Abstract
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.
Abstract (translated)
自主确定机器人在惯性框架中的姿态是一个关键的能力，对于下一代的太空机器人任务在其他国家行星上至关重要。目前，大多数正在进行中的机器人任务使用地面反馈来手动校正姿态估计中的漂移，这种人监控瓶颈会限制机器人能够在自主和进行科学测量时可以操作的范围。在本文中，我们提出了ShadowNav，一种专注于在月球上进行全局定位的方法，重点关注在夜间和夜间行驶。我们的方法使用月球坑的领先边缘作为地标，并采用粒子滤波方法将检测到的坑与已知位置的坑在离岸地图上相关联。我们讨论了开发ShadowNav框架与配备立体相机和外部照明系统的月球机器人概念相关的设计决策。最后，我们在月球仿真环境和亚利桑那州Cinder Lakes的现场测试中展示了我们提出方法的效力。
URL
https://arxiv.org/abs/2405.01673
