A crater detection and identification algorithm for autonomous lunar landing
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
[3]'s crater identification method was successful if the position information provided was very close to the true position, but resulted in a large number of mismatches otherwise.
“This approach was successful if the position information provided was very close to the true position, but resulted in a large number of mismatches otherwise.”
From Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection (2020) · cites this paper as [3]
Previous crater detectors based on traditional image processing methodologies like thresholding, edge detection, and filtering are highly sensitive to lighting conditions, noise, camera parameters, and viewing angles, and require precise modeling and specific tuning for different sets of images.
“Previous works on lunar TRN systems have developed crater detectors that are based upon traditional image processing methodologies like thresholding, edge detection, and filtering [3].These crater detectors are highly sensitive to lighting conditions, noise, camera parameters, and viewing angles, and require precise modeling and specific tuning for different sets of images.”
From Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection (2020) · cites this paper as [3]
The system of [3] used a crater detector based on traditional image processing methods, referred to as the trinary edge detector due to its method of finding craters through trinary thresholding and edge detection.
“This lunar terrain relative navigation system used a crater detector based on traditional image processing methods, which we refer to as the trinary edge detector due to its method of finding craters through trinary thresholding and edge detection.”
From Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection (2020) · cites this paper as [3]
[3] demonstrated the potential for craters to improve the state estimation of a spacecraft in lunar orbit, but struggled to consistently detect the same craters from frame to frame in a trajectory and experienced significant sensitivity to image quality, brightness, and shadowing.
“This work demonstrated the potential for craters to improve the state estimation of a spacecraft in lunar orbit.However, it struggled to consistently detect the same craters from frame to frame in a trajectory and experienced significant sensitivity to image quality, brightness, and shadowing.”
From Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection (2020) · cites this paper as [3]
Cites
Russell, E. L., Chiang, L. H., & Braatz, R. D. (2000) · Advances in industrial control · doi:10.1007/978-1-4471-0409-4_3 · Cited by 7,938 · see citing works
Vision-Aided Inertial Navigation for Spacecraft Entry, Descent, and Landing
Mourikis, A. I., Trawny, N., Roumeliotis, S. I., Johnson, A. E., Ansar, A., & Matthies, L. (2009) · IEEE Transactions on Robotics · doi:10.1109/tro.2009.2012342 · Cited by 345 · see citing works
ALHAT System Architecture and Operational Concept
Brady, T., & Schwartz, J. L. (2007) · Proceedings - IEEE Aerospace Conference · doi:10.1109/aero.2007.352725 · Cited by 27 · see citing works
VISION-BASED AUTONOMOUS APPROACH AND LANDING FOR AN AIRCRAFT USING A DIRECT VISUAL TRACKING METHOD
Gonçalves, T., Azinheira, J. R., & Rives, P. (2009) · International Conference on Informatics in Control, Automation and Robotics (ICINCO) · doi:10.5220/0002212900940101 · Cited by 16 · see citing works
Autonomous Craters Detection from Planetary Image
Ding, M., Cao, Y., & Wu, Q. (2008) · International Conference on Innovative Computing, Information and Control · doi:10.1109/icicic.2008.181 · Cited by 16 · see citing works
Visual Landmark Constellation Matching for Spacecraft Pinpoint Landing
Pham, B. V., Lacroix, S., Devy, M., et al. (2009) · AIAA Guidance, Navigation, and Control Conference and Exhibit · doi:10.2514/6.2009-5663 · Cited by 15 · see citing works
Technology for Autonomous Optical Planetary Navigation and Precision Landing
Weismuller, T., Caballero, D. E., & Leinz, M. R. (2007) · AIAA SPACE Conference & Exposition · doi:10.2514/6.2007-6173 · Cited by 13 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection
Downes, L. M., Steiner, T. J., & How, J. P. (2020) · 2020 American Control Conference (ACC) · doi:10.23919/acc45564.2020.9147595 · Cited by 35 · see citing works
- Authors
- Clerc, S., Spigai, M., & Simard-Bilodeau, V.
- Affiliations
- Thales Alenia Space (France) (FR)
- Corresponding author
- Clerc, Sébastien
- Spigai, Marc
- ORCID
- Clerc, Sébastien
- Venue
- IFAC Proceedings Volumes
- Publisher
- Elsevier BV
- Published
- 2010
- Volume
- 43
- Issue
- 16
- Pages
- 527-532
- ISSN
- 1474-6670 (ISSN-L)
- OpenAlex
- W1967382022
- Cited by
- 15
- References
- 7
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 1 · 2022: 3 · 2021: 2 · 2020: 2 · 2018: 1 · 2013: 1
- FWCI
- 0.64
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IFAC Proceedings Volumes (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Object Detection Techniques
- Keywords
- crater detection, crater identification, camera pose estimation, visual feature detection, image segmentation
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
