Real-time 3D scene reconstruction from monocular, stereo, RGB-D input using all history with GO-SLAM
Real-time 3D scene reconstruction from monocular, stereo, RGB-D input using all history with GO-SLAM
GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction
arXiv paper abstract https://arxiv.org/abs/2309.02436
arXiv PDF paper https://arxiv.org/pdf/2309.02436.pdf
Project page https://youmi-zym.github.io/projects/GO-SLAM
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.
... 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.
... update the implicit and continuous surface representation on-the-fly to ensure global consistency of 3D reconstruction.
... 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.
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