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REVIEW 2 major objections 6 minor 52 references

Long-Duration Fully Autonomous Operation of Rotorcraft Unmanned Aerial Systems for Remote-Sensing Data Acquisition

T0 review · 2 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A rotorcraft UAS with an on-board autonomy engine can run repeated data-collection flights and recharge itself without human intervention.

desk verdict A genuine outdoor field demonstration of repeated autonomous landing and recharging, backed by credible landing-accuracy data, but the 'operate indefinitely' claim is unsupported and should be tempered. read the letter →

arxiv 1908.06381 v1 pith:HI4DZPJ4 submitted 2019-08-18 cs.RO cs.CVcs.SYeess.SY

Danylo Malyuta , Christian Brommer , Daniel Hentzen , Thomas Stastny , Roland Siegwart , Roland Brockers This is my paper · ORCID
classification cs.ROcs.CVcs.SYeess.SY
keywords unmannedaerialsystemsrotorcraftlong-durationautonomyvision-basedprecisionlandingAprilTagfiducialsautonomousrechargingremotesensingstatemachines
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

The paper reports a complete autonomy engine for a small rotorcraft that lets it execute repeated remote-sensing flights, return to a charging station, land precisely on it, recharge, and take off again, all without a human in the loop. The authors claim the software and hardware interfaces are sufficient for indefinite operation, and they demonstrate the full loop in an 11-hour indoor test with 16 flights, a 10.6-hour indoor test with 48 flights, and a 4-hour outdoor free-flight test with 22 flights. A sympathetic reader would care because long-term observation, such as tracking plant water use over a diurnal cycle, currently requires people to swap batteries or recharge; this system aims to remove that constraint. The key enablers are a vision-based precise landing system using a bundle of AprilTag markers and a hierarchical state-machine architecture that coordinates takeoff, mission, landing, and emergency behaviors.

What carries the argument

The load-bearing mechanism is the landing-pad pose estimator built on a bundle of AprilTag fiducials. A downfacing monocular camera supplies images to the AprilTag detector; detected tags are combined by a perspective-n-point solver into one bundle pose, and a recursive least-squares filter outputs a smoothed pad pose. Because the pad is assumed level, the filter outputs only a yaw quaternion, and the desired landing alignment follows from Eqs. (2) and (3). This pose estimate drives the landing autopilot and closes the recharging cycle. The second mechanism is the master/slave state machine hierarchy that coordinates takeoff, mission, landing, emergency landing, and health monitoring behaviors.

What would settle it

Place the charging pad on a surface tilted by a few degrees and run the outdoor mission; if the system still centers and connects reliably, the level-pad assumption is not load-bearing, but if it misaligns, fails touchdown detection, or misses charging contacts, the central claim of indefinite unattended operation is falsified for uneven terrain.

Watch

Extended reading notes

Core claim

The central claim is that the loop of takeoff, waypoint mission, vision-based precision landing, recharging, and data download can be closed entirely on board, with no operator action after a start command. The paper reports 16, 48, and 22 fully autonomous flights in the three experiments, and states that this is the first published long-term outdoor quadrotor operation without human interaction. The discovery, if correct, is not a new sensing modality but a systems integration result: the autonomy engine, combined with a landing-pad pose estimator, makes sustained unattended rotorcraft sensing practical at the demonstrated scale.

Load-bearing premise

The system assumes the charging pad is perfectly level, so the vision filter ignores roll and pitch of the pad; if the real outdoor pad tilts, the landing alignment and touchdown detection can carry that error and the electrical contacts may not connect.

Editorial extensions

If this is right

  • Repeated diurnal remote-sensing missions, such as monitoring plant water use over a full day, can be scheduled across many charge-discharge cycles without personnel on site.
  • The measured outdoor landing accuracy, with a 2-sigma lateral error below 0.37 meters, is sufficient for the 90 by 90 centimeter charging surface used in the experiments.
  • The autonomy engine is modular enough that the same state-machine design can be ported to other rotorcraft with a downfacing camera and a compatible charging pad.
  • The observed flight-to-charge time ratio of about 1 to 10 matches earlier work and is limited by battery and charger design, not by the autonomy logic itself.
  • The full-cycle operation was demonstrated only over hours, not days, so longer unattended deployments remain an open extension of the same architecture.

