  This work develops a distributed optimization algorithm for multi-robot 3-D
semantic mapping using streaming range and visual observations and single-hop
communication. Our approach relies on gradient-based optimization of the
observation log-likelihood of each robot subject to a map consensus constraint
to build a common multi-class map of the environment. This formulation leads to
closed-form updates which resemble Bayes rule with one-hop prior averaging. To
reduce the amount of information exchanged among the robots, we utilize an
octree data structure that compresses the multi-class map distribution using
adaptive-resolution.
