Compositional and Scalable Object SLAM
Sharma, A.,
Dong, W.,
and Kaess, M.
In Proc. IEEE Intl. Conf. on Robotics and Automation, ICRA
May
2021
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 and 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 results in 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://github.com/rpl-cmu/object-slam.