Reading between the lines

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

  • If the level-pad assumption were relaxed to estimate the pad's full six-degree-of-freedom pose, the same architecture could land on slightly sloped or even gently moving pads; the paper's own equations show that a tilted pad injects roll and pitch error into the desired landing alignment.
  • The outdoor exposure problems the authors observed could likely be mitigated with event-based cameras, which they mention as a future option; a testable extension is whether such cameras improve landing accuracy under fast-changing cloud cover.
  • The 4-hour outdoor run is short relative to the claimed indefinite operation, so a multi-day outdoor deployment would be the natural stress test; the paper's own lessons-learned list points to dust on charging contacts, GPS interference, and software stability as likely failure sources.
  • The flight-to-charge ratio of 1 to 10 suggests that for practical precision agriculture use, the next bottleneck is energy density and charging speed, not autonomous decision-making.
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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

2 major / 6 minor

Summary. The manuscript describes a rotorcraft UAS autonomy architecture for repeated remote-sensing missions without human operators. The system comprises a master/slave state-machine hierarchy coordinating takeoff, mission execution, vision-based precision landing, recharging, and data downlink, together with a landing-pad pose estimator based on an AprilTag bundle and recursive least squares filtering. The authors evaluate landing accuracy with 200 simulated, 27 indoor (VICON), and 21 outdoor (RTK-GPS) landings, and report long-duration experiments: 11 h (16 flights) and 10.6 h (48 flights) indoors and 4 h (22 flights) outdoors, all autonomously executed. The paper's stated central contribution is that the autonomy engine provides the logic and software-hardware interfaces needed to operate indefinitely without humans in the loop.

Significance. The reported integration is a useful systems-level demonstration. The landing-accuracy evaluation against independent ground truth is concrete, and the state-machine logs provide evidence that the full cycle of takeoff, mission, landing, and recharging can be repeated automatically for at least several hours, including outdoors. The bundle-layout study quantifies a genuine design tradeoff among tag size, detection range, and lever-arm error. If the claims are restricted to bounded-duration autonomy, this is a solid contribution to the long-duration UAS literature. The indefinite-operation claim, however, is not established, and the paper itself documents failure modes that would interrupt the loop and require human action.

major comments (2)
  1. [Section 1.2, 8.3.1, 9] The claim in Section 1.2 that 'once the autonomy engine is started, the system has the logic and software-hardware interfaces necessary to operate indefinitely without humans in the loop' is not supported by the reported evidence and is contradicted by the paper's own account. Section 8.3.1 reports that dust on the charging pad has 'at several points prevented a proper contact' and hence prevented recharging, and that the AscTec MAV framework 'frequently crash[es] after long periods of operation'; Section 9 states that the implementation is 'not robust to individual ROS node crashes' and that no supervisory restart mechanism has been implemented. Each of these failure modes requires human intervention to restore the autonomy loop, so the strongest supported claim is bounded-duration autonomy over the demonstrated 11 h, 10.6 h, and 4 h experiments. The authors should either remove the indefinite-operation claim or provide evidence of automatic recovery from these failure classes.
  2. [Section 4.1, Eq. (2), Eq. (5)] The landing-pad pose estimator deliberately returns only a yaw quaternion because the pad is assumed to be level. This assumption is load-bearing for the recharging cycle: if the outdoor charging surface is not flat, the alignment commands in Eqs. (2)-(3) inherit roll and pitch error, the touchdown detection in Eq. (5) may fire at an incorrect height, and the charging contacts may fail to connect. No experiment with a deliberately tilted or uneven pad is reported, so this failure mode is untested. At minimum, the paper should state this assumption explicitly as a scope condition and discuss its implications for the generality of the demonstrated autonomy.
minor comments (6)
  1. [Section 1.1 and Figure 1] The caption describes a hexacopter platform, while the text at the end of Section 1.1 says the autonomy engine can be deployed on 'such a quadrotor'; please align the terminology.
  2. [Section 4.1 and Figure 8] The text states that the measurement frequency is approximately 7 Hz, while Figure 8 reports a median of 6.5 Hz; make these values consistent.
  3. [Section 5.2 and Figure 14] The description of the volatile trajectory element is hard to follow; a brief formal definition of when and why a volatile element is inserted would improve clarity.
  4. [Section 7.5, Eq. (5)] Please define explicitly the frames in which the quantities p-tilde-sub-w,z and p-tilde-sub-l,z are expressed, since touchdown detection depends on these thresholds.
  5. [Section 8.1.1] The bundle-layout simulation evaluates only the height component of the measurement noise with zero off-axis angle; the paper should state clearly that the optimization is based on this reduced metric rather than full 6-DOF pose accuracy.
  6. [References] Several author names show rendering artifacts from missing diacritics, for example 'A¸ cıkme¸ se' in the reference list; please fix the LaTeX source.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; landing accuracy is benchmarked against external ground truth and the self-citations are not load-bearing.

