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

Recent years have witnessed an emerging paradigm shift toward embodied
artificial intelligence, in which an agent must learn to solve challenging
tasks by interacting with its environment. There are several challenges in
solving embodied multimodal tasks, including long-horizon planning,
vision-and-language grounding, and efficient exploration. We focus on a
critical bottleneck, namely the performance of planning and navigation. To
tackle this challenge, we propose a Neural SLAM approach that, for the first
time, utilizes several modalities for exploration, predicts an affordance-aware
semantic map, and plans over it at the same time. This significantly improves
exploration efficiency, leads to robust long-horizon planning, and enables
effective vision-and-language grounding. With the proposed Affordance-aware
Multimodal Neural SLAM (AMSLAM) approach, we obtain more than 40% improvement
over prior published work on the ALFRED benchmark and set a new
state-of-the-art generalization performance at a success rate of 23.48% on the
test unseen scenes.

提出一种神经 SLAM 方法，利用多种模态进行探索，预测可承受意义地图并在其上进行规划，从而显著提高了探索效率，实现了鲁棒的长程规划，使得机器智能能够更有效地识别视觉和语言信息。在 ALFRED 基准测试中，相对先前发表的作品，提出的 Affordance-aware Multimodal Neural SLAM（AMSLAM）方法在成功率上实现了 23.48％的新的最高水平，取得了超过 40％的改进。