We present an uncertainty learning framework for dense neural simultaneous
localization and mapping (SLAM). Estimating pixel-wise uncertainties for the
depth input of dense SLAM methods allows to re-weigh the tracking and mapping
losses towards image regions that contain more suitable information that is
more reliable for SLAM. To this end, we propose an online framework for sensor
uncertainty estimation that can be trained in a self-supervised manner from
only 2D input data. We further discuss the advantages of the uncertainty
learning for the case of multi-sensor input. Extensive analysis,
experimentation, and ablations show that our proposed modeling paradigm
improves both mapping and tracking accuracy and often performs better than
alternatives that require ground truth depth or 3D. Our experiments show that
we achieve a 38% and 27% lower absolute trajectory tracking error (ATE) on the
7-Scenes and TUM-RGBD datasets respectively. On the popular Replica dataset on
two types of depth sensors we report an 11% F1-score improvement on RGBD SLAM
compared to the recent state-of-the-art neural implicit approaches. Our source
code will be made available.

本文提出了一种基于不确定性学习的密集神经同步定位与地图构建方法，并通过传感器不确定性估计在线框架对其进行了优化。相较于现有的神经模型，该方法在多传感器输入情况下可实现更优秀的地图构建和定位精度。

UncLe-SLAM：面向稠密神经 SLAM 的不确定性学习

UncLe-SLAM: Uncertainty Learning for Dense Neural SLAM

We propose a dense neural simultaneous localization and mapping (SLAM)
approach for monocular RGBD input which anchors the features of a neural scene
representation in a point cloud that is iteratively generated in an
input-dependent data-driven manner. We demonstrate that both tracking and
mapping can be performed with the same point-based neural scene representation
by minimizing an RGBD-based re-rendering loss. In contrast to recent dense
neural SLAM methods which anchor the scene features in a sparse grid, our
point-based approach allows dynamically adapting the anchor point density to
the information density of the input. This strategy reduces runtime and memory
usage in regions with fewer details and dedicates higher point density to
resolve fine details. Our approach performs either better or competitive to
existing dense neural RGBD SLAM methods in tracking, mapping and rendering
accuracy on the Replica, TUM-RGBD and ScanNet datasets. The source code is
available at this https URL

本文提出了一种基于密集神经算法的同步定位与建图方法，使用深度学习生成的点云来锚定神经场景的特征，通过最小化基于 RGBD 的重新渲染损失，实现对跟踪和地图绘制的同时处理，通过在信息密度低的区域减少运行时间和内存使用，并将更高的点密度分配给解决更细节问题。在 Replica，TUM-RGBD 和 ScanNet 数据集上相对于现有的神经 RGBD SLAM 方法获得了更好或相似的跟踪，地图和渲染准确性。