  Highly accurate real-time localization is of fundamental importance for the
safety and efficiency of planetary rovers exploring the surface of Mars. Mars
rover operations rely on vision-based systems to avoid hazards as well as plan
safe routes. However, vision-based systems operate on the assumption that
sufficient visual texture is visible in the scene. This poses a challenge for
vision-based navigation on Mars where regions lacking visual texture are
prevalent. To overcome this, we make use of the ability of the rover to
actively steer the visual sensor to improve fault tolerance and maximize the
perception performance. This paper answers the question of where and when to
look by presenting a method for predicting the sensor trajectory that maximizes
the localization performance of the rover. This is accomplished by an online
assessment of possible trajectories using synthetic, future camera views
created from previous observations of the scene. The proposed trajectories are
quantified and chosen based on the expected localization performance. In this
work, we validate the proposed method in field experiments at the Jet
Propulsion Laboratory (JPL) Mars Yard. Furthermore, multiple performance
metrics are identified and evaluated for reducing the overall runtime of the
algorithm. We show how actively steering the perception system increases the
localization accuracy compared to traditional fixed-sensor configurations.
