The field of Simultaneous Localization and Mapping (SLAM) and 3D reconstruction is rapidly advancing with the integration of Gaussian Splatting (GS) techniques. Recent research has focused on improving the efficiency and accuracy of GS-based SLAM systems, particularly in dynamic environments. Innovations in algorithm-hardware co-design, adaptive feature extraction, and unified optimization frameworks have led to significant breakthroughs in reconstruction quality and pose estimation accuracy. Furthermore, the incorporation of multi-spectral imaging and opacity radiance fields has enhanced geometric mapping performance and enabled more robust tracking. Noteworthy papers include: AGS, which achieves up to 17.12x speedups against state-of-the-art 3DGS accelerators. DyPho-SLAM, which presents a real-time, resource-efficient visual SLAM system designed to address the challenges of localization and photorealistic mapping in environments with dynamic objects.