  We develop an algorithm to explore an environment to generate a measurement
model for use in future localization tasks. Ergodic exploration with respect to
the likelihood of a particular class of measurement (e.g., a contact detection
measurement in tactile sensing) enables construction of the measurement model.
Exploration with respect to the information density based on the data-driven
measurement model enables localization. We test the two-stage approach in
simulations of tactile sensing, illustrating that the algorithm is capable of
identifying and localizing objects based on sparsely distributed binary
contacts. Comparisons with our method show that visiting low probability
regions lead to acquisition of new information rather than increasing the
likelihood of known information. Experiments with the Sphero SPRK robot
validate the efficacy of this method for collision-based estimation and
localization of the environment.
