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

• URL: http://arxiv.org/abs/2108.10869v1
• Date: Tue, 24 Aug 2021 17:50:10 GMT
• Title: DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras
• Authors: Zachary Teed and Jia Deng
• Abstract summary: DROID-SLAM is a new deep learning based SLAM system. It can leverage stereo or RGB-D video to achieve improved performance at test time.
• Score: 71.41252518419486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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