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arxiv: 2210.09114 · v2 · submitted 2022-10-17 · 💻 cs.RO

INSANE: Cross-Domain UAV Data Sets with Increased Number of Sensors for developing Advanced and Novel Estimators

Christian Brommer , Alessandro Fornasier , Martin Scheiber , Jeff Delaune , Roland Brockers , Jan Steinbrener , Stephan Weiss This is my paper

Pith reviewed 2026-05-24 11:02 UTC · model grok-4.3

classification 💻 cs.RO
keywords UAV datasetsMAV localizationsensor fusionground truthIMUcross-domain navigationMars analogvibration capture
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The pith

The INSANE data sets supply MAV trajectories across indoor, transitional, and Mars-analog environments with multiple IMUs, cameras, and dual-RTK ground truth for localization research.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper releases the INSANE collection of Micro Aerial Vehicle data sets to support development of localization methods that work across changing environments. Scenarios increase in difficulty from indoor motion-capture rooms, through outdoor-to-indoor transitions that force sensor changes, to fully outdoor flights in a Mars-analog site. Each recording supplies raw measurements from an extended sensor suite that includes several IMUs and cameras, plus a dedicated high-rate IMU that captures vibration dynamics. Accurate ground truth comes from dual real-time kinematic GNSS in the outdoor cases, stated to reach centimeter and sub-degree precision. The data and post-processing tools are offered to let researchers test and improve estimators under realistic conditions.

Core claim

The INSANE data sets provide MAV recordings that span controlled indoor motion-capture trajectories, outdoor-to-building transitions requiring modality switches, and purely outdoor flights in a Mars-analog setting; each set supplies raw data from multiple IMUs and cameras together with a high-rate IMU for vibration capture and highly accurate ground truth obtained via dual RTK GNSS at sub-degree and centimeter level.

What carries the argument

The INSANE data sets, which bundle raw multi-IMU and camera measurements with dual-RTK GNSS ground truth across graded environmental scenarios.

If this is right

  • Localization estimators can be tested on trajectories that force repeated changes between sensor modalities.
  • The high-rate IMU recordings enable training of machine-learning methods that reduce vibration-induced errors in inertial data.
  • Cross-environment data sets allow direct comparison of estimator performance from fully controlled indoor settings to Mars-analog outdoor conditions.
  • Raw sensor outputs support development of sensor-fusion pipelines that must handle realistic dynamics and noise without pre-filtering.
  • Availability of the data sets with post-processing tools makes results from different research groups directly comparable.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Navigation algorithms developed on these sets could be transferred more readily to future planetary rotorcraft that must operate without reliable GNSS.
  • The multi-IMU configuration may expose whether estimator robustness improves simply by adding redundant inertial channels.
  • The graded difficulty levels could serve as a benchmark ladder for measuring incremental progress in cross-domain localization.
  • Vibration-focused recordings open the possibility of studying how structural resonances of specific airframes affect particular estimator architectures.

Load-bearing premise

The dual RTK GNSS setup actually delivers the stated centimeter and sub-degree accuracy under the outdoor flight conditions described, and the raw sensor streams faithfully contain the real vibration and noise effects present in flight.

What would settle it

An independent high-precision positioning system run in parallel on one outdoor flight whose positions differ from the published dual-RTK ground truth by more than the claimed 1-sigma bounds.

Figures

Figures reproduced from arXiv: 2210.09114 by Alessandro Fornasier, Christian Brommer, Jan Steinbrener, Jeff Delaune, Martin Scheiber, Roland Brockers, Stephan Weiss.

