Simultaneous localization and mapping (SLAM) has achieved impressive performance in static environments. However, SLAM in dynamic environments remains an open question. Many methods directly filter out dynamic objects, resulting in incomplete scene reconstruction and limited accuracy of camera localization. The other works express dynamic objects by point clouds, sparse joints, or coarse meshes, which fails to provide a photorealistic representation. To overcome the aforementioned limitations, we propose a photorealistic and geometry-aware red-green-blue-depth (RGB-D) SLAM method based on Gaussian splatting. Our method is composed of three main modules to map the dynamic foreground including nonrigid humans/quadrupeds and rigid items, reconstruct the static background, and localize the camera. To map the foreground, we focus on modeling the deformations and/or motions. We consider the shape priors of humans/quadrupeds and exploit the geometric and appearance constraints of dynamic Gaussians. For background mapping, we design an optimization strategy between neighboring local maps by integrating appearance constraint into geometric alignment. As to camera localization, we leverage both static background and dynamic foreground to increase the number of observations and introduce more constraints. We explore the geometric and appearance constraints by associating 3-D Gaussians with 2-D optical flows and pixel patches. Experiments on extensive real-world datasets demonstrate that our method outperforms state-of-the-art approaches in terms of camera localization and scene mapping.