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

2021-08-24 17:50:10
Zachary Teed, Jia Deng

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 this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2108.10869

PDF

https://arxiv.org/pdf/2108.10869.pdf


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