AI-assisted hazard detection for safe lunar landing

Identifying hazards that could compromise a safe landing is a critical step of the final phases of a lander's descent trajectory. Landing on a harsh terrain can lead to the lander tipping over or prevent a deployed payload from fulfilling its mission. Many attempts to develop hazard detection systems failed to provide a comprehensive and robust assessment of the hazards present in the landing area, particularly under adverse lighting conditions. The proposed algorithm utilizes deep learning techniques combined with standard processing methods to deliver a secure landing site to the onboard guidance, navigation, and control (GNC) system. By integrating data from optical images and LiDAR point clouds, the algorithm evaluates slope, extent, local roughness, presence of obstacles, transient shadows, and permanently shadowed regions, while also considering the fuel expenses required to reach the designated site. The deep learning algorithm's training and validation, as well as the hazard detection pipeline's testing, were conducted using synthetic images and point clouds generated from a virtual simulation environment created in Unreal Engine, which enabled varying lighting conditions and lander orientation and altitude. The terrain was generated by enhancing the resolution of the lunar reconnaissance orbiter (LRO) digital elevation model (DEM), with the possibility to introduce synthetic obstacles such as rocks and craters, allowing for a realistic and comprehensive assessment of the algorithm's performance in different lunar surface scenarios. The algorithm has yielded satisfactory outcomes, effectively pinpointing a secure landing location across all conducted tests, thus reaffirming the validity of the approach.