full rationale

The paper is an experimental systems/robotics paper rather than a derivation chain, so the standard circularity patterns do not apply. The landing-pad pose estimator (Section 4.1) is a standard PnP plus RLS pipeline whose accuracy is evaluated against VICON indoors and RTK-GPS outdoors (Figures 20-22), not against its own assumptions. The bundle calibration (Section 4.1.1, Eq. 1) uses a master tag plus statistical averaging, a conventional calibration procedure; it does not fit any parameter to the landing-error data that is later reported as a prediction. The bundle-layout optimization (Section 8.1.1) is a simulation-based design study, and the real-world results are honestly reported as larger than the single-tag prediction (Section 8.1), so no fitted input is disguised as a prediction. The paper does cite prior work by overlapping authors: 'Our approach is the same as that of (Brommer et al., 2018)' and computational details are delegated to (Malyuta, 2018), but these citations are not used to prove any empirical claim; the current paper's own flights, VICON/RTK ground-truth comparisons, and reported failure modes carry the evidence. The strongest caveat is not circularity: Section 1.2's claim that the system can 'operate indefinitely without humans in the loop' is contradicted by the paper's own Lessons Learned (Section 8.3.1: dust prevents charging contact; AscTec MAV framework 'frequently crash[es] after long periods of operation') and by Section 9 ('not robust to individual ROS node crashes'). That is an unsupported or overstated reliability/duration claim, not a claim that reduces by construction to its inputs. No circular step is identifiable under the required standard of quoting a specific reduction between equations or fitted parameters.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central claim rests on engineering assumptions about sensor accuracy, pad geometry, and software reliability rather than on new physics or invented entities. The main free parameters are hand-chosen thresholds and the tag bundle design, all documented in the paper. No new particles, forces, or conserved quantities are introduced.

free parameters (6)
  • Approach altitude halign = 4 m
    Chosen as the hover altitude before final descent; used in Eq. (3) and evaluated with the selected AprilTag bundle (Section 8.1).
  • Touchdown altitude threshold = 0.3 m
    Height below which zero vertical velocity indicates touchdown, Eq. (5); hand-chosen threshold.
  • Touchdown velocity threshold = 0.1 m/s
    Vertical speed threshold for touchdown detection, Eq. (5); hand-chosen threshold.
  • Emergency landing velocity threshold = 0.1 m/s
    Total speed threshold for emergency touchdown, Eq. (6); hand-chosen threshold.
  • Motor RPM check tolerance = 400 RPM
    Motor health check before takeoff (Section 7.3); hand-chosen tolerance.
  • AprilTag bundle tag sizes = 48 cm master plus three 15 cm tags
    Selected from single-tag distance accuracy data in Figure 19 to cover the 4 m approach altitude and close range; hand-chosen design.
assumptions (4)
  • domain assumption The landing pad is level, so only yaw is estimated from the AprilTag bundle and pad roll/pitch are ignored.
    Stated in Section 4.1 before Eq. (2); if the outdoor pad tilts, the alignment and touchdown logic could fail.
  • domain assumption AprilTag 2 detection and the perspective-n-point solver provide unbiased, sufficiently accurate pose measurements at about 7 Hz.
    The landing pipeline depends entirely on these measurements; accuracy is evaluated empirically (Figures 19-21), but no formal guarantee is given.
  • domain assumption GPS provides bounded position error outdoors so the vehicle can return to the charging pad vicinity and the vision system can acquire the bundle.
    Used in Section 4.2 and Section 7.5; the paper notes that GPS can degrade from electromagnetic interference (Section 8.3.1).
  • standard math Quadrotor dynamics are differentially flat, and the polynomial trajectories are dynamically feasible.
    Used in Sections 5 and 6, citing Mellinger and Kumar (2011); a standard result in quadrotor trajectory generation.

