A Coarse-to-Fine Lunar Crater Matching Algorithm with Fast Geo-KD Searching and Robust Triangle Similarity Matching.
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| Title: | A Coarse-to-Fine Lunar Crater Matching Algorithm with Fast Geo-KD Searching and Robust Triangle Similarity Matching. |
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| Authors: | Huang, Jianbin1 (AUTHOR), He, Yuntao2 (AUTHOR) yuntaohe@buaa.edu.cn, Zhang, Yinuo3 (AUTHOR), Li, Xiaolu1 (AUTHOR), Xu, Lijun1,2 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1555. 28p. |
| Subjects: | Lunar craters, Spatial data structures, Navigation (Astronautics), Computer vision |
| Abstract: | Highlights: What are the main findings? A fast Geo-KD searching method is proposed to reduce the scope of candidate craters by combining Geohash coarse screening and KD-tree fine positioning, which reduces computational overhead of subsequent matching and avoids the efficiency bottleneck caused by global traversal of large-scale crater databases. A robust triangle similarity matching algorithm is proposed to improve robustness and efficiency under terrain inclinations and camera tilt angles with neighborhood screening and mismatching elimination, where mismatches are removed using random sample consensus (RANSAC) and local motion consistency (LMC). What are the implications of the main findings? The synergistic design of efficient database search and constrained local matching provides a lightweight and high-precision technical scheme for on-board pose estimation of lunar probes, solving the trade-off between limited computing resources and high-precision navigation requirements in deep space missions. The parameter optimization method for both search and matching stages can be generalized to other planetary surface feature matching tasks (e.g., Martian crater or rock feature matching), offering a universal framework for parameter tuning in feature-based planetary navigation algorithms. With the growing demand for precise absolute pose estimation of landers in lunar exploration missions, crater database-based navigation technology has become a core path to achieving this goal, but it faces challenges of low efficiency in large-scale data retrieval and insufficient matching robustness. To address these issues, a coarse-to-fine crater matching framework with database fast searching and robust triangle similarity matching is proposed. A Geo-KD search algorithm is designed to realize fast and accurate retrieval of craters within the field of view by combining Geohash and KD-tree. A robust triangle similarity matching algorithm is constructed through local neighborhood crater screening, triangle similarity matching, and mismatching elimination based on Random Sample Consensus (RANSAC) and Local Motion Consistency (LMC). Experiments show that the algorithm achieves an average retrieval time of 20 ms with an F1-score of 0.8 for the global lunar database with 1.29 million craters. It has an F1-score more than 0.746 and a single-frame matching time less than 1.005 s under lunar orbital phase, landing phase, and different camera pitch angles, outperforming other advanced algorithms and meeting on-orbit real-time requirements, providing reliable support for the absolute pose estimation of lunar probes. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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