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%.
3D 高斯投影（3D Gaussian Splatting, 3DGS）能够灵活调整场景表示，使其在静态环境下进行稠密视觉同时定位与建图（SLAM）时，可持续优化场景质量。然而，3DGS 在处理动态物体的不规则运动引起的环境干扰时面临挑战，这会导致摄像机跟踪精度和地图重建质量的下降。为应对这一问题，我们开发了一种基于 RGB-D 的稠密 SLAM 方法，称为动态环境中的高斯投影 SLAM（Gassidy）。 该方法通过设计的光度-几何损失函数，为每个环境组件计算高斯分布以生成渲染损失流。为了区分并过滤环境干扰，我们迭代分析渲染损失流，以检测由动态物体和静态组件之间损失值变化所表征的特征。这一过程确保了一个干净的环境，从而实现准确的场景重建。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 将摄像机跟踪精度提高了最多 97.9%，并将地图质量提升了最多 6%。