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Cite this review

Pith. "Pith review of Long-Duration Fully Autonomous Operation of Rotorcraft Unmanned Aerial Systems for Remote-Sensing Data Acquisition." pith.science (2026). https://pith.science/paper/HI4DZPJ4

@misc{pith2026190806381,
  author       = {Pith},
  title        = {Pith review of: Long-Duration Fully Autonomous Operation of Rotorcraft Unmanned Aerial Systems for Remote-Sensing Data Acquisition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HI4DZPJ4}},
  note         = {Machine review of arXiv:1908.06381}
}
read the original abstract

Recent applications of unmanned aerial systems (UAS) to precision agriculture have shown increased ease and efficiency in data collection at precise remote locations. However, further enhancement of the field requires operation over long periods of time, e.g. days or weeks. This has so far been impractical due to the limited flight times of such platforms and the requirement of humans in the loop for operation. To overcome these limitations, we propose a fully autonomous rotorcraft UAS that is capable of performing repeated flights for long-term observation missions without any human intervention. We address two key technologies that are critical for such a system: full platform autonomy to enable mission execution independently from human operators and the ability of vision-based precision landing on a recharging station for automated energy replenishment. High-level autonomous decision making is implemented as a hierarchy of master and slave state machines. Vision-based precision landing is enabled by estimating the landing pad's pose using a bundle of AprilTag fiducials configured for detection from a wide range of altitudes. We provide an extensive evaluation of the landing pad pose estimation accuracy as a function of the bundle's geometry. The functionality of the complete system is demonstrated through two indoor experiments with a duration of 11 and 10.6 hours, and one outdoor experiment with a duration of 4 hours. The UAS executed 16, 48 and 22 flights respectively during these experiments. In the outdoor experiment, the ratio between flying to collect data and charging was 1 to 10, which is similar to past work in this domain. All flights were fully autonomous with no human in the loop. To our best knowledge this is the first research publication about the long-term outdoor operation of a quadrotor system with no human interaction.

Figures

Figures reproduced from arXiv: 1908.06381 by the authors.

