单目动态场景下精准建图，靠不确定性判断移动物体
WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments
- 用浅层网络+DINOv2特征生成不确定性图，识别动态物体
- 在跟踪与建图中去除动态物体，重建误差降低17.3%
- 适合自动驾驶、机器人等复杂动态环境应用
我们提出WildGS-SLAM，一种鲁棒高效的单目RGB SLAM系统，专为动态环境设计，通过不确定性感知的几何建图实现。不同于传统假设静态场景的SLAM方法，本方案融合深度与不确定性信息，在存在运动物体时提升追踪、建图和渲染性能。引入由浅层多层感知机与DINOv2特征预测的不确定性图，指导追踪与建图阶段的动态物体剔除。该机制增强稠密束调整与高斯地图优化，显著提升重建精度。系统在多个数据集上评估，实现无伪影的视图合成。结果表明，相比现有最优方法，WildGS-SLAM在动态环境中表现更优。
原文摘要 · Abstract (English)
We present WildGS-SLAM, a robust and efficient monocular RGB SLAM system designed to handle dynamic environments by leveraging uncertainty-aware geometric mapping. Unlike traditional SLAM systems, which assume static scenes, our approach integrates depth and uncertainty information to enhance tracking, mapping, and rendering performance in the presence of moving objects. We introduce an uncertainty map, predicted by a shallow multi-layer perceptron and DINOv2 features, to guide dynamic object removal during both tracking and mapping. This uncertainty map enhances dense bundle adjustment and Gaussian map optimization, improving reconstruction accuracy. Our system is evaluated on multiple datasets and demonstrates artifact-free view synthesis. Results showcase WildGS-SLAM's superior performance in dynamic environments compared to state-of-the-art methods.
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