  Probabilistic 3D map has been applied to object segmentation with multiple
camera viewpoints, however, conventional methods lack of real-time efficiency
and functionality of multilabel object mapping. In this paper, we propose a
method to generate three-dimensional map with multilabel occupancy in
real-time. Extending our previous work in which only target label occupancy is
mapped, we achieve multilabel object segmentation in a single looking around
action. We evaluate our method by testing segmentation accuracy with 39
different objects, and applying it to a manipulation task of multiple objects
in the experiments. Our mapping-based method outperforms the conventional
projection-based method by 40 - 96\% relative (12.6 mean $IU_{3d}$), and robot
successfully recognizes (86.9\%) and manipulates multiple objects (60.7\%) in
an environment with heavy occlusions.
