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。