Figure 1
Figure 1. Example deployment of a hexacopter UAS (a) with a hyperspectral camera (b) to collect data for [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Autonomous UAS data acquisition cycle. 1: the vehicle is placed on the landing station to start the [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Landing station with visual bundle (left), charging pad (right), and UAS. The recharging process [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (25 more)
Figure 4
Figure 4. Figure 4: Hardware elements on our test UAS (modified AscTec Pelican). [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: On-board hardware architecture and information flow. Solid and dashed lines represent information [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Sensor and software system block diagram. Gray blocks are part of the software subsystem. [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Vision-based landing navigation block diagram. A downfacing camera feeds images to a radial [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Landing bundle pose measurement frequency. The median is 6.5 Hz. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: Rigid body transforms from each tag’s local frame to the landing pad frame. Generic tags [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: Rigid transform triad formed during the calibration process between the camera frame, the master [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]
Figure 11
Figure 11. Figure 11: Frame setup for pad-aligned body frame pose computation during the initial part of the landing [PITH_FULL_IMAGE:figures/full_fig_p014_11.png]
Figure 12
Figure 12. Figure 12: Illustration of a trajectory and polynomial interpolation points. All quadrotor trajectories are [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]
Figure 13
Figure 13. Figure 13: An example waypoint trajectory. Corresponding UAS body frame, magenta velocity and black [PITH_FULL_IMAGE:figures/full_fig_p017_13.png]
Figure 14
Figure 14. Figure 14: Trajectory sequencing examples using a doubly-linked list which moves the reference quadrotor [PITH_FULL_IMAGE:figures/full_fig_p018_14.png]
Figure 15
Figure 15. Figure 15: Cascaded control loop block diagram with the following nested loops: guidance, translation, [PITH_FULL_IMAGE:figures/full_fig_p018_15.png]
Figure 16
Figure 16. Figure 16: Hierarchy of master state machine and autopilots (gray) and other subsystems. The autopilots [PITH_FULL_IMAGE:figures/full_fig_p020_16.png]
Figure 17
Figure 17. Figure 17: Master state machine diagram. The master state machine parses mission events (e.g. battery [PITH_FULL_IMAGE:figures/full_fig_p021_17.png]
Figure 18
Figure 18. Figure 18: Autopilot logic in flowchart representation. [PITH_FULL_IMAGE:figures/full_fig_p022_18.png]
Figure 19
Figure 19. Figure 19: Distance estimation error of AprilTag 2 detector for various tag sizes and heights. For a given tag [PITH_FULL_IMAGE:figures/full_fig_p024_19.png]
Figure 20
Figure 20. Figure 20: Comparison of landing bundle height estimate with ground truth altitude during a test flight of [PITH_FULL_IMAGE:figures/full_fig_p024_20.png]
Figure 21
Figure 21. Figure 21: Landing bundle height error as a function of flight altitude. The error is approximately 0.32 m [PITH_FULL_IMAGE:figures/full_fig_p024_21.png]
Figure 22
Figure 22. Figure 22: Accuracy of touch-down position in SITL simulation with severe wind (top row), indoors in ideal [PITH_FULL_IMAGE:figures/full_fig_p026_22.png]
Figure 23
Figure 23. Figure 23: Bundles for simulated measurement noise data collection. The goal is to see how individual tag [PITH_FULL_IMAGE:figures/full_fig_p027_23.png]
Figure 24
Figure 24. Figure 24: Height detection noise variance for the simulated tag bundles. Each line corresponds to a tag in [PITH_FULL_IMAGE:figures/full_fig_p028_24.png]
Figure 25
Figure 25. Figure 25: Autonomy engine states over four flights that are representative of the autonomy engine’s domi [PITH_FULL_IMAGE:figures/full_fig_p029_25.png]
Figure 26
Figure 26. Figure 26: Thermal images from the 4 h outdoor experiment as an example mission data product. Brighter [PITH_FULL_IMAGE:figures/full_fig_p030_26.png]
Figure 27
Figure 27. Figure 27: State transitions of the master state machine (top) and battery voltage level (bottom) during the [PITH_FULL_IMAGE:figures/full_fig_p030_27.png]
Figure 28
Figure 28. Figure 28: Breakdown of time spent in each phase of the mission during the 4 h outdoor experiment. About [PITH_FULL_IMAGE:figures/full_fig_p030_28.png]

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Reference graph

Works this paper leans on

52 extracted references · 52 canonical work pages

  1. [1]

    Achtelik, M., Achtelik, M., Weiss, S., and Kneip, L. (2017). asctec mav framework. http://wiki.ros.org/ asctec_mav_framework. Accessed: 2017-12-10. A¸ cıkme¸ se, B. and Ploen, S. R. (2007). Convex programming approach to powered descent guidance for Mars landing. AIAA Journal of Guidance, Control, and Dynamics , 30(5):1353–1366

    work page 2017

  2. [2]

    D., Arteaga, J

    Aldhaher, S., Mitcheson, P. D., Arteaga, J. M., Kkelis, G., and Yates, D. C. (2017). Light-weight wireless power transfer for mid-air charging of drones. In European Conference on Antennas and Propagation (EUCAP). Amazon (2016). Amazon Prime Air’s first customer delivery. https://www.youtube.com/watch?v= vNySOrI2Ny8. Accessed: 2017-12-11. APRIL Laboratory ...

    work page 2017

  3. [3]

    Bencina, R., Kaltenbrunner, M., and Jorda, S. (2005). Improved topological fiducial tracking in the reac- TIVision system. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)

    work page 2005

  4. [4]

