  We develop an online probabilistic metric-semantic mapping approach for
mobile robot teams relying on streaming RGB-D observations. The generated maps
contain full continuous distributional information about the geometric surfaces
and semantic labels (e.g., chair, table, wall). Our approach is based on online
Gaussian Process (GP) training and inference, and avoids the complexity of GP
classification by regressing a truncated signed distance function (TSDF) of the
regions occupied by different semantic classes. Online regression is enabled
through a sparse pseudo-point approximation of the GP posterior. To scale to
large environments, we further consider spatial domain partitioning via an
octree data structure with overlapping leaves. An extension to the multi-robot
setting is developed by having each robot execute its own online measurement
update and then combine its posterior parameters via local weighted geometric
averaging with those of its neighbors. This yields a distributed information
processing architecture in which the GP map estimates of all robots converge to
a common map of the environment while relying only on local one-hop
communication. Our experiments demonstrate the effectiveness of the
probabilistic metric-semantic mapping technique in 2-D and 3-D environments in
both single and multi-robot settings.
