-station prism tracking, post-processed GNSS/INS, and a high-grade IMU in a factor-graph fusion, and that this reference is good enough to separate 52 open-source odometry/SLAM systems into meaningful rankings with clear failure cases.

Load-bearing premise

The reference trajectories are assumed to remain accurate in the portions of a mission where the total station loses line of sight and GNSS is absent, because those gaps are bridged by inertial/GNSS dead-reckoning; if that drift is larger than the few-centimeter gaps between competing methods, the benchmark's rankings on those segments are not reliable.

Editorial extensions

If this is right

  • Odometry and SLAM researchers get a common legged-robot test bed with 52 pre-run baselines, per-mission ATE/RTE ranks, and documented failure cases, so new methods can be compared without re-tuning every competitor.
  • Multi-modal fusion can be studied under real legged dynamics—foot contacts, slipping, body orientation changes—with synchronized LiDAR, camera, depth, IMU, and joint-encoder data on a single platform.
  • The benchmark's finding that no method dominates across missions, while visual-inertial systems fail most often on dark or featureless sequences, argues for evaluation protocols that emphasize robustness and recovery rather than mean error alone.
  • Intermediate outputs such as motion-compensated point clouds, leg odometry, terrain maps, and occupancy maps lower the entry barrier for perception, locomotion, and navigation research that does not want to build a full SLAM front end.
  • Survey-grade total-station ground truth, synchronized to sub-millisecond accuracy, makes it possible to test whether claimed improvements of a few centimeters are real or within reference noise.

Reading between the lines

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

  • If GrandTour is adopted the way earlier large datasets were, it could become the default comparison point for legged state estimation, shifting the field's focus from single-metric gains toward cross-mission robustness, initialization, and failure recovery.
  • The millimeter-level ground-truth claim is only as strong as the reference in GNSS-denied, line-of-sight-blocked segments; an independent check there, such as loop-closure-based map consistency or total-station-only interpolation, would show whether the published rankings survive.
  • Because the suite includes cross-view images and dense geometry from a moving quadruped, it is a natural testbed for learned depth, relocalization, and neural scene representation, going beyond the paper's own state-estimation benchmark.
  • A direct stress test would be to run the same benchmark on the lowest-coverage missions with a reference re-derived without dead-reckoning; if method rankings change drastically, the dataset's ranking protocol should be refined.
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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 / 4 minor

Summary. This paper introduces GrandTour, a 49-mission legged-robotics dataset collected with an ANYmal-D quadruped carrying the Boxi sensor payload. The dataset provides synchronized multi-modal data (three LiDARs, ten cameras, seven IMUs, depth cameras, proprioception, GNSS/INS) and ground-truth trajectories derived from dual-antenna RTK-GNSS with Inertial Explorer post-processing, a Leica MS60 total station, and Holistic Fusion factor-graph integration. The authors also present a localization benchmark on six missions in which 52 open-source odometry/SLAM pipelines are evaluated with ATE/RTE metrics and per-mission ranks. The central claims are that GrandTour is the largest legged-robot dataset to date, that it achieves millimeter-level ground-truth accuracy with sub-millisecond synchronization, and that the accompanying benchmark provides a rigorous cross-method comparison.

Significance. The dataset is potentially a major community resource: it combines a rich sensor suite, substantial environmental diversity, a detailed calibration chain with concrete validation artifacts (3 mm cross-camera prism consistency, LiDAR-camera overlays in Fig. 5), and an unusually detailed evaluation protocol. The open release in both Zarr and ROS formats, with derived outputs and conversion tools, is a genuine strength. If the ground-truth accuracy on low-coverage segments can be rigorously established, this would be a KITTI-class benchmark for quadruped state estimation and perception. However, the current validation of the ground-truth generation is self-referential on exactly the segments where benchmark rankings are decided, which tempers the 'millimeter-level' and '52-method benchmark' claims until the issue is addressed.