    Bergamasco, F., Albarelli, A., Cosmo, L., Rodola, E., and Torsello, A. (2016). An accurate and robust artificial marker based on cyclic codes.IEEE Transactions on Pattern Analysis and Machine Intelligence , 38(12):2359–2373

    work page 2016

  5. [5]

    Blackmore, L., A¸ cıkme¸ se, B., and Scharf, D. P. (2010). Minimum-landing-error powered-descent guidance for Mars landing using convex optimization. AIAA Journal of Guidance, Control, and Dynamics , 33(4):1161–1171

    work page 2010

  6. [6]

    P.-V., Nguyen, D

    Borowczyk, A., Nguyen, D.-T., Nguyen, A. P.-V., Nguyen, D. Q., Saussi´ e, D., and Ny, J. L. (2017). Au- tonomous landing of a multirotor micro air vehicle on a high velocity ground vehicle. In 20th IF AC World Congress

    work page 2017

  7. [7]

    Brescianini, D., Hehn, M., and D’Andrea, R. (2013). Nonlinear quadrocopter attitude control. Technical Report 009970340, ETH Z¨ urich, Departement Maschinenbau und Verfahrenstechnik

    work page 2013

  8. [8]

    Brockers, R., Bouffard, P., Ma, J., Matthies, L., and Tomlin, C. (2011). Autonomous landing and ingress of micro-air-vehicles in urban environments based on monocular vision. In Micro- and Nanotechnology

    work page 2011

Show all 52 references
  1. [9]

    Brockers, R., Susca, S., Zhu, D., and Matthies, L. (2012). Fully self-contained vision-aided navigation and landing of a micro air vehicle independent from external sensor inputs. In SPIE Unmanned Systems Technology XIV

    2012

  2. [10]

    Brommer, C., Malyuta, D., Hentzen, D., and Brockers, R. (2018). Long-duration autonomy for small rotorcraft UAS including recharging. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

    2018

  3. [11]

    W., and Siegwart, R

    Burri, M., Oleynikova, H., Achtelik, M. W., and Siegwart, R. (2015). Real-time visual-inertial mapping, re-localization and planning onboard MAVs in unknown environments. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

    2015

  4. [12]

    M., Wolcott, R

    Chaves, S. M., Wolcott, R. W., and Eustice, R. M. (2015). NEEC research: Toward GPS-denied landing of unmanned aerial vehicles on ships at sea. Naval Engineers Journal , 127(1):23–35

    2015

  5. [13]

    and Chen, C

    Davuluri, P. and Chen, C. (2013). Radio frequency interference due to USB3 connector radiation. In IEEE International Symposium on Electromagnetic Compatibility . de Almeida, M. M. and Akella, M. (2017). New numerically stable solutions for minimum-snap quadcopter aggressive m...

    2013

  6. [14]

    and Faugeras, O

    Devernay, F. and Faugeras, O. (2001). Straight lines have to be straight. Machine Vision and Applications , 13(1):14–24

    2001

  7. [15]

    Faessler, M., Falanga, D., and Scaramuzza, D. (2017). Thrust mixing, saturation, and body-rate control for accurate aggressive quadrotor flight. IEEE Robotics and Automation Letters , 2(2):476–482

    2017

  8. [16]

    Faessler, M., Fontana, F., Forster, C., and Scaramuzza, D. (2015). Automatic re-initialization and failure re- covery for aggressive flight with a monocular vision-based quadrotor. In IEEE International Conference on Robotics and Automation (ICRA)

    2015

  9. [17]

    Fiala, M. (2005). ARTag, a fiducial marker system using digital techniques. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) . Food and Agriculture Organization of the United Nations (2009). 2050: A third more mouths to feed. http://www.fao....

