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.

GO-SLAM 是一种基于深度学习的稠密视觉 SLAM 框架，通过全局优化姿态和三维重建实时改进相机跟踪和重构方面的错误累积问题，并通过有效的闭环检测和在线全捆绑调整支持鲁棒的姿态估计和实时三维重建，同时在运行中实时更新隐式和连续的表面表示以确保全局一致性的三维重建，各种合成和真实世界数据集的结果表明，GO-SLAM 在跟踪鲁棒性和重建精度方面优于现有方法，并且具有多样性，可配合单目、立体和 RGB-D 输入工作。

GO-SLAM：一致性 3D 即时重建的全局优化

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

We present ESLAM, an efficient implicit neural representation method for
Simultaneous Localization and Mapping (SLAM). ESLAM reads RGB-D frames with
unknown camera poses in a sequential manner and incrementally reconstructs the
scene representation while estimating the current camera position in the scene.
We incorporate the latest advances in Neural Radiance Fields (NeRF) into a SLAM
system, resulting in an efficient and accurate dense visual SLAM method. Our
scene representation consists of multi-scale axis-aligned perpendicular feature
planes and shallow decoders that, for each point in the continuous space,
decode the interpolated features into Truncated Signed Distance Field (TSDF)
and RGB values. Our extensive experiments on three standard datasets, Replica,
ScanNet, and TUM RGB-D show that ESLAM improves the accuracy of 3D
reconstruction and camera localization of state-of-the-art dense visual SLAM
methods by more than 50%, while it runs up to 10 times faster and does not
require any pre-training.

ESLAM 是一种用于同时定位和建图的高效隐式神经表示方法，它基于多尺度轴对齐法线特征面和浅解码器，使用神经辐射场技术，将连续空间中的特征解码成距离场和 RGB 值，并成功地在三个标准数据集上实现了超过 50% 的 3D 重建和相机定位精度提升，速度提升了 10 倍。