  We present a fast, scalable, and accurate Simultaneous Localization and
Mapping (SLAM) system that represents indoor scenes as a graph of objects.
Leveraging the observation that artificial environments are structured and
occupied by recognizable objects, we show that a compositional scalable object
mapping formulation is amenable to a robust SLAM solution for drift-free large
scale indoor reconstruction. To achieve this, we propose a novel semantically
assisted data association strategy that obtains unambiguous persistent object
landmarks, and a 2.5D compositional rendering method that enables reliable
frame-to-model RGB-D tracking. Consequently, we deliver an optimized online
implementation that can run at near frame rate with a single graphics card, and
provide a comprehensive evaluation against state of the art baselines. An open
source implementation will be provided at https://placeholder.