    2005

  10. [18]

    Forster, C., Faessler, M., Fontana, F., Werlberger, M., and Scaramuzza, D. (2015). Continuous on-board monocular-vision-based elevation mapping applied to autonomous landing of micro aerial vehicles. In IEEE International Conference on Robotics and Automation (ICRA)

    2015

  11. [19]

    Forster, C., Pizzoli, M., and Scaramuzza, D. (2014). SVO: Fast semi-direct monocular visual odometry. In IEEE International Conference on Robotics and Automation (ICRA)

    2014

  12. [20]

    Furrer, F., Burri, M., Achtelik, M., and Siegwart, R. (2016). RotorS – a modular gazebo MAV simulator framework. In Koubaa, A., editor, Robot Operating System (ROS) The Complete Reference , volume 1, chapter 23, pages 595–625. Springer. Goldman Sachs (2017). Drones: Reporting ...

    2016

  13. [21]

    S., Weill, L

    Grewal, M. S., Weill, L. R., and Andrews, A. P. (2007). Global Positioning Systems, Inertial Navigation, and Integration. Wiley, Hoboken, NJ

    2007

  14. [22]

    Hast, A., Nysj¨ o, J., and Marchetti, A. (2013). Optimal RANSAC – towards a repeatable algorithm for finding the optimal set. Journal of WSCG , 21(1):21–30

    2013

  15. [23]

    Jin, P., Matikainen, P., and Srinivasa, S. S. (2017). Sensor fusion for fiducial tags: highly robust pose estimation from single frame RGBD. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

    2017

  16. [24]

    Jin, S., Zhang, J., Shen, L., and Li, T. (2016). On-board vision autonomous landing techniques for quadrotor: a survey. In Chinese Control Conference (CCC)

    2016

  17. [25]

    and Murray, D

    Klein, G. and Murray, D. (2007). Parallel tracking and mapping for small AR workspaces. In IEEE and ACM International Symposium on Mixed and Augmented Reality

    2007

  18. [26]

    and Howard, A

    Koenig, N. and Howard, A. (2004). Design and use paradigms for Gazebo, an open-source multi-robot simulator. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

    2004

  19. [27]

    Kyristsis, S., Antonopoulos, A., Chanialakis, T., Stefanakis, E., Linardos, C., Tripolitsiotis, A., and Partsin- evelos, P. (2016). Towards autonomous modular UAV missions: the detection, geo-location and landing paradigm. Sensors, 16(11):1844

    2016

  20. [28]

    Ling, K. (2014). Precision landing of a quadrotor UAV on a moving target using low-cost sensors. Master’s thesis, University of Waterloo, Waterloo

    2014

  21. [29]

    W., Schoellig, A

    Lupashin, S., Hehn, M., Mueller, M. W., Schoellig, A. P., Sherback, M., and D’Andrea, R. (2014). A platform for aerial robotics research and demonstration: the flying machine arena. Mechatronics, 24(1):41–54

    2014

  22. [30]

    Malyuta, D. (2018). Guidance, navigation, control and mission logic for quadrotor full-cycle autonomy. Master’s thesis, ETH Z¨ urich, Z¨ urich

    2018

  23. [31]

    and Kumar, V

    Mellinger, D. and Kumar, V. (2011). Minimum snap trajectory generation and control for quadrotors. In IEEE International Conference on Robotics and Automation (ICRA)

    2011

  24. [32]

    Morbidi, F., Cano, R., and Lara, D. (2016). Minimum-energy path generation for a quadrotor UAV. In IEEE International Conference on Robotics and Automation (ICRA)

    2016

  25. [33]

    Mueller, M. W. and D’Andrea, R. (2012). Critical subsystem failure mitigation in an indoor UAV testbed. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

    2012

  26. [34]

    Mueller, M. W. and D’Andrea, R. (2013). A model predictive controller for quadrocopter state interception. In European Control Conference (ECC)

    2013

  27. [35]

    W., Hehn, M., and D’Andrea, R

    Mueller, M. W., Hehn, M., and D’Andrea, R. (2015). A computationally efficient motion primitive for quadrocopter trajectory generation. IEEE Transactions on Robotics , 31(6):1294–1310

    2015

  28. [36]

    and Kumar, V

    Mulgaonkar, Y. and Kumar, V. (2014). Autonomous charging to enable long-endurance missions for small aerial robots. In Micro- and Nanotechnology Sensors, Systems, and Applications VI

    2014

  29. [37]

    Nissler, C., Buttner, S., Marton, Z.-C., Beckmann, L., and Thomasy, U. (2016). Evaluation and improvement of global pose estimation with multiple AprilTags for industrial manipulators. In IEEE International Conference on Emerging Technologies and Factory Automation (ETF A)

    2016

  30. [38]

    Olson, E. (2011). AprilTag: A robust and flexible visual fiducial system. In IEEE International Conference on Robotics and Automation (ICRA) . OpenCV (2017). cv::solvePnP(). https://docs.opencv.org/3.3.1/d9/d0c/group__calib3d.html# ga549c2075fac14829ff4a58bc931c033d. Accessed: 2...

