Deep Learning-Based Lunar Crater Terrain Relative Navigation
Accurate position estimation is crucial for the successful implementation of future lunar landings using autonomous vehicles, especially in dangerous environments with sparse terrain features. In this paper, we propose a terrain relative navigation (TRN) algorithm combining our deep-learning crater detector, which was designed specifically for the NASA Crater Detection Challenge problem, and an Extended Kalman Filter (EKF). Our detector analyzes crater features from the monocular images acquired from orbit, and their matches with craters from a global database are identified via a Hungarian assignment approach followed by the consensus-based outliers removal method. The estimated measurem...
| Authors | Batu Candan, Simone Servadio |
|---|---|
| Primary category | cs.RO |
| Published | 10 June 2026 |
| Last updated at source | 18 June 2026 |
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