Point cloud registration algorithms are susceptible to initial pose and noise, resulting in low accuracy and increased processing time. To address these issues, a point cloud registration algorithm based on texture and geometric features was proposed. First, geometric features such as curvature and density, along with texture features containing color information, were extracted from the point cloud to select a set of feature points. Then, the feature point set was described using FPFH-3DLGOP descriptors, and key points were matched through the random sample consensus (RANSAC) algorithm to achieve coarse registration, thereby obtaining a favorable initial pose. Finally, fine registration was performed using the iterative closest point (ICP) algorithm based on a KD-tree. Experiments were conducted using the Stanford dataset and publicly available Sketchfab data to validate the algorithm. The experimental results show that the proposed algorithm improves time efficiency and accuracy by approximately 25% and 30% respectively, compared to existing algorithms under conditions of poor initial pose and noise. It is concluded that, under the same conditions, the proposed algorithm offers higher time efficiency and accuracy compared to conventional algorithms.