The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Visually guided landing of an unmanned aerial vehicle
Abstract
We present the design and implementation of a real-time, vision-based landing algorithm for an autonomous helicopter. The landing algorithm is integrated with algorithms for visual acquisition of the target (a helipad) and navigation to the target, from an arbitrary initial position and orientation. We use vision for precise target detection and recognition, and a combination of vision and Global Positioning System for navigation. The helicopter updates its landing target parameters based on vision and uses an onboard behavior-based controller to follow a path to the landing site. We present significant results from flight trials in the field which demonstrate that our detection, recognition, and control algorithms are accurate, robust, and repeatable.
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
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
A visual odometer for autonomous helicopter flight
Amidi, O., Kanade, T., & Fujita, K. (1999) · Robotics and Autonomous Systems · doi:10.1016/s0921-8890(99)00016-0 · Cited by 148
Cited by
Han, W., Zhang, X., Wang, Y., Wang, L., Huang, X., Li, J., Wang, S., Chen, W., Li, X., Feng, R., et al. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.05.032 · Cited by 303
Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter
Wen, W., Pfeifer, T., Bai, X., & Hsu, L. (2021) · NAVIGATION · doi:10.1002/navi.421 · Cited by 259
Metadata
- Authors
- Saripalli, S., Montgomery, J. F., & Sukhatme, G. S.
- Venue
- IEEE Transactions on Robotics and Automation
- Published
- 2003-06
- Volume
- 19
- Issue
- 3
- Pages
- 371-380
- ISSN
- 1042-296X (ISSN-L)
- OpenAlex
- W2158598687
- Cited by
- 504
- References
- 2
- FWCI
- 31.36
- Type
- journal article
- Language
- en
- Open access
- closed
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: Visually guided landing of an unmanned aerial vehicle. https://app.cochat.ai/papers/10.1109/tra.2003.810239
- The paper
- Saripalli, S., Montgomery, J. F., & Sukhatme, G. S. (2003). Visually guided landing of an unmanned aerial vehicle. IEEE Transactions on Robotics and Automation. https://doi.org/10.1109/tra.2003.810239
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.