Gravimetric localization on the surface of small bodies

The localization of landers on the surface of small bodies has traditionally relied on observations from a mothership (e.g. Rosetta's Philae lander and Hayabusa 2's MASCOT and MINERVA landers). However, when line-of-sight with the mothership is not always available, or for surface rovers that travel large distances, alternative mothership-independent localization techniques may be required. On-board vision-based techniques have demonstrated effective localization in terrestrial applications as well as for Mars rovers, but may be unreliable on small bodies where rovers must contend with fast-moving shadows and issues acquiring images such as dusty lenses, sun blinding, and a rotating field of view. We investigate the feasibility of an entirely new localization approach based on surface gravimetry, where a rover can constrain its location on the surface by precisely measuring the local gravity vector. This mothership-independent localization technique is well-suited to hopping rovers that can bounce and tumble over the surface of small bodies; it is insensitive to surface illumination, and even works at night. We develop a Bayesian framework for computing localization “likelihood maps” from gravimetry data, accounting for all sensor and model uncertainties. We then propose a method for deriving landing distributions of a bouncing rover from simulation data to serve as a prior for the localization estimate. Finally, we conduct a case study on the Philae lander, where we show how this approach could have helped reject localization hypotheses and significantly narrow the search area for the lost Philae lander. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.