The ability to determine the pose of a rover in an inertial frame autonomously is a crucial capability necessary for the next generation of surface rover missions on other planetary bodies. Currently, most on-going rover missions utilize ground-in-the-loop interventions to manually correct for drift in the pose estimate and this human supervision bottlenecks the distance over which rovers can operate autonomously and carry out scientific measurements. In this paper, we present ShadowNav, an autonomous approach for global localization on the Moon with an emphasis on driving in darkness and at nighttime. Our approach uses the leading edge of Lunar craters as landmarks and a particle filtering approach is used to associate detected craters with known ones on an offboard map. We discuss the key design decisions in developing the ShadowNav framework for use with a Lunar rover concept equipped with a stereo camera and an external illumination source. Finally, we demonstrate the efficacy of our proposed approach in both a Lunar simulation environment and on data collected during a field test at Cinder Lakes, Arizona.

翻译：惯性坐标系中无人车姿态的自主确定能力是下一代行星表面巡视任务所需的关键核心技术。当前多数在编巡视任务依赖地面回路的人工干预来修正姿态估计累积误差，这种人工监督机制已成为限制无人车自主行驶距离及科学测量效率的瓶颈。本文提出ShadowNav——一种面向月球暗区及夜间行驶场景的自主全局定位方法。该方法以月球撞击坑边缘作为路标，通过粒子滤波算法将检测到的撞击坑与离线地图中的已知撞击坑进行关联。我们阐述了ShadowNav框架的核心设计决策，使其适用于配备立体相机与外部照明源的月球车概念原型。最后，我们通过月球仿真环境以及在亚利桑那州辛德湖野外测试数据验证了所提方法的有效性。