High-quality 3D reconstruction of weakly textured soft terrain based on 3D Gaussian splatting

3D map creation based on machine vision in a weak texture environment is a difficult problem of robot SLAM. Therefore, this paper tries a new method of low-texture map creation based on 3D Gaussian Splatting (3DGS). Through the acquisition of RGB images of the weak texture environment (lawn) by an Unmanned Aerial Vehicle (UAV) equipped with a monocular camera, the improved Structure from Motion (SfM) and 3DGS processing is used to generate a high-fidelity 3D map of complex and weak texture terrain. Experiments show that the peak signal-to-noise ratio (PSNR) of the training set is more than 40dB. For the new view, the Structural Similarity (SSIM) index in the frequency domain is improved by more than 0.7 in the low and mid-frequency range. While limited in high-frequency detail reconstruction, the method excels in global consistency and medium-scale feature capture, with efficient reconstruction and real-time rendering capabilities. This approach sets the stage for more advanced semantic mapping, promising applications in desert landscapes, barn interiors, and wilderness first aid scenarios.