Gassidy：动态环境中的高斯泼溅SLAM
Gassidy: Gaussian Splatting SLAM in Dynamic Environments
AI总结：
Gassidy提出一种基于渲染损失流迭代分析动态物体干扰的RGB-D稠密SLAM方法，在公开数据集上实现相机跟踪精度最高提升97.9%、地图质量最高提升6%。
摘要：
3D高斯泼溅（3DGS）允许灵活调整场景表示，从而在静态环境中的稠密视觉同时定位与建图（SLAM）过程中持续优化场景质量。然而，3DGS在处理来自不规则运动的动态物体所带来的环境干扰时面临挑战，导致相机跟踪精度和地图重建质量均出现退化。为应对这一挑战，我们开发了一种RGB-D稠密SLAM方法，称为动态环境中的高斯泼溅SLAM（Gassidy）。该方法基于设计的光度-几何损失函数，计算高斯以生成每个环境组件的渲染损失流。为了区分并过滤环境干扰，我们迭代分析渲染损失流，以检测动态物体与静态组件之间损失值变化的特征。这一过程确保了用于精确场景重建的干净环境。与最先进的SLAM方法相比，在公开数据集上的实验结果表明，Gassidy将相机跟踪精度提升了最高97.9%，并将地图质量提升了最高6%。
英文摘要：
3D Gaussian Splatting (3DGS) allows flexible adjustments to scene representation, enabling continuous optimization of scene quality during dense visual simultaneous localization and mapping (SLAM) in static environments. However, 3DGS faces challenges in handling environmental disturbances from dynamic objects with irregular movement, leading to degradation in both camera tracking accuracy and map reconstruction quality. To address this challenge, we develop an RGB-D dense SLAM which is called Gaussian Splatting SLAM in Dynamic Environments (Gassidy). This approach calculates Gaussians to generate rendering loss flows for each environmental component based on a designed photometric-geometric loss function. To distinguish and filter environmental disturbances, we iteratively analyze rendering loss flows to detect features characterized by changes in loss values between dynamic objects and static components. This process ensures a clean environment for accurate scene reconstruction. Compared to state-of-the-art SLAM methods, experimental results on open datasets show that Gassidy improves camera tracking precision by up to 97.9% and enhances map quality by up to 6%.
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。
