  Autonomous robots that interact with their environment require a detailed
semantic scene model. For this, volumetric semantic maps are frequently used.
The scene understanding can further be improved by including object-level
information in the map. In this work, we extend a multi-view 3D semantic
mapping system consisting of a network of distributed smart edge sensors with
object-level information, to enable downstream tasks that need object-level
input. Objects are represented in the map via their 3D mesh model or as an
object-centric volumetric sub-map that can model arbitrary object geometry when
no detailed 3D model is available. We propose a keypoint-based approach to
estimate object poses via PnP and refinement via ICP alignment of the 3D object
model with the observed point cloud segments. Object instances are tracked to
integrate observations over time and to be robust against temporary occlusions.
Our method is evaluated on the public Behave dataset where it shows pose
estimation accuracy within a few centimeters and in real-world experiments with
the sensor network in a challenging lab environment where multiple chairs and a
table are tracked through the scene online, in real time even under high
occlusions.
