Current Mars surface exploration is primarily pre-scripted on a day-by-day basis. Mars rovers have a limited ability to autonomously select targets for follow-up study that match pre-defined target signatures. However, when exploring new environments, we are also interested in observations that differ from what previously has been seen. In this work, we develop and evaluate methods for a Mars rover to use novelty to guide the selection of observation targets with the goal of accelerating discovery. In a study comparing three image content representations and five novelty-based ranking methods, we found that the Isolation Forest identified the largest number of novel targets using a combination of intensity and shape features to represent the candidate targets. It was followed closely by the Local RX algorithm using raw pixel features. All algorithms achieved performance well above alternatives such as random selection or selecting the best match to current science objectives, which do not account for novelty.
Recommended citation: Wagstaff, K. L., Francis, R. F., Kerner, H. R., Lu, S., Nerrise, F., Bell III, J. F., Doran, G., and Rebbapragada, U. (2020). "Novelty-driven onboard targeting for Mars rovers." International Symposium on Artificial Intelligence, Robotics and Automation in Space (I-SAIRAS).