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We develop an online probabilistic metric-semantic mapping approach formobile robot teams relying on streaming RGB-D observations. The generated mapscontain full continuous distributional information about the geometric surfacesand semantic labels (e.g., chair, table, wall). Our approach is based on onlineGaussian Process (GP) training and inference, and avoids the complexity of GPclassification by regressing a truncated signed distance function (TSDF) of theregions occupied by different semantic classes. Online regression is enabledthrough a sparse pseudo-point approximation of the GP posterior. To scale tolarge environments, we further consider spatial domain partitioning via anoctree data structure with overlapping leaves. An extension to the multi-robotsetting is developed by having each robot execute its own online measurementupdate and then combine its posterior parameters via local weighted geometricaveraging with those of its neighbors. This yields a distributed informationprocessing architecture in which the GP map estimates of all robots converge toa common map of the environment while relying only on local one-hopcommunication. Our experiments demonstrate the effectiveness of theprobabilistic metric-semantic mapping technique in 2-D and 3-D environments inboth single and multi-robot settings.

Dense Incremental Metric-Semantic Mapping for Multi-Agent Systems via Sparse Gaussian Process Regression