论文标题
使用神经网络对月球着陆点的检测和初步评估
Detection and Initial Assessment of Lunar Landing Sites Using Neural Networks
论文作者
论文摘要
机器人和人类月球着陆是未来NASA任务的重点。精确着陆功能对于确保任务的成功以及着陆器和机组人员的安全至关重要。在进入表面的方法中，存在与危险相对导航相关的多个挑战，以确保安全着陆。本文将着重于被动自主危害检测和避免子系统，以对指导系统的可能着陆区进行初步评估。该系统使用单个摄像头和Mobilenetv2神经网络架构来检测和辨别安全的着陆点和危险，例如岩石，阴影和陨石坑。然后，来自运动的单眼结构将重新创建表面以提供斜率和粗糙度分析。
Robotic and human lunar landings are a focus of future NASA missions. Precision landing capabilities are vital to guarantee the success of the mission, and the safety of the lander and crew. During the approach to the surface there are multiple challenges associated with Hazard Relative Navigation to ensure safe landings. This paper will focus on a passive autonomous hazard detection and avoidance sub-system to generate an initial assessment of possible landing regions for the guidance system. The system uses a single camera and the MobileNetV2 neural network architecture to detect and discern between safe landing sites and hazards such as rocks, shadows, and craters. Then a monocular structure from motion will recreate the surface to provide slope and roughness analysis.
