Rock Segmentation, Matching and 3D Reconstruction Based on Stereo Vision
Abstract
Rock evaluation and return are important work of lunar exploration, they should be done by the lunar rover’s hardware and software. A series of algorithms related to rock segmentation, stereo matching and 3D reconstruction which are realized by stereo vision are described in this paper. The experiments based on real image show the validity of the algorithms.
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?
As mission distance increases, planetary rovers increasingly require safer, more real-time, and more accurate navigation systems.
“As the mission distance increase, the planetary rovers increasingly require safer, more real-time, and more accurate navigation systems [29,86,87].”
From Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers—A Systematic Literature Review (2022) · cites this paper as [29]
Reference [29] applied K-mean cluster to ensure safe wandering for the planetary rover; reference [11] utilized Mask-R-CNN to support the Mars sample return mission; and reference [28] used the mean-shift algorithm for geological analysis.
“For example, reference [29] applied the K-mean cluster to ensure safe wandering for the planetary rover; reference [11] utilized Mask-R-CNN to support the Mars sample return mission; and reference [28] used the mean-shift algorithm for geological analysis.”
From Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers—A Systematic Literature Review (2022) · cites this paper as [29]
The citation lists haven’t been fetched yet
References 4 · Cited by 1
Research works printed but not linked: 5
- david, 2005, Data Mining During Rover Traverse: From Images to Geologic Signatures, 8th International Symposium on Artificial Intelligence Robotics and Automation in Space, 324
- 10.1029/1999JE001182 · DOI
- 10.1109/IRDS.2002.1041491 · DOI
- song, 2008, Automated Rock Segmentation for Mars Exploration Rover Imagery, 39th Lunar and Planetary Science Conference, 2043
- castano, 2007, Onborad Autonomous Rover Science, Proc IEEE Aerospace Conference, 1
- Authors
- Gao, H., Li, B., Yu, Y., & Chen, L.
- Affiliations
- Shenyang Ligong University (CN)
- Venue
- 2010 Third International Conference on Intelligent Networks and Intelligent Systems
- Publisher
- IEEE
- Published
- 2010-11
- OpenAlex
- W2067425414
- Cited by
- 1
- References
- 4
- Citations by year
- 2022: 1
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Intelligent Networks and Intelligent Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- stereo matching, 3D reconstruction, stereo vision, lunar rover, lunar exploration, lunar surface imaging
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
