  Many robot applications call for autonomous exploration and mapping of
unknown and unstructured environments. Information-based exploration
techniques, such as Cauchy-Schwarz quadratic mutual information (CSQMI) and
fast Shannon mutual information (FSMI), have successfully achieved active
binary occupancy mapping with range measurements. However, as we envision
robots performing complex tasks specified with semantically meaningful objects,
it is necessary to capture semantic categories in the measurements, map
representation, and exploration objective. This work develops a Bayesian
multi-class mapping algorithm utilizing range-category measurements. We derive
a closed-form efficiently computable lower bound for the Shannon mutual
information between the multi-class map and the measurements. The bound allows
rapid evaluation of many potential robot trajectories for autonomous
exploration and mapping. We compare our method against frontier-based and FSMI
exploration and apply it in a 3-D photo-realistic simulation environment.
