Abstract
An autonomous crater detection method based on the image gray value feature is proposed in this paper. In this method, region of interest (ROI) is used to identify crater's edge distribution from real planet landing navigation images conveniently, thus avoiding dependence of image on solar elevation. Then an algorithm for determining both vehicle attitude and position by use of the nonlinear predictive filter is also described. Considering that landing terrain plays a significant role in celestial body landing navigation, a method of terrain map simulation is proposed. By sorting surface points into levels, new points completely depend on their higher-level neighbors according to the fractal theory. A celestial body random terrain map modeling method is proposed. Finally the paper presents the validation of the algorithms in the end.
| Original language | English |
|---|---|
| Pages (from-to) | 908-915 |
| Number of pages | 8 |
| Journal | Yuhang Xuebao/Journal of Astronautics |
| Volume | 35 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 30 Aug 2014 |
Keywords
- Automated crater detection
- Autonomous optical navigation
- Gray value features
- Precise landing
- Terrain map modeling