动态场景下实现高精度定位与建图，首次用4D高斯点云解决运动物体干扰问题。
Embracing Dynamics: Dynamics-aware 4D Gaussian Splatting SLAM
- 将时间维度融入高斯点云，构建动态感知的4D场景表示
- 通过动态感知模块过滤不稳定的运动点，显著降低位姿漂移
- 在真实动态数据集上优于当前最佳方法，适合复杂动态环境应用
得益于3D高斯泼溅（3DGS）的实时高保真渲染能力，同步定位与建图（SLAM）技术已实现照片级地图生成。然而，现有基于3DGS的SLAM因场景静态表示，在动态环境中面临位姿漂移和重建失真问题。为此，我们提出首个基于4DGS地图表示的动态环境SLAM方法——D4DGS-SLAM。通过引入时间维度，该方法实现对动态场景的高质量重建。利用动态感知的InfoModule，可获取场景点的动态性、可见性和可靠性，并据此过滤不稳定动态点以优化跟踪。在优化高斯点时，针对不同动态特性的点施加差异化的各向同性正则项。在真实动态场景数据集上的实验表明，本方法在相机位姿追踪与地图质量方面均超越当前最先进水平。
原文摘要 · Abstract (English)
Simultaneous localization and mapping (SLAM) technology has recently achieved photorealistic mapping capabilities thanks to the real-time, high-fidelity rendering enabled by 3D Gaussian Splatting (3DGS). However, due to the static representation of scenes, current 3DGS-based SLAM encounters issues with pose drift and failure to reconstruct accurate maps in dynamic environments. To address this problem, we present D4DGS-SLAM, the first SLAM method based on 4DGS map representation for dynamic environments. By incorporating the temporal dimension into scene representation, D4DGS-SLAM enables high-quality reconstruction of dynamic scenes. Utilizing the dynamics-aware InfoModule, we can obtain the dynamics, visibility, and reliability of scene points, and filter out unstable dynamic points for tracking accordingly. When optimizing Gaussian points, we apply different isotropic regularization terms to Gaussians with varying dynamic characteristics. Experimental results on real-world dynamic scene datasets demonstrate that our method outperforms state-of-the-art approaches in both camera pose tracking and map quality.
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