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

Zachary Teed · Jia Deng

Keywords: [ Deep Learning ]

[ Abstract ]
[ OpenReview
Fri 10 Dec 8:30 a.m. PST — 10 a.m. PST
Oral presentation: Oral Session 5: Vision Applications
Fri 10 Dec 4 p.m. PST — 5 p.m. PST


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

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