DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras
This addresses the problem of accurate and reliable 3D mapping and localization for robotics and AR/VR applications, representing a strong specific gain rather than an incremental improvement.
The authors tackled the problem of visual SLAM for monocular, stereo, and RGB-D cameras by introducing DROID-SLAM, a deep learning system that achieves large improvements in accuracy and robustness with substantially fewer catastrophic failures compared to prior work.
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.