Abstract: Simultaneous localization and mapping (SLAM) in dynamic scenes is highly susceptible to moving objects, occlusions, and illumination variations, which often lead to pose drift and reconstruction artifacts. Existing methods remain limited in identifying unreliable dynamic observations and constraining map updates. They frequently depend on category priors, fixed thresholds, or scene-specific distributions, and may incorporate moving objects into the map as spurious static structures. To address these issues, a Multi-Source Consistency Residual-Enhanced Gaussian SLAM method, termed MCRGS-SLAM, is proposed for dynamic scenes. It models dynamic interference from the perspective of observation reliability in a continuous and interpretable manner, and embeds reliability constraints into both front-end pose estimation and back-end Gaussian mapping to improve the localization accuracy and static map reconstruction quality of monocular SLAM. The method is built on the physical constraint that static regions should satisfy multi-view consistency. It constructs four complementary consistency residuals, namely appearance, geometric, motion, and structural residuals, thereby transforming dynamic observation identification into a quantifiable physical measurement. Specifically, the appearance residual characterizes cross-view brightness and texture consistency, the geometric residual measures the stability of depth projection relationships, the motion residual detects independent displacement that cannot be explained by camera ego-motion, and the structural residual describes variations in local edges and geometric patterns. Unlike strategies that directly remove dynamic regions using binary masks or fixed thresholds, these residuals are modeled as continuous physical constraints to represent the degree to which pixel observations deviate from the static-scene assumption. On this basis, a semantic–geometric dual-stream reliability inference network, named Multi-source Consistency Residual Network (MCR-Net), is designed. The semantic stream extracts high-level semantic features to provide category-level dynamic priors, while the geometric stream encodes residual evidence to represent multi-view consistency. The two streams are fused through an attention mechanism to generate a pixel-wise reliability map. This map is introduced into both the front-end and back-end of the SLAM system as soft weights. In front-end pose optimization, appearance, motion, geometric, and structural constraints are weighted by reliability to reduce the interference of dynamic outliers in camera pose estimation. In back-end 3D Gaussian Splatting (3DGS) mapping, the reliability map guides the initialization, update, and removal of Gaussian primitives, thereby adaptively suppressing map contamination caused by dynamic observations. In this way, dynamic observation handling is transformed from discrete removal into reliability-based continuous weighting, which preserves boundary regions and weakly reliable static observations while reducing the cumulative influence of dynamic outliers on pose estimation and Gaussian map updates. In addition, MCRGS-SLAM establishes a self-supervised closed-loop optimization mechanism based on reprojection errors and rendering consistency, enabling the network to adapt online to dynamic variations in unknown scenes. Experiments on dynamic-scene datasets, including Bonn and TUM, show that MCRGS-SLAM achieves competitive performance in both localization accuracy and reconstruction quality. In localization evaluation, MCRGS-SLAM obtains an average ATE RMSE of 2.35 cm on the Bonn Dynamic dataset, outperforming several representative methods. These results indicate that reliability-weighted optimization effectively reduces the impact of dynamic observations on pose estimation. In the reconstruction and rendering quality evaluation, the proposed method achieves average PSNR, SSIM, and LPIPS values of 17.99 dB, 0.730, and 0.272 on the TUM dynamic dataset, respectively. Compared with Dy3DGS-SLAM, a representative dynamic 3DGS-SLAM method, it improves PSNR by 0.14 dB and SSIM by approximately 1.1%, while reducing LPIPS by approximately 4.6%, effectively mitigating reconstruction artifacts caused by dynamic objects. Results on real-world complex scene sequences further demonstrate that MCRGS-SLAM maintains stable performance under scene distribution shifts and unstructured dynamic interference, indicating good cross-scene applicability.

摘要： 动态场景下的同时定位与建图（Simultaneous Localization and Mapping，SLAM）易受运动目标、遮挡及光照变化等因素影响，导致位姿估计漂移与地图重建伪影。现有方法在动态观测判别和地图更新约束方面仍存在局限，容易依赖类别先验、固定阈值或特定场景分布，并可能将运动目标误建为伪静态结构。针对上述问题，提出一种面向动态场景的多源一致性残差增强高斯SLAM方法（Multi-Source Consistency Residual-Enhanced Gaussian SLAM，MCRGS-SLAM）。该方法从观测可靠性角度对动态干扰进行连续、可解释建模，并将可靠性约束嵌入前端位姿估计与后端高斯建图过程，以提升动态场景下单目SLAM系统的定位精度和静态地图重建质量。 该方法基于静态区域需满足多视图一致性的物理约束，构建外观、几何、运动和结构四类互补的一致性残差，将动态观测判别转化为可量化的物理度量。其中，外观残差用于刻画跨视角亮度与纹理一致性，几何残差用于衡量深度投影关系的稳定性，运动残差用于检测难以由相机自运动解释的独立位移，结构残差用于描述局部边缘与几何形态变化。不同于直接采用二值掩膜或固定阈值剔除动态区域的策略，上述残差将被建模为连续物理约束，用于表征像素观测相对静态假设的偏离程度。在此基础上，设计语义—几何双流融合的可靠性推理网络（Multi-source Consistency Residual Network，MCR-Net）。语义流提取高层语义特征，用以提供类别级动态先验，几何流编码残差证据，用以表征多视图一致性，两路信息经注意力机制融合后生成像素级可靠性图谱。该图谱以软权重形式作用于 SLAM 系统前后端：在前端位姿优化中，对外观、运动、几何和结构约束进行可靠性加权，降低动态外点对相机位姿求解的干扰；在后端3D高斯（3D Gaussian Splatting，3DGS）建图中，指导高斯基元的初始化、更新与剔除，从而自适应抑制动态观测造成的地图污染。由此，系统将动态观测处理从离散剔除转化为基于可靠性的连续加权，在保留边界区域和弱可靠静态观测的同时，减弱动态外点对位姿估计与高斯地图更新的累积影响。此外，MCRGS-SLAM 构建基于重投影误差与渲染一致性的自监督闭环优化机制，使网络能够在线适应未知场景中的动态变化。 在Bonn和TUM等动态场景数据集上的实验结果表明，MCRGS-SLAM在定位精度和重建渲染质量方面均取得了良好的综合表现。在定位精度评价中，该方法在Bonn动态数据集上的平均绝对轨迹误差均方根（ATE RMSE）为2.35 cm，优于多类代表性方法，说明可靠性加权优化能够有效减弱动态观测对位姿估计的影响。在重建渲染质量评价中，该方法在TUM动态数据集上的平均PSNR、SSIM和LPIPS分别为17.99 dB、0.73和0.272，相较于现有代表性动态3DGS-SLAM方法Dy3DGS-SLAM，PSNR提升0.14 dB，SSIM提升约1.1%，LPIPS降低约4.6%，有效减少了动态目标引起的建图伪影。真实复杂场景序列上的结果进一步表明，该方法在场景分布变化和非结构化动态干扰下仍能保持稳定表现，具有较好的跨场景适用能力。