  Robots can be used to collect environmental data in regions that are
difficult for humans to traverse. However, limitations remain in the size of
region that a robot can directly observe per unit time. We introduce a method
for selecting a limited number of observation points in a large region, from
which we can predict the state of unobserved points in the region. We combine a
low rank model of a target attribute with an information-maximizing path
planner to predict the state of the attribute throughout a region. Our approach
is agnostic to the choice of target attribute and robot monitoring platform. We
evaluate our method in simulation on two real-world environment datasets, each
containing observations from one to two million possible sampling locations. We
compare against a random sampler and four variations of a baseline sampler from
the ecology literature. Our method outperforms the baselines in terms of
average Fisher information gain per samples taken and performs comparably for
average reconstruction error in most trials.
