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.
@article{arxiv.2011.02658,
title = {Compositional Scalable Object SLAM},
author = {Akash Sharma and Wei Dong and Michael Kaess},
journal= {arXiv preprint arXiv:2011.02658},
year = {2020}
}
Comments
Submitted to the 2021 IEEE International Conference on Robotics and Automation (ICRA) 7 pages, 7 figures