ShadowNav: Autonomous Global Localization for Lunar Navigation in Darkness

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.

Figures (13)

• Figure 1: The ShadowNav concept relies on using crater rims as landmarks. As the rover begins driving (a), its uncertainty in the global frame increases and must be corrected. By equipping the Lunar rover with a stereo camera and an external illumination source, the leading edges of detected craters (shown in green) are used to associate the crater with known ones from an offline map and decrease the pose uncertainty. As the rover continues to drive, (b) this process repeats intermittently to be able to continue mission operations.
• Figure 2: The four key components of the ShadowNav algorithmic pipeline are illustrated here. The system entails enhancing the image and then performing stereo and then a crater rim detection. This crater rim detection is then used as an input sample to a particle filter to perform absolute localization.
• Figure 3: Image of the data collection rig capturing an image of a crater at Cinder Lakes Apollo Training Area.
• Figure 4: A 1000m+ simulated trajectory overlaid onto a simulated orbital map of the Lunar South Pole Region. The map contains ground truth landmark craters marked with white circles.
• Figure 5: Three trajectories collected in the Cinder Lakes Apollo training area. (a) and (b) were collected in the better preserved South site and (c) was collected in the North site which is part of an ohv area.