This paper presents a novel approach to enhance unmanned aerial vehicle (UAV) based simultaneous localization and mapping (SLAM) performance in dynamic environments using 3D Gaussian Splatting (3DGS) technology. By leveraging Gaussian kernel functions to represent 3D points, 3DGS dynamically adapts kernel sizes based on local geometric structures, enabling smooth rendering in sparse regions while preserving critical details in high-curvature areas. The Oriented FAST and Rotated BRIEF (ORB) feature fusion integrates image texture into point clouds by mapping 2D ORB keypoints to 3D space using depth maps, significantly enhancing geometric and texture detail representation, particularly at object edges and corners. Robustness is improved through adaptive resizing and statistical outlier removal, ensuring accuracy in repetitive or low-texture regions. Adaptive point sizing refines precision using depth variations while eigenvalue-based curvature metrics extract high-curvature features to enhance geometric clarity. An optimized Gaussian rendering framework prioritizes critical regions via a score-based mechanism, balancing fidelity and resource efficiency. Extensive experiments validate significant improvements. On the TUM dataset, the proposed method achieves a monocular tracking root mean square error (RMSE) of 0.0287 m (vs. 0.0396 m for MonoGS) and an RGB-D tracking RMSE of 0.0134 m (vs. 0.0158 m for MonoGS), demonstrating superior pose estimation accuracy. On the Replica dataset, it attains an average peak signal-to-noise ratio of 39.38 dB (vs. 37.50 dB for MonoGS) and a trajectory RMSE of 0.469 cm (vs. 0.58 cm for MonoGS), confirming enhanced map quality and geometric consistency under dynamic conditions. Real-world UAV tests using DJI Mini 4 data showcase improved detail preservation, structural optimization, and real-time capability in complex indoor environments. Visual comparisons demonstrate that the proposed method produces sharper textures, more accurate edge reconstruction, and better illumination consistency than baseline methods, with particularly notable improvements in challenging regions such as object boundaries and low-texture areas. Our approach consistently outperforms state-of-the-art SLAM methods in accuracy, robustness, and reconstruction fidelity.