# DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras

**Authors:** Zachary Teed, Jia Deng  
**arXiv:** [2108.10869](https://arxiv.org/abs/2108.10869)  
**Date:** 2021-08-24  
**Summary page:** https://www.summarizepaper.com/fr/paper/2108.10869v1/

## 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.