Figure 1
Figure 1. Figure 1: This diagram shows the embedded companion boards and sensor [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: This illustration shows the vehicle design with coordinate frames for the placement and orientation of the sensors on the experiment platform in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The sensor suite has three IMUs where each serves a different aspect. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Example image of the outdoor data set, showing the over-lapping [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: This illustration shows the co-mounted camera and laser range [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Tested EMI preventive variations for dedicated RTK Antenna shield [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The graph shows the placement of individual fiducial markers and [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The image shows the vibration test bench to which the vehicle [PITH_FULL_IMAGE:figures/full_fig_p009_10.png] view at source ↗
Figure 14
Figure 14. Figure 14: The entrance of the Dronehall which the aerial vehicle is flying [PITH_FULL_IMAGE:figures/full_fig_p010_14.png] view at source ↗
Figure 12
Figure 12. Figure 12: Location map and flight sectors for the Klagenfurt Dronehall indoor [PITH_FULL_IMAGE:figures/full_fig_p010_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: ArUco fiducial marker field design for the outdoor-indoor transition [PITH_FULL_IMAGE:figures/full_fig_p010_13.png] view at source ↗
Figure 15
Figure 15. Figure 15: This map illustrates the RTK GNSS accuracy in close proximity to the [PITH_FULL_IMAGE:figures/full_fig_p011_15.png] view at source ↗
Figure 17
Figure 17. Figure 17: Indoor motion capture flight patterns. Left: Ascending square trajectory, Middle: Upwards spiral, Right: Translations with pick and place elements. IV. EXPERIMENT - DATA This section describes the patterns and scenarios which were performed for each location. In general, the majority of data sets have an initialization phase. This initialization phase consists of a smooth impulse in the direction of the I… view at source ↗
Figure 18
Figure 18. Figure 18: This graph shows the change of the magnetic field during the [PITH_FULL_IMAGE:figures/full_fig_p013_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Trajectory and overlays for the outdoor to indoor transition data sets. [PITH_FULL_IMAGE:figures/full_fig_p013_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Representative image of a crater wall in the Negev desert. This [PITH_FULL_IMAGE:figures/full_fig_p013_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Trajectory and map overlays for the Mars analog data sets. Zone 1 shows the habitat in a small valley where a part of the cliff mapping data was [PITH_FULL_IMAGE:figures/full_fig_p014_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Calibration of PX4 rate values (left), and resonant frequencies as a function of motor RPM (right). As mentioned before, we chose to use individual raw measurements for the generation of the ground truth data because of the high measurement accuracy. Using a recursive [PITH_FULL_IMAGE:figures/full_fig_p014_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Prediction of spectral resonances based on PX4 rate values (color) [PITH_FULL_IMAGE:figures/full_fig_p015_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Ground truth sensor frame setup. RTK GNSS and Magnetome [PITH_FULL_IMAGE:figures/full_fig_p015_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Alignment of the ground truth segments for the outdoor area, the [PITH_FULL_IMAGE:figures/full_fig_p016_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: State-of-the-art EKF filtering results for transition data set one, [PITH_FULL_IMAGE:figures/full_fig_p017_26.png] view at source ↗
Figure 27
Figure 27. Figure 27: Illustrative visual inertial localization example using a planar Mars [PITH_FULL_IMAGE:figures/full_fig_p017_27.png] view at source ↗
Figure 29
Figure 29. Figure 29: Calibration directory [PITH_FULL_IMAGE:figures/full_fig_p020_29.png] view at source ↗
Figure 28
Figure 28. Figure 28: Main data directory calibration nav_cam.yaml stereo_cam.yaml gps.yaml lsm_imu.yaml px4_imu.yaml rs_imu.yaml lsd_mag.yaml px4_mag.yaml rs_odom.yaml tags.yaml uwb.yaml lrf.yaml time_ref.yaml segment_alignment.yaml [PITH_FULL_IMAGE:figures/full_fig_p020_28.png] view at source ↗
read the original abstract

For real-world applications, autonomous mobile robotic platforms must be capable of navigating safely in a multitude of different and dynamic environments with accurate and robust localization being a key prerequisite. To support further research in this domain, we present the INSANE data sets - a collection of versatile Micro Aerial Vehicle (MAV) data sets for cross-environment localization. The data sets provide various scenarios with multiple stages of difficulty for localization methods. These scenarios range from trajectories in the controlled environment of an indoor motion capture facility, to experiments where the vehicle performs an outdoor maneuver and transitions into a building, requiring changes of sensor modalities, up to purely outdoor flight maneuvers in a challenging Mars analog environment to simulate scenarios which current and future Mars helicopters would need to perform. The presented work aims to provide data that reflects real-world scenarios and sensor effects. The extensive sensor suite includes various sensor categories, including multiple Inertial Measurement Units (IMUs) and cameras. Sensor data is made available as raw measurements and each data set provides highly accurate ground truth, including the outdoor experiments where a dual Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) setup provides sub-degree and centimeter accuracy (1-sigma). The sensor suite also includes a dedicated high-rate IMU to capture all the vibration dynamics of the vehicle during flight to support research on novel machine learning-based sensor signal enhancement methods for improved localization. The data sets and post-processing tools are available at: https://sst.aau.at/cns/datasets

