  For robotic interaction in environments shared with other agents, access to
volumetric and semantic maps of the scene is crucial. However, such
environments are inevitably subject to long-term changes, which the map needs
to account for. We thus propose panoptic multi-TSDFs as a novel representation
for multi-resolution volumetric mapping in changing environments. By leveraging
high-level information for 3D reconstruction, our proposed system allocates
high resolution only where needed. Through reasoning on the object level,
semantic consistency over time is achieved. This enables our method to maintain
up-to-date reconstructions with high accuracy while improving coverage by
incorporating previous data. We show in thorough experimental evaluation that
our map can be efficiently constructed, maintained, and queried during online
operation, and that the presented approach can operate robustly on real depth
sensors using non-optimized panoptic segmentation as input.
