Evaluation of Front-End Feature Matching for Lunar Visual SLAM

The study evaluates eleven feature detector–matcher combinations for lunar visual SLAM using synthetic, lunar-testbed, and terrestrial-analogue datasets. SuperPoint pairings outperform SIFT with ratio-tested FLANN in a rotation-only metric on lunar testbeds, but SIFT excels when rotation and translation are jointly scored on synthetic data. In stereo VO, SuperPoint maintains tracking across all datasets, while classical detectors are more sensitive to lighting conditions, and the fastest learned combination is SuperPoint with LightGlue.

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