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript presents the INSANE collection of MAV flight datasets spanning controlled indoor motion-capture trajectories, indoor-to-outdoor transitions, and purely outdoor flights in a Mars-analog environment. Each release supplies raw multi-IMU (including a dedicated high-rate unit), camera, and auxiliary sensor streams together with ground-truth poses, the latter obtained from motion capture indoors and a dual-RTK GNSS configuration outdoors that is stated to deliver centimeter-level position and sub-degree attitude accuracy (1-sigma). Post-processing tools and the data are made available at a public URL.

Significance. If the released raw measurements accurately reflect real-world vibration and sensor effects and the claimed ground-truth accuracy is substantiated, the datasets would constitute a useful public resource for cross-domain localization benchmarking and for the development of vibration-aware or learning-based estimators.

major comments (1)
  1. [Abstract] Abstract: the claim that the dual-RTK GNSS setup 'provides sub-degree and centimeter accuracy (1-sigma)' for the outdoor experiments is asserted without any supporting error statistics, residual plots, covariance traces, or cross-validation against motion-capture data on transition flights. Because this accuracy figure is central to the assertion of 'highly accurate ground truth' usable for localization benchmarking, the absence of empirical validation is load-bearing.
minor comments (2)
  1. The manuscript would benefit from an explicit table listing, for each dataset, the exact sensor models, sampling rates, and ground-truth source, together with the duration and environment label.
  2. The public URL should be accompanied by a permanent identifier (e.g., DOI or Zenodo record) to ensure long-term accessibility of the data and tools.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review. The single major comment identifies a clear gap in empirical support for the claimed RTK accuracy. We address it directly below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that the dual-RTK GNSS setup 'provides sub-degree and centimeter accuracy (1-sigma)' for the outdoor experiments is asserted without any supporting error statistics, residual plots, covariance traces, or cross-validation against motion-capture data on transition flights. Because this accuracy figure is central to the assertion of 'highly accurate ground truth' usable for localization benchmarking, the absence of empirical validation is load-bearing.

    Authors: We agree that the abstract states the dual-RTK accuracy figure without accompanying validation data or plots. This constitutes a substantive omission for a dataset paper whose primary value rests on the quality of the ground truth. In the revision we will (1) add a dedicated subsection or appendix presenting RTK residual statistics, covariance traces, and position/attitude error histograms from the outdoor flights; (2) include any available cross-checks on the indoor-to-outdoor transition sequences (e.g., short periods where both motion-capture and RTK are simultaneously available); and (3) tone the abstract claim to reflect only what the new empirical material supports. We will also make the raw RTK solution files and processing scripts available so readers can reproduce the accuracy assessment. revision: yes

Circularity Check

0 steps flagged

No derivation chain; data-release paper with no predictions or fitted results

full rationale

The manuscript is a dataset release describing MAV trajectories, sensor suites (multiple IMUs, cameras, high-rate IMU), and ground-truth sources (motion capture indoors, dual RTK GNSS outdoors). It contains no equations, no parameter fitting, no predictions, and no derivation steps that could reduce to inputs by construction. The accuracy statement for RTK GNSS is an assertion about the collection setup rather than the output of any internal model or self-citation chain. No patterns from the enumerated circularity kinds are present.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a dataset release paper. No mathematical derivations, fitted parameters, background axioms, or postulated entities are introduced.

pith-pipeline@v0.9.0 · 5823 in / 1239 out tokens · 28036 ms · 2026-05-24T11:02:10.214602+00:00 · methodology

discussion (0)

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