  We present a method for image-guided exploration for mobile robotic systems.
Our approach extends ergodic exploration methods, a recent exploration approach
that prioritizes complete coverage of a space, with the use of a learned image
classifier that automatically detects objects and updates an information map to
guide further exploration and localization of objects. Additionally, to improve
outcomes of the information collected by our robot's visual sensor, we present
a decomposition of the ergodic optimization problem as bi-level coarse and fine
solvers, which act respectively on the robot's body and the robot's visual
sensor.
  Our approach is applied to geological survey and localization of rock
formations for Mars rovers, with real images from Mars rovers used to train the
image classifier. Results demonstrate 1) improved localization of rock
formations compared to naive approaches while 2) minimizing the path length of
the exploration through the bi-level exploration.
