Autonomous Navigation via a Cascading CNN Framework Leveraging Synthetic Terrain Images
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?
The test accuracy from the cascading architecture across several illumination conditions was 60-70%, with poor performance in low-illumination conditions.
“However, the test accuracy from this approach across several illumination conditions was 60–70%, with poor performance in low-illumination conditions.”
From Probabilistic regression for autonomous terrain relative navigation via multi-modal feature learning (2024) · cites this paper as [29]
The cascading architecture developed in29 simplified the complex learning task by reducing the number of classes and posed the problem as a 'quadrant-classification' problem instead, improving on the performance for 2D classification.
“The cascading architecture developed in29 simplified the complex learning task by reducing the number of classes and posed the problem as a ‘quadrant-classification’ problem instead, improving on the performance for 2D classification.”
From Probabilistic regression for autonomous terrain relative navigation via multi-modal feature learning (2024) · cites this paper as [29]
The citation lists haven’t been fetched yet
References 8 · Cited by 2
- Authors
- Rolen, A., & Singh, S.
- Affiliations
- Rensselaer Polytechnic Institute (US)
- Corresponding author
- Rolen, Abigail
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Pages
- 529-540
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4389161442
- Cited by
- 2
- References
- 8
- Citations by year
- 2026: 1 · 2024: 1
- FWCI
- 1.06
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Dynamics and Control
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- autonomous spacecraft navigation, powered descent, lunar landing, guidance, navigation and control, image segmentation, course correction, digital terrain model, ray tracing, Lunar Reconnaissance Orbiter
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
