NeurIPS 2021oral666 citations
DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras
Abstract
We introduce DROID-SLAM, a new deep learning based SLAM system. DROID-SLAM consists of recurrent iterative updates of camera pose and pixelwise depth through a Dense Bundle Adjustment layer. DROID-SLAM is accurate, achieving large improvements over prior work, and robust, suffering from substantially fewer catastrophic failures. Despite training on monocular video, it can leverage stereo or RGB-D video to achieve improved performance at test time. The URL to our open source code is https://github.com/princeton-vl/DROID-SLAM.
SLAMSimultaneous Localization and Mapping3D
BibTeX
@inproceedings{
teed2021droidslam,
title={{DROID}-{SLAM}: Deep Visual {SLAM} for Monocular, Stereo, and {RGB}-D Cameras},
author={Zachary Teed and Jia Deng},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=ZBfUo_dr4H}
}