发表机构
Sungkyunkwan University（成均馆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对神经渲染SLAM中预测可靠性缺失的问题，提出BayesianGS-SLAM，通过概率公式同时估计颜色与深度不确定性，并集成到建图、跟踪和关键帧选择中，在真实数据集上提升深度不确定性排名并减少关键帧数量。
AI 中文摘要
基于神经渲染的SLAM依赖渲染的RGB-D残差进行相机跟踪和地图优化，但由于传感器噪声、有限的观测覆盖和不完整的地图表示，这些预测的可靠性可能差异很大。在没有显式可靠性估计的情况下，不可靠的残差可能对位姿优化产生不利影响，而已被当前地图充分解释的帧可能触发冗余的地图更新。在本文中，我们提出了BayesianGS-SLAM，一个不确定性感知的3D高斯泼溅SLAM框架，它在建图过程中估计预测的颜色和深度不确定性，并在整个SLAM流程中一致地重用这些不确定性。我们易于处理的概率公式将传感器噪声不确定性分量与通过渲染过程传播的不透明度引起的地图表示分量相结合。由此产生的预测不确定性被用于增强建图，通过鲁棒的位姿目标归一化跟踪残差，并使用基于预测惊奇的关键帧准则评估传入帧。与先前主要考虑颜色不确定性或仅在建图期间使用不确定性的不确定性感知神经渲染SLAM方法不同，我们的框架同时估计颜色和深度的预测不确定性，并将其集成到建图、跟踪和关键帧选择中。在真实世界RGB-D数据集上的评估表明，与现有的不确定性感知SLAM方法相比，深度不确定性误差排名显著改善。此外，所提出的关键帧选择策略减少了所选关键帧的数量和建图调用，同时保持了有竞争力的跟踪和渲染性能。
英文摘要
Neural-rendering-based SLAM relies on rendered RGB-D residuals for camera tracking and map optimization, but the reliability of these predictions can vary substantially because of sensor noise, limited observation coverage, and incomplete map representations. Without an explicit reliability estimate, unreliable residuals may adversely affect pose optimization, while frames already well explained by the current map may trigger redundant mapping updates. In this paper, we present BayesianGS-SLAM, an uncertainty-aware 3D Gaussian Splatting SLAM framework that estimates predictive color and depth uncertainty during mapping and consistently reuses it across the SLAM pipeline. Our tractable probabilistic formulation combines a sensor-noise uncertainty component with an opacity-induced map-representation component propagated through the rendering process. The resulting predictive uncertainty is used to augment mapping, normalize tracking residuals through a robust pose objective, and evaluate incoming frames using a predictive-surprise-based keyframe criterion. Unlike prior uncertainty-aware neural-rendering SLAM methods that primarily consider color uncertainty or use uncertainty only during mapping, our framework estimates predictive uncertainty for both color and depth and integrates it into mapping, tracking, and keyframe selection. Evaluations on real-world RGB-D datasets demonstrate substantially improved depth uncertainty-error ranking compared with existing uncertainty-aware SLAM methods. Moreover, the proposed keyframe-selection strategy reduces the number of selected keyframes and mapping calls while maintaining competitive tracking and rendering performance.
