3D Gaussian Splatting has emerged as a powerful representation of geometry
and appearance for RGB-only dense Simultaneous Localization and Mapping (SLAM),
as it provides a compact dense map representation while enabling efficient and
high-quality map rendering. However, existing methods show significantly worse
reconstruction quality than competing methods using other 3D representations,
e.g. neural points clouds, since they either do not employ global map and pose
optimization or make use of monocular depth. In response, we propose the first
RGB-only SLAM system with a dense 3D Gaussian map representation that utilizes
all benefits of globally optimized tracking by adapting dynamically to keyframe
pose and depth updates by actively deforming the 3D Gaussian map. Moreover, we
find that refining the depth updates in inaccurate areas with a monocular depth
estimator further improves the accuracy of the 3D reconstruction. Our
experiments on the Replica, TUM-RGBD, and ScanNet datasets indicate the
effectiveness of globally optimized 3D Gaussians, as the approach achieves
superior or on par performance with existing RGB-only SLAM methods methods in
tracking, mapping and rendering accuracy while yielding small map sizes and
fast runtimes. The source code is available at
this https URL

使用全局优化的 3D 高斯地图表示，结合单目深度估计、动态自适应关键帧姿态和深度更新，我们提出了首个只使用 RGB 的 SLAM 系统，实现了优化的跟踪、建图和渲染精度，并获得小型地图与快速运行时。

Splat-SLAM: 全局优化的仅 RGB 的 SLAM 与 3D 高斯

Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians

Map representation learned by expert demonstrations has shown promising
research value. However, recent advancements in the visual navigation field
face challenges due to the lack of human datasets in the real world for
efficient supervised representation learning of the environments. We present a
Landmark-Aware Visual Navigation (LAVN) dataset to allow for supervised
learning of human-centric exploration policies and map building. We collect RGB
observation and human point-click pairs as a human annotator explores virtual
and real-world environments with the goal of full coverage exploration of the
space. The human annotators also provide distinct landmark examples along each
trajectory, which we intuit will simplify the task of map or graph building and
localization. These human point-clicks serve as direct supervision for waypoint
prediction when learning to explore in environments. Our dataset covers a wide
spectrum of scenes, including rooms in indoor environments, as well as walkways
outdoors. Dataset is available at DOI: 10.5281/zenodo.10608067.

通过专家演示学习的地图表示已经显示出很大的研究价值。然而，由于缺乏真实世界中的人类数据集以有效监督环境学习的可视化导航领域近期发展面临挑战。我们提出了一个地标感知的视觉导航（LAVN）数据集，以便进行人类中心探索策略和地图构建的监督学习。我们收集了 RGB 观测和人类点击配对，人类标注者在虚拟和真实环境中进行探索，并以全覆盖探索空间为目标。人类标注者还提供了每个轨迹上的不同地标示例，我们直观地认为这将简化地图或图的构建和定位任务。当学习在环境中进行探索时，这些人类点击作为路径点的直接监督。我们的数据集涵盖了广泛的场景，包括室内环境中的房间和室外的走道。数据集可在 DOI：10.5281/zenodo.10608067 上获得。