major comments (3)
  1. [Sec. 4.2, Eq. (1), Tables 3–4, 8–11, Fig. 10] The ground-truth validation is self-referential on the segments that matter most. TPS positions are factors in the Holistic Fusion graph (Eq. 1), so the reported 0.0028 m mean ATE against raw TPS measures self-consistency, not absolute accuracy between fixes. When the MS60 line of sight is lost, the graph retains Inertial Explorer unary poses and the HG4930 IMU, i.e., dead-reckoning; the text states that this 'lead[s] to drift accumulation,' and Table 4 gives 0.11 m horizontal position RMS for a 60 s GNSS outage. On benchmark missions ARC-2 (27.3% MS60 coverage), ARC-7 (35.8%, no GNSS), and CON-4 (67.1%, no GNSS), TPS gaps of tens of seconds are the norm due to the re-lock procedure (Fig. 10), so GT on those arcs can plausibly drift to the decimeter level. Since ATE/RTE separations among top LIO/LIVO methods on ARC-2/ARC-7 are about 1–8 cm (Table 9), the per-mission ranks and any 'millim
  2. [Abstract, Sec. 2.3, Sec. 9] The 'millimeter-level accuracy' claim is overgeneralized. The 2.8 mm validation is shown only on the SPX-2 sequence of Fig. 9; the text does not state whether the aggregate numbers cover all missions or a single representative one. No per-mission validation against an independent reference is provided for the 49 missions, many of which have far lower MS60 coverage (e.g., 18.8%, 20.3%, 23.6%, 27.3%). Please replace the global claim with a quantified statement of GT accuracy as a function of coverage, or restrict it to segments where it is actually supported.
  3. [Sec. 7.1.2, Tables 8–11, Eqs. (2)–(3)] The ordinal benchmark claims would benefit from uncertainty quantification. The text itself notes that many methods are separated by 'a few millimeters to about a centimeter in RTE and a few centimeters in ATE, often comparable to the reported standard deviations,' yet summary ranks (Eq. 3) and statements such as 'Coco-LIC and FAST-LIVO2 obtain the best average ranks' are reported without confidence intervals or significance tests. Given the GT uncertainty on low-coverage missions identified above, rank instability is a real risk. Please provide error bars, bootstrap/permutation intervals, or a sensitivity analysis with respect to GT perturbations.
minor comments (4)
  1. [Sec. 7.1 intro] The list reads '1) State Estimation and localization (Sec. 7.1), 1) Perception (Sec. 7.2), and 3) Locomotion & Navigation'; the second '1)' should be '2)'.
  2. [Table 2, Sec. 7.1.2] Typo: 'Intertial Explorer' in Table 2; also 'HF4930 IMU' in Sec. 7.1.2 should be 'HG4930 IMU'.
  3. [Table 3 / Fig. 7] The GNSS column uses 'Yes/No/Partially' but Fig. 7 only distinguishes Yes/No. Please define 'Partially' and explain how it is counted in the figure.
  4. [Sec. 4.2] Terminology is inconsistent: 'dual RTK-GPS' (Sec. 2.3), 'dual-antenna RTK GNSS' (Sec. 3.1), and 'NovAtel SPAN CPT7' should be unified to avoid confusion about the GNSS receiver/INS.

Circularity Check

1 steps flagged · score 6.0 of 10

Circular GT validation: Holistic Fusion is evaluated against the same TPS positions it consumes as factor-graph inputs, so the 2.8 mm ATE is a fit residual, not independent evidence of millimeter-level ground truth.

  1. fitted input called prediction [Sec. 4.2, 'Holistic Fusion Ground Truth', Eq. (1) and validation paragraph]
    "Holistic Fusion ... enables the fusion of TPS position measurements, Inertial Explorer post-processed poses, and HG4930 IMU measurements. ... When compared against the raw TPS position measurements, ATE for Holistic Fusion is a mean of 0.0028 m with standard deviation (σ) of 0.0020 m and RMSE 0.0034 m, whereas the Inertial Explorer tightly coupled solution yields a mean ATE of 0.132 m with σ=0.0721 m and RMSE 0.1504 m."

    The MAP estimate X* = arg max p(X|Z) in Eq. (1) uses the TPS position measurements as input factors, along with IE unary poses and IMU data. Reporting ATE/RMSE of Holistic Fusion against those same raw TPS positions therefore measures how well the graph fit its own factors, not absolute trajectory accuracy. The graph also estimates alignment-transform context variables, which can absorb static frame errors, further reducing the residual. The quoted 2.8 mm ATE / 3.4 mm RMSE is thus a self-consistency residual, yet it is used to support the paper's 'millimeter-level accuracy' ground-truth claim. The comparison to Inertial Explorer is not independent either, since IE post-processed poses are also inputs to the same graph. No independent bound is provided for the GNSS-denied / TPS-occluded seg

