Map-Guided Coarse-to-Fine Lunar Rover Localization via Multiview Crater Matching Article Swipe
Chenming Ye
,
Zhizhong Kang
,
Xue Wan
,
Shuai Shao
,
Juntao Yang
,
Zhen Cao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3594111
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3594111
Deep space investigation has gradually developed from remote sensing to planet surface exploration, and rover survey is the main way for planet surface exploration. Precise localization is essential to ensure the safety of the rover. In the past decades, manual-assisted station visual positioning has been extensively employed, while recent studies on track reckoning and visual odometry have provided a foundation for the advancement of automated localization of rovers. However, the cumulative error generated by long-distance exploration is difficult to avoid without global constraints. In this work, we propose a coarse-to-fine localization method wherein craters are employed as landmarks. The orientation of craters captured by the camera serves as positional information to guide a grid search and matching process within the map, achieving initial coarse localization. The precise edges of the craters are extracted from the panoramic camera (PCAM) images and aligned with the crater rim in the map to refine the localization result, thereby enabling fine localization of the rover. To validate the proposed method, we conducted experiments on datasets from the Chang’e-3 and Chang’e-4 lunar missions, as well as a simulated dataset containing 50 image pairs constructed from a local digital elevation model (DEM) of the lunar surface. The results demonstrate that, starting from an initial estimate with an error margin of less than 50 m, our method significantly enhances localization accuracy, achieving meter-level precision, with most results concentrated within approximately 2 m. Furthermore, the algorithm operates at a subsecond processing speed, which ensures its execution during the rover’s panoramic imaging phase between motion and stop, thereby satisfying the real-time localization requirements of actual mission scenarios.
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https://doi.org/10.1109/jstars.2025.3594111Digital Object Identifier
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Map-Guided Coarse-to-Fine Lunar Rover Localization via Multiview Crater MatchingWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
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Chenming Ye, Zhizhong Kang, Xue Wan, Shuai Shao, Juntao Yang, Zhen CaoList of authors in order
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https://doi.org/10.1109/jstars.2025.3594111Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/jstars.2025.3594111Direct OA link when available
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Impact crater, Computer science, Remote sensing, Astrobiology, Matching (statistics), Geology, Computer vision, Lunar craters, Artificial intelligence, Physics, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
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