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.

Zhang, Y., Tosi, F., Mattoccia, S., Poggi, M. (2023). GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction [10.1109/ICCV51070.2023.00345].

GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction

Zhang, Youmin;Tosi, Fabio;Mattoccia, Stefano;Poggi, Matteo
2023

Abstract

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.
2023
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
3704
3714
Zhang, Y., Tosi, F., Mattoccia, S., Poggi, M. (2023). GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction [10.1109/ICCV51070.2023.00345].
Zhang, Youmin; Tosi, Fabio; Mattoccia, Stefano; Poggi, Matteo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/957759
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