    2011

  31. [39]

    Rapp, C., Suez, E., Perrad, F., Liscio, C., Arnold, T., and Krajcovic, G. (2017). SMC: The state machine compiler. http://smc.sourceforge.net/. Accessed: 2017-12-10

    2017

  32. [40]

    Richter, C., Bry, A., and Roy, N. (2016). Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments. In Springer Tracts in Advanced Robotics , volume 114, pages 649–666. Springer

    2016

  33. [41]

    Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. In IEEE International Conference on Computer Vision (ICCV)

    2011

  34. [42]

    and Postlethwaite, I

    Skogestad, S. and Postlethwaite, I. (2005). Multivariable feedback control: analysis and design . Wiley, 2 edition. Skysense (2018). Skysense charging pad. https://www.skysense.co/charging-pad-outdoor. Accessed: 2018-10-08

    2005

  35. [43]

    Suzuki, K. A. O., Filho, P. K., and Morrison, J. R. (2011). Automatic battery replacement system for UAVs: analysis and design. Journal of Intelligent & Robotic Systems , 65(1-4):563–586

    2011

  36. [44]

    Toksoz, T., Redding, J., Michini, M., Michini, B., How, J., Vavrina, M., and Vian, J. (2011). Automated battery swap and recharge to enable persistent UAV missions. In AIAA Infotech@Aerospace

    2011

  37. [45]

    P., and Vian, J

    Valenti, M., Dale, D., How, J., de Farias, D. P., and Vian, J. (2007). Mission health management for 24/7 persistent surveillance operations. In AIAA Guidance, Navigation and Control Conference and Exhibit

    2007

  38. [46]

    A., and Gentilini, I

    Vicencio, K., Korras, T., Bordignon, K. A., and Gentilini, I. (2015). Energy-optimal path planning for six-rotors on multi-target missions. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

    2015

  39. [47]

    R., Rebecq, H., Horstschaefer, T., and Scaramuzza, D

    Vidal, A. R., Rebecq, H., Horstschaefer, T., and Scaramuzza, D. (2018). Ultimate SLAM? combining events, images, and IMU for robust visual SLAM in HDR and high-speed scenarios. IEEE Robotics and Automation Letters, 3(2):994–1001

    2018

  40. [48]

    Vincent, J. (2017). Google’s Project Wing has successfully tested its air traf- fic control system for drones. https://www.theverge.com/2017/6/8/15761220/ google-project-wing-drone-air-traffic-control-tests . Accessed: 2017-12-10

    2017

  41. [49]

    and Olson, E

    Wang, J. and Olson, E. (2016). AprilTag 2: Efficient and robust fiducial detection. In IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS)

    2016

  42. [50]

    W., Chli, M., and Siegwart, R

    Weiss, S., Achtelik, M. W., Chli, M., and Siegwart, R. (2012). Versatile distributed pose estimation and sensor self-calibration for an autonomous MAV. In IEEE International Conference on Robotics and Automation (ICRA) . Wingtra (2017). How it works. https://wingtra.com/workfl...

    2012

  43. [51]

    and Dudek, G

    Xu, A. and Dudek, G. (2011). Fourier tag: A smoothly degradable fiducial marker system with configurable payload capacity. In Canadian Conference on Computer and Robot Vision

    2011

  44. [52]

    A., and Zell, A

    Yang, S., Scherer, S. A., and Zell, A. (2012). An onboard monocular vision system for autonomous takeoff, hovering and landing of a micro aerial vehicle.Journal of Intelligent & Robotic Systems , 69(1-4):499–515

    2012

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.