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Many robot applications call for autonomous exploration and mapping ofunknown and unstructured environments. Information-based explorationtechniques, such as Cauchy-Schwarz quadratic mutual information (CSQMI) andfast Shannon mutual information (FSMI), have successfully achieved activebinary occupancy mapping with range measurements. However, as we envisionrobots performing complex tasks specified with semantically meaningful objects,it is necessary to capture semantic categories in the measurements, maprepresentation, and exploration objective. This work develops a Bayesianmulti-class mapping algorithm utilizing range-category measurements. We derivea closed-form efficiently computable lower bound for the Shannon mutualinformation between the multi-class map and the measurements. The bound allowsrapid evaluation of many potential robot trajectories for autonomousexploration and mapping. We compare our method against frontier-based and FSMIexploration and apply it in a 3-D photo-realistic simulation environment.

Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observation