  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.
