The field of Simultaneous Localization and Mapping (SLAM) and 3D reconstruction is rapidly advancing with the introduction of new methodologies and techniques. Recent developments have focused on improving the accuracy and robustness of SLAM systems, particularly in dynamic and densely populated environments. The integration of semantic information with 3D Gaussian representations has shown promising results in enhancing both localization and mapping accuracy. Additionally, the use of hybrid representation models and structural supervision has improved scene representation capacity and global consistency. Noteworthy papers in this area include 4D Gaussian Splatting SLAM, which proposes an efficient architecture for incrementally tracking camera poses and constructing Gaussian radiance fields in dynamic scenes, and STAMICS, which integrates semantic information with 3D Gaussian representations for dense RGB-D SLAM. Other notable papers include GI-SLAM, which introduces an IMU-enhanced camera tracking module and a realistic 3D Gaussian-based scene representation, and HS-SLAM, which proposes a hybrid encoding network and structural supervision for improved dense SLAM.