  Neural implicit representations have recently demonstrated compelling results
on dense Simultaneous Localization And Mapping (SLAM) but suffer from the
accumulation of errors in camera tracking and distortion in the reconstruction.
Purposely, we present GO-SLAM, a deep-learning-based dense visual SLAM
framework globally optimizing poses and 3D reconstruction in real-time. Robust
pose estimation is at its core, supported by efficient loop closing and online
full bundle adjustment, which optimize per frame by utilizing the learned
global geometry of the complete history of input frames. Simultaneously, we
update the implicit and continuous surface representation on-the-fly to ensure
global consistency of 3D reconstruction. Results on various synthetic and
real-world datasets demonstrate that GO-SLAM outperforms state-of-the-art
approaches at tracking robustness and reconstruction accuracy. Furthermore,
GO-SLAM is versatile and can run with monocular, stereo, and RGB-D input.
