  For intelligent robots to interact in meaningful ways with their environment,
they must understand both the geometric and semantic properties of the scene
surrounding them. The majority of research to date has addressed these mapping
challenges separately, focusing on either geometric or semantic mapping. In
this paper we address the problem of building environmental maps that include
both semantically meaningful, object-level entities and point- or mesh-based
geometrical representations. We simultaneously build geometric point cloud
models of previously unseen instances of known object classes and create a map
that contains these object models as central entities. Our system leverages
sparse, feature-based RGB-D SLAM, image-based deep-learning object detection
and 3D unsupervised segmentation.