full rationale

The single concrete circular step is in Sec. 4.2: Holistic Fusion consumes TPS position measurements as factors in its factor graph, and the paper then validates Holistic Fusion against those same raw TPS measurements, reporting a mean ATE of 0.0028 m. This is a fit residual, not an external accuracy check, so it cannot independently support the abstract/Sec. 2.3 claim of 'millimeter-level accuracy.' The paper itself acknowledges that periods without both MS60 and Inertial Explorer unary poses require 'IMU-only dead-reckoning, leading to drift accumulation,' and Table 4 allows 0.11 m position error over a 60 s GNSS outage. On benchmark missions such as ARC-2 and ARC-7, where MS60 coverage is only 27.3% and 35.8%, Table 9 ranks methods separated by roughly 1–8 cm ATE; a 5–10 cm GT drift over multi-second-to-minute TPS outages is at the scale of those rankings. The stated GT restriction to segments with 'Inertial Explorer pose availability or MS60 measurements' is not a meaningful quality gate because IE emits poses continuously, including during GNSS-denied coasting. This is a partial circularity affecting the central GT-accuracy evidence. The dataset release, calibration chain, and 52-method benchmark are nevertheless substantial and mostly independent, so the overall score is 6 rather than higher.

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

The paper's central value is empirical. Everything load-bearing is either instrument accuracy taken on vendor authority, fusion weights inherited from the authors' prior library, or evaluation thresholds chosen by hand. These are reasonable for a dataset paper, but they mean the 'millimeter-level' and 'comprehensive benchmark' claims are not fully self-contained. No new physical entities are postulated; Boxi, Kleinkram, and the AP20-IMU are engineered hardware/software artifacts documented elsewhere, not new forces, particles, or conserved quantities.

free parameters (4)
  • Holistic Fusion factor-graph weights and covariances = not disclosed (inherited from Nubert et al., 2025)
    The 2.8 mm ATE ground-truth quality claim depends on how TPS, Inertial Explorer, and IMU factors are weighted in Eq. (1); the values are not given in this paper and are set by the authors' own prior work.
  • Trajectory association threshold t_max_diff = 0.5 × median(Δt_dense) ≈ 0.025 s for a 20 Hz reference
    Hand-set in Sec. 7.1; changes which estimate/reference pairs are compared and thus affects all benchmark numbers.
  • RTE path-length increment Δ = 0.5 m
    Hand-set based on quadruped motion characteristics; directly scales the reported relative trajectory error values.
  • Umeyama single-transform alignment = one rigid 4×4 transform per trajectory
    Modeling choice the authors explicitly flag as an assumption; if a trajectory needs multiple frames, all ATE numbers shift.
assumptions (5)
  • domain assumption Leica MS60/AP20 accuracy specifications (1.5 mm range, ±2 mm static, 20 Hz) hold as stated
    Anchors the mm-level GT claim; sourced from the vendor, not independently verified in this paper (Sec. 4.2).
  • domain assumption Inertial Explorer tightly-coupled performance matches Table 4 vendor specs (e.g., 0.01-0.02 m position RMS over 10 s GNSS outages)
    Used to claim GT remains bounded during GNSS outages; stated as 'within 2σ variation' per spec sheet (Sec. 4.2).
  • domain assumption Sub-ms time-synchronization accuracy holds across all sensors except the ZED2i
    Verified by a custom alignment tool whose methodology is not detailed; all fusion and calibration overlay claims depend on it (Sec. 3.2.1).
  • standard math Factor-graph MAP estimation and Umeyama least-squares are valid standard tools
    Eq. (1) and the evaluation protocol rely on standard estimation mathematics (Sec. 4.2, Sec. 7.1).
  • domain assumption Benchmark comparisons are valid on restricted GT spans (segments with Inertial Explorer or MS60 data)
    The paper truncates GT to well-supported segments; the tables do not mark which portions of each mission were dropped, so per-mission comparability assumes the restriction affects all methods equally (Sec. 4.2).

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Cite this review

Pith. "Pith review of GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation." pith.science (2026). https://pith.science/paper/EDLAMBM7

@misc{pith2026260218164,
  author       = {Pith},
  title        = {Pith review of: GrandTour: A Legged Robotics Dataset in the Wild for Multi-Modal Perception and State Estimation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EDLAMBM7}},
  note         = {Machine review of arXiv:2602.18164}
}
read the original abstract

Accurate state estimation and multi-modal perception are prerequisites for autonomous legged robots in complex, large-scale environments. To date, no large-scale public legged-robot dataset captures the real-world conditions needed to develop and benchmark algorithms for legged-robot state estimation, perception, and navigation. To address this, we introduce the GrandTour dataset, a multi-modal legged-robotics dataset collected across challenging outdoor and indoor environments, featuring an ANYbotics ANYmal-D quadruped equipped with the Boxi multi-modal sensor payload. GrandTour spans a broad range of environments and operational scenarios across distinct test sites, ranging from alpine scenery and forests to demolished buildings and urban areas, and covers a wide variation in scale, complexity, illumination, and weather conditions. The dataset provides time-synchronized sensor data from spinning LiDARs, multiple RGB cameras with complementary characteristics, proprioceptive sensors, and stereo depth cameras. Moreover, it includes high-precision ground-truth trajectories from satellite-based RTK-GNSS and a Leica Geosystems total station. This dataset supports research in SLAM, high-precision state estimation, and multi-modal learning, enabling rigorous evaluation and development of new approaches to sensor fusion in legged robotic systems. With its extensive scope, GrandTour represents the largest open-access legged-robotics dataset to date. The dataset is available at https://grand-tour.leggedrobotics.com on HuggingFace (ROS-independent), and in ROS formats, along with tools and demo resources.

