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arXiv 2411.08373cs.RO

DG-SLAM:基于混合位姿优化的鲁棒动态高斯泼溅SLAM

DG-SLAM: Robust Dynamic Gaussian Splatting SLAM with Hybrid Pose Optimization

Fudan University(复旦大学);Autonomous Driving Division, NIO(蔚来汽车自动驾驶部门)

Yueming Xu, Haochen Jiang, Zhongyang Xiao, Jianfeng Feng, Li Zhang

AI总结:

针对动态场景下视觉SLAM位姿估计不精确的问题,提出基于3D高斯的DG-SLAM系统,通过运动掩码生成、自适应高斯点管理和混合相机跟踪算法,实现了高精度的位姿估计、高保真重建与实时渲染。

摘要:

在动态场景中实现鲁棒且精确的位姿估计是视觉同步定位与建图(SLAM)的一项重大研究挑战。近期将Gaussian Splatting集成到SLAM系统中的进展已被证明能有效利用显式3D高斯模型创建高质量渲染,显著提升了环境重建保真度。然而,这些方法依赖静态环境假设,由于几何和光度观测的不一致,在动态环境中面临挑战。为解决此问题,我们提出了DG-SLAM,这是首个基于3D高斯的鲁棒动态视觉SLAM系统,可提供精确的相机位姿估计和高保真重建。具体而言,我们提出了有效策略,包括运动掩码生成、自适应高斯点管理以及混合相机跟踪算法,以提高位姿估计的准确性和鲁棒性。大量实验表明,DG-SLAM在动态场景的相机位姿估计、地图重建和新视角合成方面达到了最先进的性能,在超越现有方法的同时保持了实时渲染能力。

英文摘要:

Achieving robust and precise pose estimation in dynamic scenes is a significant research challenge in Visual Simultaneous Localization and Mapping (SLAM). Recent advancements integrating Gaussian Splatting into SLAM systems have proven effective in creating high-quality renderings using explicit 3D Gaussian models, significantly improving environmental reconstruction fidelity. However, these approaches depend on a static environment assumption and face challenges in dynamic environments due to inconsistent observations of geometry and photometry. To address this problem, we propose DG-SLAM, the first robust dynamic visual SLAM system grounded in 3D Gaussians, which provides precise camera pose estimation alongside high-fidelity reconstructions. Specifically, we propose effective strategies, including motion mask generation, adaptive Gaussian point management, and a hybrid camera tracking algorithm to improve the accuracy and robustness of pose estimation. Extensive experiments demonstrate that DG-SLAM delivers state-of-the-art performance in camera pose estimation, map reconstruction, and novel-view synthesis in dynamic scenes, outperforming existing methods meanwhile preserving real-time rendering ability.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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