Autonomous Obstacle Detection and Landing Site Selection Method for Complex Lunar Approach Phases

In response to the challenges of autonomous obstacle detection and landing site selection during the soft-landing approach phase of the Chang'e probes, this paper proposes a method based on Fractal Brownian Motion (FBM) for local lunar terrain reconstruction and obstacle identification. Using passive optical imagery and incorporating statistical models of crater morphology and distribution, the method approximates the 3D structure of candidate landing areas and estimates local slopes. Combined with texture analysis (TA) via K-means clustering evaluated by silhouette coefficients, and by integrating slope and roughness metrics, feasible landing zones are autonomously identified. Simulation results demonstrate that the proposed approach can rapidly reconstruct terrain and select suitable landing areas, showing good mission adaptability.