The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Learning high-speed flight in the wild
Open accessAbstract
Deep Learning enables agile flight in challenging environments with onboard sensing and computation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., Pang, R., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00140 · Cited by 9,708
Stereo Processing by Semiglobal Matching and Mutual Information
Hirschmuller, H. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1166 · Cited by 4,195
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566
Self-citationAn introduction to MCMC for machine learning.
Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning · doi:10.1023/a:1020281327116 · Cited by 2,451
Minimum snap trajectory generation and control for quadrotors
Mellinger, D., & Kumar, V. (2011) · 2011 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980409 · Cited by 2,367
Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments
Richter, C., Bry, A., & Roy, N. (2016) · Springer Tracts in Advanced Robotics · doi:10.1007/978-3-319-28872-7_37 · Cited by 747
Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight
Zhou, B., Gao, F., Wang, L., Liu, C., & Shen, S. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2927938 · Cited by 747
Voxblox: Incremental 3D Euclidean Signed Distance Fields for on-board MAV planning
Oleynikova, H., Taylor, Z., Fehr, M., Siegwart, R., & Nieto, J. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202315 · Cited by 658
CAD2RL: Real Single-Image Flight Without a Single Real Image
Sadeghi, F., & Levine, S. (2017) · Robotics: Science and Systems XIII · doi:10.15607/rss.2017.xiii.034 · Cited by 618
Cited by
Champion-level drone racing using deep reinforcement learning
Kaufmann, E., Bauersfeld, L., Loquercio, A., Müller, M., Koltun, V., & Scaramuzza, D. (2023) · Nature · doi:10.1038/s41586-023-06419-4 · Cited by 710
Self-citationNavigating to objects in the real world
Gervet, T., Chintala, S., Batra, D., Malik, J., & Chaplot, D. S. (2023) · Science Robotics · doi:10.1126/scirobotics.adf6991 · Cited by 125
Metadata
- Authors
- Loquercio, A., Kaufmann, E., Ranftl, R., Müller, M., Koltun, V., & Scaramuzza, D.
- Venue
- Science Robotics
- Published
- 2021-10-13
- Volume
- 6
- Issue
- 59
- Pages
- eabg5810-eabg5810
- ISSN
- 2470-9476 (ISSN-L)
- OpenAlex
- W3202883604
- Cited by
- 375
- References
- 38
- FWCI
- 17.03
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Learning high-speed flight in the wild. https://app.cochat.ai/papers/10.1126/scirobotics.abg5810
- The paper
- Loquercio, A., Kaufmann, E., Ranftl, R., Müller, M., Koltun, V., & Scaramuzza, D. (2021). Learning high-speed flight in the wild. Science Robotics. https://doi.org/10.1126/scirobotics.abg5810
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
