Problem
Research questions and friction points this paper is trying to address.
dynamic environments
SLAM systems
semantic scene understanding
camera tracking
geometry reconstruction
RoSe-SLAM: Robust Semantic-Aware Gaussian Splatting SLAM from Dynamic Monocular Videos
🤖 AI Summary
为解决动态环境下SLAM系统精度下降问题，提出RoSe-SLAM方法，通过从单目视频中提取语义特征并结合时空运动掩模生成模块，实现准确的相机跟踪和高质量几何重建。
📝 Abstract
In dynamic and unstructured environments, conventional SLAM systems generally suffer from significant accuracy degeneration due to their static assumptions. In this work, we propose Robust Semantic-aware Gaussian Splatting SLAM (RoSe-SLAM), to address the dynamic challenge by a holistic semantic scene understanding from uncalibrated monocular inputs, achieving accurate camera tracking and high-quality geometry reconstruction. Unlike conventional semantic SLAM using handcrafted semantic labels, our RoSe-SLAM exploits the semantic feature from 2D foundation model to enhance the dynamic tracking and mapping performance. By distilling the rich semantic features to our Gaussian fields, our method effectively identifies dynamic distractors and achieves semantic-aware multi-view consistency, significantly enhancing the geometric reconstruction and scene inpainting. Specifically, we propose a spatial-temporal motion mask generation module, enabling both long-term motion monitoring and short-term transient dynamics capturing, achieving robust and effective disentanglement of dynamic objects and static backgrounds. During global bundle adjustment, we propose an occlusion-aware keyframe selection mechanism to prioritize the occlusion as metric to pick the keyframes, and a multi-view semantic consistency module to improve the mapping quality in dynamic environments. By combining geometric motion cues with semantic priors, our system dynamically filters unreliable observations and reconstructs accurate static scene geometry. Extensive experiments conducted on benchmark datasets including dynamic TUM, Bonn and Wild-Mocap datasets, demonstrate that our method achieves superior performance in both trajectory estimation and static scene mapping, outperforming existing dynamic RGB SLAM baselines in long-term dynamic indoor environments.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Robust Semantic-aware Gaussian Splatting SLAM
spatial-temporal motion mask generation
occlusion-aware keyframe selection
multi-view semantic consistency
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
No related jobs found.
