Neural implicit representations have recently been demonstrated in many
fields including Simultaneous Localization And Mapping (SLAM). Current neural
SLAM can achieve ideal results in reconstructing bounded scenes, but this
relies on the input of RGB-D images. Neural-based SLAM based only on RGB images
is unable to reconstruct the scale of the scene accurately, and it also suffers
from scale drift due to errors accumulated during tracking. To overcome these
limitations, we present MoD-SLAM, a monocular dense mapping method that allows
global pose optimization and 3D reconstruction in real-time in unbounded
scenes. Optimizing scene reconstruction by monocular depth estimation and using
loop closure detection to update camera pose enable detailed and precise
reconstruction on large scenes. Compared to previous work, our approach is more
robust, scalable and versatile. Our experiments demonstrate that MoD-SLAM has
more excellent mapping performance than prior neural SLAM methods, especially
in large borderless scenes.

MoD-SLAM 是一种基于神经网络的单目稠密建图方法，通过使用单目深度估计来优化场景重建，并利用闭环检测来更新相机姿态，实现实时在无边界场景中进行全局姿态优化和三维重建。与以往神经网络建图方法相比，我们的方法更加稳健、可扩展和多功能，在大型无边界场景中表现出更卓越的建图性能。

MoD-SLAM：无边界三维场景重建的单目稠密地图生成

MoD-SLAM: Monocular Dense Mapping for Unbounded 3D Scene Reconstruction

Camera geo-localization from a monocular video is a fundamental task for
video analysis and autonomous navigation. Although 3D reconstruction is a key
technique to obtain camera poses, monocular 3D reconstruction in a large
environment tends to result in the accumulation of errors in rotation,
translation, and especially in scale: a problem known as scale drift. To
overcome these errors, we propose a novel framework that integrates incremental
structure from motion (SfM) and a scale drift correction method utilizing
geo-tagged images, such as those provided by Google Street View. Our correction
method begins by obtaining sparse 6-DoF correspondences between the
reconstructed 3D map coordinate system and the world coordinate system, by
using geo-tagged images. Then, it corrects scale drift by applying pose graph
optimization over Sim(3) constraints and bundle adjustment. Experimental
evaluations on large-scale datasets show that the proposed framework not only
sufficiently corrects scale drift, but also achieves accurate geo-localization
in a kilometer-scale environment.

该研究论文使用增量 SfM 和基于地理标记图像的尺度漂移校正方法来解决单眼视频中的尺度漂移问题，并在公共数据集上进行实验验证，成果表现出了在千米尺度环境下实现了准确的地理定位