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：摘要：自主确定漫游车在惯性坐标系中位姿的能力，是下一代行星表面任务中漫游车所需的关键能力。目前，大多数正在进行的漫游车任务依赖地面闭环干预人工修正位姿估计漂移，这种人类监督限制了漫游车可自主运行及开展科学测量的距离。本文提出ShadowNav——一种面向月球全局定位的自主方法，重点解决暗区和夜间行驶问题。该方法以月球环形山前沿边缘为地标，采用粒子滤波方法将检测到的环形山与星载地图中的已知环形山进行关联。我们讨论了开发ShadowNav框架的关键设计决策，该框架适用于配备立体相机与外部照明源的月球漫游车概念。最后，在月球仿真环境及亚利桑那煤渣湖野外试验数据上验证了所提方法的有效性。