Figures

Figures reproduced from arXiv: 2602.18164 by the authors.

Figure 1
Figure 1. GrandTour dataset preview. Top: views of ANYmal traversing diverse environments during GrandTour, inset summarizes sensor suite and dataset scale (49 missions, >10 km, >5 h). Bottom: aerial imagery alongside on-board RGB views from a subset of the cameras for six missions of GrandTour (ETH-1, PIL-1, EIG-1, SPX-2, HEAP-1, ARC-2). The path color indicates the frequency with which the robot is at that location. sensors… view at source ↗
Figure 2
Figure 2. Sensor placement visualization of the entire GrandTour sensor suite. A) Shows the spatial relationships of the sensors on the Boxi payload, and B) shows the sensors of the ANYmal base platform components. Each colored axis represents a sensor or joint frame. For brevity, repeated sensors are not shown (e.g., 6× Intel RealSense D435i depth cameras). stereo, and multi-view imaging. The suite further contains seven IMU… view at source ↗
Figure 3
Figure 3. System architecture and sensor interfaces of the combined Boxi–ANYmal platform. All compute units (Jetson AGX Orin, Intel NUC, Raspberry Pi) are connected to the UbiSwitch Ethernet device, and the sensors are connected to their respective compute platforms via various interfaces, such as USB 3.1, GMSL2, RJ45, and I²C. Name Description Time Synchronization† LiDARs (Livox) Livox Mid-360 vFoV: 59◦ , Range: 0.1 m, 40 m,… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Top-down projection of range observations around the robot in GrandTour (scale bar: 3 m), illustrating the complementary coverage of the sensor suite. A) Combined view with color-coding: Hesai LiDAR (red), Livox LiDAR (blue), VLP16 (green), and ANYmal-Depth cameras (ye…
Figure 5
Figure 5. Figure 5: The calibration provided with the GrandTour dataset is validated through the overlay of point clouds from different LiDARs onto all available RGB images of GrandTour. As shown, the point clouds align with the correct visual features in the image. Points are colorized b…
Figure 6
Figure 6. Figure 6: A colorized point cloud map of the HAUS-1 sequence is shown to highlight the accuracy of the sensor calibration. A) and B) shows the front-center colored CoreResearch camera from different moments during the sequence, whereas C) shows the colorized point cloud map gene…
Figure 7
Figure 7. Figure 7: GNSS coverage and semantic diversity of the dataset. A) Number of sequences recorded with and without GNSS. B) The most common intersections of semantic labels across all missions, illustrating how weather, environment, terrain, and hazard conditions co-occur. C) Numbe…
Figure 9
Figure 9. Figure 9: Holistic Fusion-based 6-DoF ground truth generation is shown for the SPX-2 sequence of GrandTour. As expected, when the MS60 line-of-sight is obstructed, the standard deviation of the estimate increases. • SPX-2: On the Jungfraujoch research station, dynamic initializa…
Figure 8
Figure 8. Figure 8: Data format overview. A) Zarr layout on HuggingFace. Topics are stored as multi-arrays; for instance, the hdr_front contains a timestamp and sequence_id. For images, the sequence_id can be used to retrieve the corresponding .jpeg file. Metadata, including intrinsics, e…
Figure 10
Figure 10. Figure 10: Data collection methodology for the GrandTour dataset. The ANYmal robot equipped with the Boxi sensor payload traverses environments with varying GNSS availability (red circle indicates denied regions) and total station visibility constraints (yellow circle). The depl…
Figure 11
Figure 11. Figure 11: GrandTour dataset example from ANYmal showing synchronized proprioceptive signals over a 5 s window (15 s to 20 s from bag start). From top to bottom: LF_HFE joint position (state-estimator output, SEA-measured position, and commanded/target position), binary foot con…
Figure 12
Figure 12. Figure 12: Overview of GrandTour applications for perception, locomotion, and navigation. (A) Online mesh generation with SLAMesh (Ruan et al., 2023). (B) Prediction of friction parameters from RGB images using training data collected via proprioceptive terrain interaction (Chen…

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Forward citations

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    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.